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Analytical method for quantification of economic risks during feasibility analysis for large engineering… Ranasinghe, Kulatilaka Arthanayake Malik Kumar 1990

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ANALYTICAL METHOD FOR QUANTIFICATION OF ECONOMIC RISKS DURING FEASIBILITY ANALYSIS FOR LARGE ENGINEERING PROJECTS By KULATILAKA ARTHANAYAKE MALIK KUMAR RANASINGHE B. Sc. (Engineering) Honors, University of Moratuwa, Sri Lanka. M. A. Sc., The University of British Columbia, Canada. A THESIS S U B M I T T E D IN PARTIAL F U L F I L L M E N T O F THE REQUIREMENTS FOR T H E DEGREE OF DOCTOR OF PHILOSOPHY  in T H E F A C U L T Y O F G R A D U A T E STUDIES D E P A R T M E N T O F CIVIL ENGINEERING  We accept this thesis as conforming to the required standard  T H E UNIVERSITY O F BRITISH C O L U M B I A  August 1990 © KULATILAKA ARTHANAYAKE MALIK KUMAR RANASINGHE, 1990  In presenting this thesis in partial fulfilment of the degree  requirements for an advanced  at the University of British Columbia, I agree that the Library shall make it  freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department  or by his  or  her  representatives.  It  is  understood  that  copying  or  publication of this thesis for financial gain shall not be allowed without my written permission.  Department of  <^*v\\_  <sto<s;\€J&V?\Vvjgg|  The University of British Columbia Vancouver, Canada  Date  DE-6 (2/88)  hUuSxuu&x  W^O  Abstract  The objectives of this thesis are to develop an analytical method for economic risk quantification during feasibility analysis for large engineering projects and to computerize the method to explore its behavior, to validate it and to test its practicality for the measurement of uncertainty of decision variables such as project duration, cost, revenue, net present value and internal rate of return. Based on the probability of project success the method can be utilized to assist on strategic feasibility analysis issues such as contingency provision, "go-no go" decisions and adopting phased or fast track construction. The method is developed by applying a risk measurement framework to the project economic structure. The risk measurement framework is developed for any function Y  =  <7(X),  between a derived variable and its correlated primary variables. Using a  variable transformation, it transforms the correlated primary variables and the function to the uncorrelated space. Then utilizing the truncated Taylor series expansion of the transformed function and the first four moments of the transformed uncorrelated variables it approximates the first four moments of the derived variable. Using these first four moments and the Pearson family of distributions the uncertainty of the derived variable is quantified as a cumulative distribution function. The first four moments for the primary variables are evaluated from the Pearson family of distributions using accurate, calibrated and coherent subjective percentile estimates elicited from experts. The correlations between the primary variables are elicited as positive definite correlation matrices. The project economic structure describes an engineering project in three hierarchical levels, namely, work package/revenue stream, project ii  \  performance and project decision. Each of these levels can be described by Y = £f(X), with the derived variables of the lower levels as the primary variables for the upper level. Therefore, the input as expert judgements is only at the work package/revenue stream level. Project duration is estimated by combining the generalized PNET algorithm to the project economic structure. This permits the evaluation of the multiple paths in the project network. Also, the limiting values of the PNET transitional correlation (0,1) permits the estimation of bounds on all of the derived variables. Project cost and revenue are evaluated in terms of current, total and discounted dollars, thereby emphasizing the economic effects of time, inflation and interest on net present value and internal rate of return. The internal rate of return is evaluated from a variation of Hillier's method. The analytical method is validated using Monte Carlo simulation. The validations show that the analytical method is a comprehensive and extremely economical alternative to Monte Carlo simulation for economic risk quantification of large engineering projects. In addition, they highlight the ability of the analytical method to go beyond the capabilities of simulation in the treatment of correlation, which are seen to be significant in the application problems. From these applications a technique to provide contingencies based on the probability of project success and to distribute the contingency to individual work packages is developed.  ni  Table of C o n t e n t s  Abstract  ii  List of Tables  xii  List of Figures  xvi  Acknowledgement  xvii  1 Introduction  1  1.1  General  1  1.2  Background for the Research  2  1.3  Problem Statement and Structure  8  1.3.1  Work Package/Revenue Stream Level  10  1.3.2  Project Performance Level  11  1.3.3  Project Decision Level  12  1.3.4  Observation  12  1.4  Objectives of the Research  13  1.5  Previous Research and Motivation  14  1.6  Structure of the Thesis  17  2 Risk Measurement Framework  20  2.1  General  20  2.2  The Pearson Family of Distributions  21  2.3  The Moments of a Primary Variable  24  iv  2.4  Moments of the Derived Variable  31  2.4.1  Truncated Taylor Series  32  2.4.2  Variable Transformation Method  34  2.4.3  Moments of the Uncorrelated Variables  36  2.4.4  The Function  37  2.4.5  The First Four Moments  37  2.5  Cumulative Distribution Function  39  2.6  Application of the Framework  40  2.6.1  Example 1 : Activity Duration  40  2.6.2  Example 2 : Stochastic Breakeven Analysis  42  2.6.3  Example 3 : Linear Function  45  2.7  Summary  48  3 Elicitation of Subjective Probabilities  50  3.1  General  50  3.2  Subjective Probabilities  51  3.3  Definitions and Assumptions  53  3.4  Pre-Elicitation Stage  57  3.4.1  Motivating Phase  57  3.4.2  Structuring Phase  58  3.4.3  Conditioning Phase  59  3.5  Elicitation Stage  61  3.6  Feedback and Consensus Estimates  67  3.7  Analysis Stage  69  3.8  Verification  70  3.9  Summary  71  v  4 Correlations Between Variables  73  4.1  General  73  4.2  Correlation between Primary Variables  74  4.2.1  Positive Definite Correlation Matrix  75  4.2.2  Elicitation of a Correlation Matrix  76  4.3  Correlation between Derived Variables  79  4.4  Multicollinearity  85  4.5  Numerical Study  87  4.5.1  Variable Transformation Method  87  4.5.2  The Standard Approach  89  4.5.3  The Comparison  91  4.5.4  Transformation under Multicollinearity  93  4.6  Summary  96  5 Decomposition of a Derived Variable  102  5.1  General  5.2  Decomposition  103  5.3  Hypotheses  105  5.4  Test Statistics  108  5.5  Experiment  112  5.5.1  The Activity  112  5.5.2  Procedure  112  5.6  5.7  •  102  Analysis  113  5.6.1  Moments from Decomposition  113  5.6.2  Experimental Results  116  5.6.3  Hypotheses Testing  119  Summary  121 vi  6  T h e Analytical M e t h o d  122  6.1  General  122  6.2  Work Package/Revenue Stream Level  124  6.2.1  Work Package Duration  124  6.2.2  Work Package Start Time  126  6.2.3  Work Package Cost  132  6.2.4  Net Revenue Stream  133  6.3  6.4  6.5  6.6 7  Project Performance Level  136  6.3.1  Project Duration  138  6.3.2  Project Cost  138  6.3.3  Project Revenue  140  Project Decision Level  140  6.4.1  Project Net Present Value  140  6.4.2  Project Internal Rate of Return  141  Discussion  143  6.5.1  Computational Accuracy  145  6.5.2  Standard Approach  146  Summary  149  Validations and Applications  151  7.1  General  151  7.2  Monte Carlo Simulation  153  7.2.1  Treatment of Correlations  154  7.2.2  The Number of Iterations  156  7.3  Modified PNET Algorithm  158  7.3.1  Road Pavement Project  159  7.3.2  Industrial Building Project  161  vii  7.4  Parallel Network  163  7.5  First Example  168  7.5.1  Second Limiting Case  168  7.5.2  First Validation  171  7.5.3  Second Validation  177  7.5.4  Discussion  188  7.6  7.7  7.8  Second Example  191  7.6.1  Third Validation  194  7.6.2  Fourth Validation  197  7.6.3  Correlations at All Levels of the Project  207  7.6.4  Discussion  218  Sensitivity Analysis and Contingency  218  7.7.1  Sensitivity Analysis  219  7.7.2  Distribution of Contingency  221  Summary  227  8 Conclusions and Recommendations  231  8.1  Conclusions  231  8.2  Recommendations for Future Work  234  8.2.1  Analytical Method  234  8.2.2  Computer Programs  236  8.2.3  Risk Management Process  237  Bibliography  238  Appendices  252  A The First Four Moments  252 viii  A.l General  252  A.2 Expected Value  252  A.3 Second Central Moment  253  A.4 Third Central Moment  255  A.5 Fourth Central Moment  257  A.6 Note : Higher Order Moments  259  B Investigation of Ro  260  C Bounds for a Correlation Coefficient  262  C.l The Proof  262  C. 2 The Bounds  264  D The Computer Programs D. l  266  General  266  D.2 ELICIT - Program to Obtain Input Data  266  D.3 TIERA - Program for Risk Quantification  270  E The Correction Factor a  278  F Input Data for Numerical Examples  280  ix  L i s t of Tables  2.1  Subjective Percentile Estimates for A, P and L  41  2.2  Statistics for the R a n d o m Variables  41  2.3  First Four Moments and Partial Derivatives of Transformed Variables  42  2.4  Comparison of Moments and Shape Characteristics  43  2.5  Comparison of Estimation Approaches  45  2.6  Comparison of Moments and Shape Characteristics  46  2.7  Comparison of the First Partial Derivatives of Y  46  2.8  Comparison of Moments and Shape Characteristics  47  4.1  Quantity Descriptors (Q) (ft )  91  4.2  Labour Productivity Rates, P ; (ft /m.d) L  92  4.3  Labour Usage, L ; (m.d/year)  92  4.4  Condition Number (<fi) and Correlation Coefficients  4.5  First Four Moments of the Work Package Durations  94  4.6  Moments of the Duration with an Unstable Correlation M a t r i x . . . .  95  5.1  A c t u a l and-Estimated Statistics for the Activity Duration (minutes) .  117  5.2  Test Statistics for Expected Values and Standard Deviations  118  5.3  Significance Tests for Hypotheses (5.2) to (5.9) at 95% confidence level 120  6.1  Statistics for Work Package Costs  6.2  First Four. Moments and Shape Characteristics for Project Cost  7.1  Ordered Paths and Duration Statistics - Table 2, A n g et a l , (1975)  3  3  x  93  147 . . . .  148 159  7.2  Ordered Paths and Duration Statistics from Modified PNET  161  7.3  Ordered Paths and Duration Statistics for the Industrial Building . . 164  7.4  Statistics for Project Duration for First Limiting Case  166  7.5  Statistics for Project Duration for Second Limiting Case  171  7.6  Statistics for Project Duration from First Validation - Ex #1  172  7.7  Statistics for Discounted Project Cost from First Validation - Ex #1  174  7.8  Statistics for Discounted Project Revenue from First Validation-Ex #1 174  7.9  Statistics for Project NPV from First Validation - Ex #1  175  7.10 Statistics for Project IRR from First Validation - Ex #1  176  7.11 Comparison of CPU times from First Validation - Ex #1  176  7.12 Statistics for Project Duration from Second Validation - Ex #1  . . . 181  7.13 Statistics for Discounted Project Cost from Second Validation - Ex #1 182 7.14 Statistics for Discounted Project Revenue from Second Validation-Ex #1  182  7.15 Statistics for Project NPV from Second Validation - Ex #1  183  7.16 Statistics for Project IRR from Second Validation - Ex #1  183  7.17 Comparison of CPU times from Second Validation - Ex #1  184  7.18 Deterministic and Probabilistic Analyses of Project Cost  189  7.19 Statistics for Project Duration from Third Validation - Ex #2  . . . .  194  7.20 Statistics for Discounted Project Cost from Third Validation - Ex #2  195  7.21 Statistics for Discounted Project Revenue from Third Validation - Ex #2  195  7.22 Statistics for Project NPV from Third Validation - Ex #2  196  7.23 Statistics for Project IRR from Third Validation - Ex #2  196  7.24 Comparison of CPU times from Third Validation - Ex #2  198  7.25 Statistics for Project Duration from Fourth Validation - Ex #2 . . . .  202  xi  7.26  Statistics for Project Variables  203  7.27  Statistics for Discounted Project Cost from Fourth Validation-Ex #2  204  7.28  Statistics for Discounted Project Revenue from Fourth Validation-Ex #2  205  7.29  Statistics for Project N P V from Fourth Validation - E x #2  205  7.30  Statistics for Project I R R from Fourth Validation - E x #2  206  7.31  Comparison of C P U times from Fourth Validation - E x #2  206  7.32  Statistics for Project Variables  214  7.33  Comparison of the Statistics for Project Duration  215  7.34  Comparison of the Statistics for Current Dollar Project Cost  215  7.35 Xp, 7.36  C and ^ , ^ for Different Probabilities of Success E  c  Statistics for Current Dollar Work Package Cost - E x #2  222 224  7.37 Distributed Contingency and Probability of Success  225  F.l  Activities and Estimated Durations (Pavement Project) . . . . . . . . .  281  F.2  Activities and Estimated Durations (Industrial Building Project) . . .  282  F.3  Deterministic Values for Work Package Durations and Costs  284  F.4  Statistics for Work Package Durations and Costs  285  F.5  Statistics for Revised Work Package Durations  286  F.6  Statistics for Annual Revenue and Operating Costs  287  F.7  Statistics for Quantity Descriptor Qi (ft )  288  F.8  Statistics for Labour Productivity Rate  F.9  Statistics for Labour Usage L ;  3  F.10 Statistics for Equipment Usage  P  (ft /m.d) 3  Li  (m.d/year) E{ (e.d/year)  288 289 290  F . l l Statistics for Subcontractor Cost Si ($)  290  F.12 Statistics for Common Primary Variables  291  F.13 Statistics for Annual Revenue and Operating Costs  292  xii  List of Figures  j  1.1  Overall Assessment of Project Results  4  1.2  Average Economic Rates of Return for Evaluated Projects  4  1.3  Project Completion Time Overruns/Underruns  6  1.4  Average Project Cost Overruns  6  1.5  Precedence Network for an Engineering Project  9  2.1  Moment Ratio Plane Showing Pearson Types I-XII  23  2.2  The Steps of the Iterative Process  26  2.3  The "Best Fit" Distribution  30  2.4  Approximated Pearson Type Distributions for P(x)  44  3.1  Calibration Curve  55  3.2  Subjective Percentile Estimates  66  4.1  Feasible Regions for T for R  80  4.2  Correlation from Common (Shared) Primary Variables  82  4.3  Expected Values  98  4.4  Second Central Moment  99  4.5  Third Central Moment  100  4.6  Fourth Central Moment  101  5.1  t Distribution for Two Tailed Test  110  5.2  t Distribution for Upper Tailed Test  110  5.3  x  Ill  n  to be Positive Definite  i  2  Distribution for Two Tailed Test  xiii  5.4  x Distribution for Upper Tailed Test  Ill  6.1  Generalized Cash Flow Diagram for an Engineering Project  123  6.2  Cash Flow Diagram for the Analytical Method  123  6.3  Flowchart for the Analytical Method  125  6.4  Generalized Discounted Work Package Cost  134  6.5  Upper and Lower Bounds for Project Duration  139  6.6  Upper and Lower Bounds for Project Cost  139  6.7  Bounds for the Project Net Present Value  144  6.8  Bounds for the Project Internal Rate of Return  144  7.1  Random Variate Generation  157  7.2  The Correction Factor a for Different Values of p  157  7.3  The Precedence Network for the Road Pavement Project  160  7.4  The Precedence Network for the Industrial Building Project  162  7.5  The Parallel Network  165  7.6  CDFs for Project Duration for the Parallel Network  167  7.7  The Project Network for the First Example  169  7.8  CDFs for Project Duration for the Single Dominant Path  170  7.9  CDFs for Project Duration - First Validation - Ex #1  178  7.10 CDFs for Project Duration - First Validation - Ex #1  178  7.11 CDFs for Discounted Project Cost - First Validation - Ex #1  179  2  7.12 CDFs for Discounted Project Revenue - First Validation - Ex #1  . . 179  7.13 CDFs for Project Net Present Value - First Validation - Ex #1 . . . . 180 7.14 CDFs for Project Internal Rate of Return - First Validation - Ex #1  180  7.15 CDFs for Project Duration - Second Validation - Ex #1  185  7.16 CDFs for Project Duration - Second Validation - Ex #1  185  XIV  7.17 CDFs for Discounted Project Cost - Second Validation - Ex #1  ...  186  7.18 CDFs for Discounted Project Revenue - Second Validation - Ex #1 7.19 CDFs for Project Net Present Value - Second Validation - Ex #1  186 . . 187  7.20 CDFs for Project Internal Rate of Return - Second Validation - Ex #1 187 7.21 CDFs for Current Dollar Project Cost - Second Validation - Ex #1  . 190  7.22 CDFs for Total Dollar Project Cost - Second Validation - Ex #1 . . . 190 7.23 The Project Network for the Second Example  192  7.24 CDFs for Project Duration - Third Validation - Ex #2  199  7.25 CDFs for Project Duration - Third Validation - Ex #2  199  7.26 CDFs for Discounted Project Cost - Third Validation - Ex #2 . . . . 200 7.27 CDFs for Discounted Project Revenue - Third Validation - Ex #2 . . 200 7.28 CDFs for Project Net Present Value - Third Validation - Ex #2 . . .  201  7.29 CDFs for Project Internal Rate of Return - Third Validation - Ex #2  201  7.30 CDFs for Project Duration - Fourth Validation - Ex #2  208  7.31 CDFs for Project Duration - Fourth Validation - Ex #2  208  7.32 CDFs for Discounted Project Cost - Fourth Validation - Ex #2  ...  7.33 CDFs for Discounted Project Revenue - Fourth Validation - Ex #2 7.34 CDFs for Project Net Present Value - Fourth Validation - Ex #2  209  . 209 . . 210  7.35 CDFs for Project Internal Rate of Return - Fourth Validation - Ex #2 210 7.36 Correlation Matrix for the Complete System  211  7.37 CDFs for Project Duration  216  7.38 CDFs for Total Dollar Project Cost  216  7.39 CDFs for Project Net Present Value  217  7.40 CDFs for Project Internal Rate of Return  217  7.41 CDF for Current Dollar Project Cost  226  7.42 CDF for Current DoUar Cost for Work Package #4  226  xv  D.l Flowchart for ELICIT  267  D.2 Flowchart to Ensure Coherence of Subjective Estimates  269  D.3 Flowchart of the Modified PNET Algorithm  271  D.4 Flowchart to Trace all the Paths to a Work Package  272  D.5 Typical Output from TIERA  274  i i  i xvi  Acknowledgement  I wish to express my most sincere gratitude to Dr. Alan Russell, my teacher and supervisor, who has had a profound and positive impact on my academic and professional attitudes. I greatly appreciate his advice, guidance and support throughout my graduate studies. This thesis would not exist without his patient efforts and valuable suggestions. My special thanks to Dr. Bill Caselton and Dr. Ricardo Foschi for the numerous stimulating discussions, especially on the areas of correlations and simulations. Their efforts in reviewing this thesis are greatly appreciated. I thank Dr Frank Navin and Dr. Karl Bury for serving on my supervisory committee. Acknowledgement is most gratefully extended to the Canadian Commonwealth Scholarship and Fellowship Plan who provided the scholarship which enabled me to pursue graduate studies in Canada. My special thanks to Miss. Deirdre Roeser of the above plan for making my stay in Canada an enjoyable experience. To Ashley Herath, Arif Rahemtulla, Gerard Canisius, Damika Wickremesinghe, Chanaka Edirisinghe, Ron Yaworsky, Ibrahim Al-Hammad, fellow colleagues and friends, many thanks for your moral support and encouragement.  You made the  bad times more tolerable and the good times more enjoyable. Finally, to Deepthi, your patience, support and encouragement is most gratefully acknowledged.  xvu  To my parents and to my grandmother  for their support and encouragement  in all my endeavors.  xvm !  Chapter 1 Introduction "Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise." John W. Tukey, Annals of Mathematical Statistics, vol. 33, 1962, p.13.  1.1  General  This thesis describes the development, validation and application of an analytical method for time and economic risk quantification during feasibility analysis for large engineering projects. The method has the ability to quantify the uncertainty in and estimate the bounds on decision variables of a project implemented in traditional, fast track or phased construction. The pragmatic convention of treatingriskand uncertainty as synonyms is adopted in this thesis. The precision in the computations presented is to facilitate comparisons with otherriskquantification techniques. This precision however, belies the accuracy of estimations which can be achieved for real life projects. This chapter describes the background for the research problem, the economic structure adopted to represent an engineering project, the research objectives, a brief state-of-the-art and an overview of the thesis.  1  Chapter 1. Introduction  1.2  2  Background for the Research  Large, complex engineering projects will continue to be undertaken both in the developed and developing worlds to meet the increase in demand for infrastructure, energy, raw materials and employment. Typically these projects have long durations, high costs, multiple investors and are undertaken in uncertain environments. The generation of benefits at the earliest possible date to pay back or justify the large investments required for such projects has necessitated the adoption of concepts such as fast track and phased construction. The very nature of these concepts coupled with the increasing size and complexity of such projects necessitates explicit treatment of risk and uncertainty, especially in the early stages. The World Bank reports that about 20% of the projects evaluated between 1974 and 1986 were determined to be unsatisfactory (seefigure1.1). The "satisfactory" projects between 1974 - 1984 were based on the achievement of at least a 10% economic rate of return, or other significant benefits if the economic rate of return was lower, or an evaluator's qualitative judgement about the performance if no economic rate of return was calculated. The classifications for 1985 and 1986 were based on achievement of one of the three states: 1. wholly satisfactory : project achieves or exceeds all its major objectives, achieves substantial results in almost all respects; 2. satisfactory : project achieves most of its objectives and has satisfactory results with no major shortcomings; 3. marginally satisfactory : project reveals major shortcomings in meeting objectives and/or achievements but is still considered worthwhile. (Project Performance Results for 1986 (1988)). Figure (1.2) depicts the average economic rates of return at appraisal and average re-estimated economic rates of return calculated shortly after final disbursement of Bank funds. Both rates are based on future flows of economic benefits. Thefirstis calculated from project costs and economic events predicted in the appraisal phase, while the second is based on the actual  Chapter 1. Introduction  3  project cost, relative price changes and current economic events. The figures clearly display the risks and uncertainty associated with the predictions that are made during feasibility analysis. The critical reasons for project failure, besides an adverse economic environment, were deficiencies in project design. These include the lack of: clarity and acceptance of objectives in terms of technical, economic and administrative criteria; and/or the thoroughness with which the project design is prepared and appraised. Over one third of the projects reviewed by the World Bank in 1985 were judged to have been adversely affected by deficiencies in preparation or appraisal (The Twelfth Annual Review of Project Performance Results, 1987). A profile of the project completion time overruns/underruns for 1513 projects reviewed by World Bank between 1974 to 1986 is shown in figure (1.3). Time overrun/underrun refers to the difference between actual and appraised project execution time. The execution time is from the signing date of the loan/credit to actual completion date. The average project execution time for those reviewed in 1986 was 6.1 years. The principal reasons for completion delays were inadequate project preparation, changes in project scope, administrative constraints within the country and the unfamiliarity of the borrower with Bank procurement procedures, delays in the appointment of staff or consultants, and lack of financial support for the project by the borrower (The Twelfth Annual Review of Project Performance Results, 1987). The average cost overruns for 1269 projects reviewed by the World Bank between 1974 to 1986 are depicted in figure (1.4). The average cost overrun is the unweighted mean of the percent cost overrun for individual projects. The World Bank states that while Bank forecasting methods deserve continual scrutiny to enhance their effectiveness in identifying development opportunities, the  Chapter 1. Introduction  4  100  8  Wholly Satisfactory  90 -  '2 80 Q_ "O  70 -  Satisfactory  Satisfactory  o 60  O. E o 50 O o 40 -  Marginally Satisfactory  a>  co 30 c <D 20  2  a> a. 10 h  Unsatisfactory J  I  i  i  i  I  i  Unsatisfactory  L  74 75 76 77 78 79 80 81 82 83 84 85 Evaluation Year Figure 1.1: Overall Assessment of Project Results Source : Appendix Tables 8 and 9. - Project Performance Results for 1986 (1988), Appendix Table 1.14 - 12th Annual Review of Project Performance Results, (1987). 30 Appraised ERR 25  Re-estimated ERR  15 3C3  10  5 -  74  75  76  77  78  79 80 81 Evaluation Year  82  83  84  85  86  Figure 1.2: Average Economic Rates of Return for Evaluated Projects Source : Appendix Table 10 - Project Performance Results for 1986 (1988).  Chapter 1. Introduction  5  resulting investments will continue to face considerable risk and uncertainty. The array of difficulties now confronting borrowers, such as foreign debt, domestic inflation, exchange rates and the continued volatility of external factors, implies that risk will remain an important issue, calling for broader risk analysis and more deliberate efforts at risk management. It is suggested that the way to addressrisksdirectly at the feasibility stage is to present the probability of project success (Project Performance Results for 1986 (1988)). After an extensive study on risk management in engineering construction, Hayes et al. (1986) concluded that: all too often,risksare either ignored, or dealt with in a completely arbitrary way (simply adding 10% contingency onto the estimated cost of a project is typical); and the greatest uncertainty is present in the earliest stages in the life of a project, which is also when decisions of greatest impact are made. Risks must be treated at this phase; and since all parties involved in construction projects and contracts would benefit from reduction in uncertainty prior to financial commitment, more effort should be devoted to risk management. While risk and uncertainty are distinguished in the context of decision analysis (Siddall, 1972), Perry and Hayes (1985b) state that risk and uncertainty are inherently present in all construction projects and in the practice of construction risk management such distinctions are unnecessary and may even be unhelpful. The objective of the feasibility analysis is to develop and evaluate alternatives so that the most desirable ones are selected and implemented. Generally speaking, the selected alternatives should be, in the decision maker's view, the best in terms of technical, economic and socio-political feasibility. However, in practice technical feasibility is considered as the dominant criterion (Jaafari, 1988a, 1988b). Youker (1989) states that the economic analysis should be treated as the decision criterion and done before, rather than after detailed engineering design.  Chapter 1. Introduction 100  3ZZ3  90  Delay > 200% 100%<Delay<;200%  .1  80  £  70  50%<Delay<100%  60  ej 50 O)  | v  40  20%< Delay <50%  2 30  Q Q_  0%<Delay<20%  20  Delay < 0%  10  1974-79  0  1980-84 1985 Evaluation Year  1986  Figure 1.3: Project Completion Time Overruns/Underruns Source : Appendix Table 18 - Project Performance Results for 1986 (1988). 45 40 35  g  30 -  & 25 h B  § 0 2  I  15 10 h 5 74  75  76  77  78  79 80 81 82 Evaluation Year  83  84  85  86  Figure 1.4: Average Project Cost Overruns Source : Appendix Table 17 - Project Performance Results for 1986 (1988).  Chapter 1. Introduction  7  A project is economically feasible if the net present value of the benefits generated from it exceeds the net present value of its cost at the minimum attractive rate of return (marr). This thesis treats net present value of a project at marr and its internal (economic) rate of return as the two basic measures that guide the decisions on the economic feasibility of an engineering project (Au, 1988; Bonini, 1975; Cooper and Chapman, 1987; Thompson and Wilmer, 1985). Other measures such as: ratio of net present value over total initial capital investment (Jaafari, 1988b) to complement total life cycle cost (Jaafari, 1988a, 1988c) and risk adjusted discount rate (Farid et al., 1989) have been suggested for construction projects. Taylor (1988) argues that the most reliable approach for appraising projects is using the criterion of net present value alone, and not net present value divided by the initial cost. The greatest degree of uncertainty about the future estimates is encountered at the feasibility stage. Consequently, decisions taken during this stage of a project can have a large impact on its final cost and its duration. However, it is in this stage that decision makers have the greatest leeway to make changes in the scope of the project, restructure a marginally unfeasible project into a feasible one, or even to cancel the project with minimum loss (Youker, 1989). The limited information available at this stage increases the uncertainty of such decisions. The ability to identify, measure and respond to potentialrisksand uncertainties will significantly improve the quality of decisions made during feasibility analysis. This process of risk identification, risk quantification and risk response is considered as the most suitable approach for risk management in engineering projects (Flanagan et al., 1987; Perry and Hayes, 1985b). More comprehensive discussions onriskmanagement in engineering projects are found in Ashley (1980a, 1980b); Ashley and Bonner (1987), Chicken and Hayns (1989); Cooper and Chapman (1987); Hayes et al. (1986); Jaafari (1986, 1987, 1988b); Perry (1986); Perry and Hayes (1985a, 1985b); Thompson (1981); Youker (1989).  Chapter 1. Introduction  1.3  8  Problem Statement and Structure  Economic risk quantification is a vital step for risk management in large engineering projects because it develops the basis for the decision maker to respond to identified risks. While economic risk quantification techniques for engineering projects are available, in their current form many lack the ability to model large engineering projects realistically for a comprehensive feasibility analysis. Some of the considerations for realistic modeling are: limitation of data and the need for judgements; interaction of time with cost and revenue; correlation among variables; existence of multiple paths to complete a project; the number of variables that can be used in the analysis; and most importantly the need to evaluate a range of alternatives economically to select the best strategy to develop a project. These issues are dealt with explicitly in this thesis in the formulation of the analytical method for risk quantification. Central to this method is the description of the project economic structure as a hierarchy containing all of the derived time and economic variables of an engineering project. The one presented is an extension of the structure developed by Ranasinghe (1987) to represent an engineering project. In this thesis, three levels of description, namely, project decision, project performance and work package/revenue stream, describe the project economic structure. Work packages and revenue streams of a project are linked together by way of a precedence network (see figure 1.5).  The work package/revenue stream level is  considered as the lowest level at which meaningful information can be obtained during feasibility analysis. However, if necessary, a work package can be further decomposed to a sub-network of activities, with each activity having a duration and cost function. Definitions relevant to this structure are as follows.  W.P#4  <§'  W.P#1 Revenue Stream #2  at  W.P#5  o n Q.  et>  P  aa> S!  Start W.P  W.P#2  o  W.P#6  o •-»  6  w P  92. p  —  1  Revenue Stream #3  W.P#3  l-J  epra  13 •i <o >. o  W.P#7  Revenue Stream #1  3 e o  Chapter 1. Introduction  1.3.1  10  Work Package/Revenue Stream Level  The variables at the work package/revenue stream level are, Work Package Duration : Work package duration can be estimated directly by the analyst (holistic value) or derived using a functional relationship which treats work scope, anticipated job conditions, likely construction methods, productivity and resource levels or a sub-network of activities. Work Package Cost : A generalized expression for work package cost which can be used to estimate constant, current, total dollar cost and discounted value is as follows: WPd  =  f e^i'^ci  f ' Tc  C i(r)  ( i-^ 8c  0  e  dr  T  (1.1)  Jo  +(1 -  f)e^-y^e i a ec  Ts  f  Coif^e^i-^dr  Tci  Jo  where WPCi is the discounted i  ih  stant dollar cash flow for the i  th  work package cost, work package, Ts  ci  Coi(r)  is the function for con-  and Ta are work package start  time and duration, T is the time at which the repayment of interim financing is p  due for all work packages, / is the equity fraction, 6c , r and y are inflation, interest {  and discount rates respectively.  The time r is measured from the start of the i  th  work package. C^T) can be either holistic or a decomposed function of work scope, resources applied, and productivity. The work package cost is expressed in discounted dollars for generality. When required, the work package cost can be expressed: in total dollars (constant -f inflation + financing) by setting the discount rate to zero (WPCTDI)]  in current dollars by  setting the discount rate to zero and equity fraction to one (WPCcDi)]  o r m  constant  dollars by setting discount and inflation rates to zero and equity fraction to one {WPCcoi).  11  Chapter 1. Introduction  Net Revenue Stream : The present value of a net revenue stream can be expressed as follows: NRSi  =  f  TSm+Tm  SR,  where  NRSi  \R (t)e ^- ^ 6R  - Moiity^'}  Ts  oi  JT  1  is the discounted  i  ih  (1.2)  e~ dt yt  J  net revenue stream,  and Moi(t) are the func-  Roi(t)  tions for constant dollar cash flow for i gross revenue and operation and maintenance th  cost, and  Ts  y  m  and TJH are early start time and duration of the revenue stream,  are inflation and discount rates respectively.  Roi(t)  and M^t)  Giti^Mi  can be either  holistic or decomposed functional forms.  1.3.2  Project Performance Level  The variables at the project performance level are as follows. Project Duration  ^ = £ WPDa  (1.3)  t=i  where  Tj  is the duration of the j  package on the j  th  t h  path and WPDij  is the duration of the  i  th  work  path. For the deterministic case, project duration is given by, T  =  max\/j  (Tj)  j = 1,  ,n  (1.4)  When time is uncertain, the probability of completing the project in time t, denoted by p(t), (Ang et al., 1975), is given by, p(t)  =  1 +  where  Ti,T , 2  ,T  n  [P(T->t) + + P(2i < t,T  P(T <t,T >t) l  2  < t,  2  , r _ < t,T n  x  n  > t)]  (1.5)  are durations of the possible paths to complete the project.  Times and probabilities for intermediate milestones can be determined in a similar manner.  Chapter  Introduction  1.  12  Project Cost n  Discounted  Project  Cost  =  ^  WPCi  (1.6)  t=i n  Project  Cost  in Total  Dollars  =  WPC  (1.7)  WPCcDi  (1-8)  TDi  t=i n  Project  Cost  in Current  Dollars  =  ^ i  = l n  Project  Cost  in Constant  Dollars  = ^  VFPCco;  (1-9)  i=i Project Revenue r  Discounted  1.3.3  Project  Revenue  — ^  NRSi  (1-10)  Project Decision Level  The variables at the project decision level are as follows.  Net Present Value NPV  — Discounted  Project  Revenue  — Discounted  Project  Cost  (1.11)  Internal Rate of Return IRR  1.3.4  = Discount  Rate  when  NPV = 0  (1.12)  Observation  The variables at every level of the project economic structure can be described by Y = #(X), where Y is defined as the derived variable and X is the vector of its primary variables. The derived variables of the lower levels of the project economic structure are the primary variables for the higher levels. At the work package/revenue stream level the derived variables are work package duration, start time, cost and  Chapter 1.  Introduction  13  net revenue streams. At the project performance level the derived variables are the project duration, cost, revenue and cash flow profile while the primary variables are the derived variables at the work package/revenue stream level. At the project decision level the derived variables are project net present value and internal rate of return, while the primary variables are discounted project cost and revenue. At the work package/revenue stream level, time, cost and revenue may be predicted using a variety of functional forms - growth and decay functions for revenue, production functions for time and cost through to network models. These production functions are generally multiplicative and/or additive in nature. The functions for derived variables at the project performance and decision levels are always predetermined and linear.  1.4  Objectives of the Research  The primary objectives of this research are: 1. to develop an analytical method for economic risk quantification during feasibility analysis for large engineering projects, 2. to computerize the method to explore its behavior, to validate it and to test its practicality in the measurement of uncertainty of performance and decision variables. The secondary objective of this research is to lay the foundation for obtaining the input data necessary to make the analytical method a practical tool for the construction industry. The input data are in the form of subjective probabilities and correlation matrices for primary variables.  Chapter 1. Introduction  14  The desired features of the analytical method are: model the interaction of time, cost and revenue throughout the life cycle of the project; provide the freedom to model a project to any level of detail using any number of variables; recognize the constraints that exist during feasibility stage, such as data limitations and the need for subjective probabilities; quantify the uncertainty in and estimate bounds on performance variables such as duration, cost, revenue, net present value and internal rate of return of a project; perform sensitivity and probabilistic analysis; consider multiple paths (shorter paths with higher variance or skewness) when evaluating project duration; treat correlations at all levels of the project; estimate individual contributions to overall uncertainty; provide intermediate milestone information to set realistic targets for performance; and have theflexibilityto model and evaluate a range of alternatives economically to select the best strategy to develop a project.  1.5  Previous Research and Motivation  A review of the literature shows that estimates for project decision and performance variables are still treated deterministically by most authors. A number of authors have recognized the random nature of estimates and adopted probabilistic concepts in developing their methods. These methods are classified below depending on their individual applications. Probabilistic T i m e Methods  : those which evaluate the duration of activities and  the project as the decision criterion (Ahuja and Nandakumar, 1985; Ang et al., 1975; Carr et al., 1974; Crandall, 1976, 1977; Crandall and Woolery, 1982; Elmaghraby, 1977; Hall, 1986; Jaafari, 1984; Kennedy and Thrall, 1976; King and Wilson, 1967; King et al., 1967; King and Lukas, 1973; McGough, 1982; Mirchandani, 1976; Pritsker and Happ, 1966; Pritsker and Whitehouse, 1966; Woolery and Crandall, 1983).  Chapter 1.  Introduction  15  Probabilistic Cost Methods : those which evaluate project cost as the decision criterion (Bjornsson, 1977; Deshmukh, 1976; Flanagan and Norman, 1980; Flanagan et a l . , 1987; Hemphill, 1968; Reinschmidt and Frank, 1976; Shafer, 1974; Smith and Thoem, 1976; Spooner, 1974; Vergara and Boyer, 1974; Wallace, 1977). Probabilistic Time/Cost Methods : those which treat cost as time dependent when evaluating project cost as the decision criterion (Ahuja and Arunachalam, 1984; Baker, 1986; Borcherding, 1977; Chapman, 1979; Chapman and Cooper, 1983; Chapman et al., 1985; Cooper et al., 1985; DeCoster, 1976; Diekmann, 1983; Jaafari, 1988a; 1988c; Moeller, 1972; Thompson and W h i t m a n , 1973; V a n Tetterode, 1971). Probabilistic Present Value Methods : those which evaluate project net present value and internal rate of return as decision criteria (Cooper and Chapman, 1987; Hillier, 1963, 1969; H u l l , 1980; Pouliquen, 1970; Reutlinger, 1970; Thompson, 1981; Thompson and W h i t m a n , 1974; Thompson and Wilmer, 1985; Wagle, 1967; Zinn et a l , 1977). From this classification, only those methods which evaluate project net present value and internal rate of return are suitable for economic feasibility studies because they represent the family of criteria used to evaluate a project.  O f these, C A S P A R  (Computer Aided Simulation for Project Appraisal and Review) developed by Thompson and W i l m e r (1985) is the widely applied model for large engineering projects Severn T i d a l Power (Thompson et al., 1980), Mersey Barrage (Perry et al, 1983). C A S P A R (Thompson and W i l m e r , 1985) is a project management tool designed to model the interaction of time, cost and revenue throughout the entire life of a project.  It differs from the normal economic appraisal model as it is network based  and is designed to simulate the realistic interaction of time and money.  CASPAR  models a project i n four stages. T h e first is a definitive model of the project constructed from a network of inter-related  activities using a precedence diagram, to  Chapter 1. Introduction  16  which costs and revenues are attached. The second stage identifies and investigates major uncertainties by performing a sensitivity analysis. During the third stage the definitive model is reviewed in light of the sensitivity analysis. At the fourth stage a probabilistic risk analysis is performed using the revised definitive model in a Monte Carlo simulation. A suitable probability distribution is assumed for the uncertain variables - a generalized triangular distribution has been assumed for variables in the applications. The decision criteria are the project net present value and internal rate of return. PROJECT (Thompson and Whitman, 1974) is an older version of CASPAR. When CASPAR and other simulation based methods (Bjornsson, 1977; Flanagan et al., 1987; Hull, 1980; Jaafari, 1988a; 1988c; Moeller, 1972; Pouliquen, 1970; Thompson and Whitman, 1974; Van Tetterode, 1971) are considered in the context of the desired features of the analytical method, issues such as modelling interaction of time, cost and revenue throughout the life cycle of the project; quantifying uncertainty of decision variables by developing cumulative distribution functions; performing sensitivity and probabilistic analysis; treating correlations at the level of variable input; the effect of multiple paths in the project network when evaluating project duration; and obtaining milestone information are resolved. However, when the number of variables in the analysis is large and the variables are correlated, Monte Carlo simulation can be both time consuming and computationally expensive, precluding exploration of a wide range of alternative strategies. Hence, the motivation for an analytical method that can handle a realistic formulation of the problem, a large number of correlated variables in the analysis and yet is computationally economical, thereby permitting the evaluation of several strategies.  Chapter 1. Introduction  1.6  17  Structure of the Thesis  Chapter two develops the risk measurement framework which is the foundation for the analytical method.  The framework, based on four assumptions, quantifies the  uncertainty of a derived variable that is functionally related to a set of primary variables (Y = £f(X)). The uncertainty of the derived variable is quantified by developing a cumulative distribution function for it. This development is based on the first four moments of a derived variable obtained from moment analysis using the truncated Taylor series expansion of the transformed function for g(X), and the first four moments of transformed variables. The first four moments are the expected value and second to fourth central moments.  The correlations between primary variables are  treated by using a variable transformation approach. A numerical example is used to demonstrate the framework, while the stochastic breakeven problem is used for comparison with some published results (Kottas and Lau, 1978). Chapter three develops an approach to elicit accurate and calibrated subjective probabilities as percentile estimates of an expert's subjective prior probabihty distribution for a primary variable. The analysis and verification method ensures that the measured belief is coherent and useful for the quantification of uncertainty of a derived variable. Chapter four discusses the correlations between variables. The discussion highlights the positive definite correlation matrix. A method to elicit a positive definite correlation matrix for primary variables and a method to obtain a positive definite correlation matrix for the derived variables when only linear correlations between primary variables are available are developed. Numerical comparisons under general conditions and multicollinearity are performed to show that the variable transformation approach is more robust than the standard method used to treat correlations in moment analysis.  Chapter 1. Introduction  18  Chapter five describes a study on the decomposition of a derived variable that is sometimes estimated holistically in the elicitation of subjective probabilities. T h e study contains hypotheses,  an experiment and test statistics to compare the two  estimation approaches used in engineering risk quantification.  T h e duration of an  activity is used as the example for the derived variable to compare holistic versus decomposed methods of estimation. Chapter six combines all of the developments and studies done in chapters two to five with the project economic structure to develop the analytical method for time and economic risk quantification for large engineering projects.  T h e method com-  putes the moments for derived variables at the work package/revenue stream level (work package duration, cost, and net revenue), project performance level (project duration, cost and revenue) and project decision level (net present value and internal rate of return) using the moments and correlation matrices for primary variables in their functional forms. T h e shape characteristics of the derived variables are used to approximate Pearson type distributions to quantify their uncertainty. T h e computed moments for derived variables at project decision and performance levels are exact. The approximations for moments are only for the derived variables at the work package/revenue  stream level.  T h e expected value, standard deviation and cumulative  distribution function for project duration are obtained from the modified P N E T approach (Ang et al., 1975), while those for project internal rate of return are derived from a variation of Hillier's method (Hillier, 1963). Chapter seven describes the validations and applications of the analytical method. The validations are done using Monte Carlo simulation. T h e modified P N E T algorithm is validated by solving two numerical examples that were presented by A n g et al. (1975).  T h e Monte Carlo simulation process is first validated using two Hm-  iting cases. T h e first hmiting case is a parallel network while the second is a single  Chapter 1. Introduction  19  dominant path in a highly interrelated network. Six simulations of two engineering projects are used to validate the analytical method. The data for the first example is obtained from an actual deterministic feasibility analysis. The second is a hypothetical engineering project developed to demonstrate the full potential of the analytical method. The types of sensitivity analyses that can be performed by the analytical method are explored. A detailed method to distribute the contingency for a derived variable to its primary variables using one of the sensitivity analyses is presented. Chapter eight contains the conclusions and recommendations for future work. Appendices A, B, C and E contain proofs and derivations used for the developments described by this thesis. Appendix D describes the two computer programs, "ELICIT" and "TIERA", developed to obtain input data and perform time and economic risk quantification. Appendix F contains the input values used for the numerical examples presented in chapter seven.  Chapter 2 Risk Measurement Framework  2.1  General  The framework to quantify the uncertainty of a derived variable is developed in this chapter. The inspiration for this development is the "unified statistical framework for probabilistic planning models" suggested by Kottas and Lau (1980), (1982). Their framework is a computational alternative to simulation for models involving additive and multiplicative functions of random variables. The proposed framework is for any arbitrary function, #(X), between the derived variable and its primary variables. This development is based on four assumptions which are explicitly identified and discussed, moment analysis and a function #(X).  The use of a truncated Taylor  series expansion of the function, <7(X), for moment analysis generalizes the type of functional relationship between the derived variable and its primary variables. In addition to <?(X), moments of the primary variables are required to evaluate the moments of the derived variable. The Pearson family of distributions and subjective percentile estimates are used to approximate the moments of the primary variables. The cumulative distribution function for the derived variable is approximated from the Pearson family of distributions using its first four moments. The next section describes the Pearson family of distributions. In the third section an iterative process for approximating the first four moments of a primary variable is developed. The fourth section describes the approach to approximate the first four  20  Chapter 2. Risk Measurement Framework  21  moments of the derived variable. The approximation of the cumulative distribution function for the derived variable is described in the fifth section. The application of the risk measurement framework to three examples: duration of a construction activity; the breakeven analysis problem; and a linear function is presented in the sixth section. The seventh section highlights the contributions of this chapter.  2.2  The Pearson Family of Distributions  The Pearson family of distributions are obtained as solutions of the differential equation which, when the origin of x is at the mean has the form,  dy T dx  - y (x + b)  =  —T"T  7  L  2  a -+- 6 x + c x  l  < x < L  2  (2.1)  l  where the coefficients a,fc,and c are functions of the moment ratios (V/Si, 02)>  a n  d  may be expressed as (Amos and Daniel, 1971),  a =  h  "'< ' 10 & - 12 ft - 18 4 f t  3  f  t  =10f ^- '12V - > 18 3  3  By  2  (2.2)  K  1  (2.3)  K  1  2/3 - 3 / 3 , - 6 10 & - 12 0 i - 18 2  where A = —5 and /3 =—5. P2, p3, and p 2  A*!  central moments of the random variable x.  4  are the second, third, and fourth  Chapter 2. Risk Measurement Framework  For each pair of (y/8i,B ) 2  22  the solution of equation (2.1) defines a density function  with mean zero on an interval L  x  < x < L . The solutions assume a variety of 2  different mathematical forms according to the values of the moment ratios (Johnson et al., 1963). These forms or "types of distributions" may be associated with different regions in a plane having rectangular co-ordinate axes y/j5[ and B (see figure 2.1). 2  Since the shapes of the distributions change continuously across the boundaries of the regions, Johnson et al. (1963) compiled tables for the Pearson family of distributions by treating the problem as a single unity. These tables, tabulate the standardized deviate forfifteenpercentage points based on the values of  and  B . Thefifteenpercentage points are namely the median, upper and lower 0.25, 0.5, 2  1.0, 2.5, 5.0, 10.0, and 25.0 percentage points. Amos and Daniel (1971) extended the Pearson tables to cover a much larger area of the plane (y//3~[,B ). 2  Assumption 2.1 : The derived and the primary variables are continuous and their probabihty distributions are approximated by the Pearson family of distributions. The variables of the project economic structure such as time, cost, revenue, inflation and interest rates are all continuous in nature. The continuous random variable is a convenience for probabilistic applications. Most of the probabihty distributions used for applications in engineering such as - Normal, Beta (Typel), Exponential (TypeX), square (Typelll),  Uniform, Lognormal (TypeV),  F (TypeVI),  Gamma  (Typelll),  Student's t (TypeVII),  Chi-  are members of the Pearson family of distributions  (Harr, 1987; Ord, 1985). While there is no guarantee that a Pearson type distribution will always provide a good fit for a variable, the theoretical developments of the Pearson family (Kendall and Stuart, 1969; Ord, 1972) and the widespread applications of the Pearson system indicate that it will provide a good fit to most "real life" distributions (Kottas  Chapter 2. Risk Measurement Framework  Figure 2.1: Moment Ratio Plane Showing Pearson Types I-XII Source : Amos and Daniel, (1971)  23  Chapter 2. Risk Measurement Framework  24  and Lau, 1982). However, it must be noted that there are theoretical examples for which Pearson type provides a poor fit for a variable with the same first four moments (Pearson, 1963). The assumption permits the development of the framework as a "distribution free" method because of the highflexibilityof the Pearson system (SiddaU, 1972).  2.3  The Moments of a Primary Variable  A continuous random primary variable approximated by a Pearson type distribution can be expressed by its first four moments (Kendall and Stuart, 1969; Ord, 1972). The first four moments of a primary variable are its expected value and second to fourth central moments.  Assumption 2.2 : An expert can provide estimates for percentiles of his subjective prior probability distribution for a primary variable at the input level. The use of subjective probabilities to quantify the uncertainty about the primary variables stems from the observation that actuarial or relative frequency based data are unavailable or not meaningful for direct input as probability forecasts for estimating future events (Wright and Ayton, 1987). To use subjective probabilities as input to risk analyses, they have to be accurate, calibrated and coherent (Lindley et al., 1979). In chapter three a method to elicit accurate, calibrated and coherent subjective probabilities as percentile estimates of an expert's subjective prior probability distributions for primary variables is developed. A step by step iterative process for approximating the first four moments of a primary variable (see figure 2.2) is set out in this section. The sole purpose of approximating third and fourth central moments of primary variables is to approximate third and fourth central moments of the derived variable. This information is required  Chapter 2.  Risk Measurement  25  Framework  to fit a Pearson distribution and to make probabilistic statements about the derived variable. The starting point follows from the first two assumptions. The first assumption permits the use of the table for percentage points of standardized Pearson distributions (Amos and Daniel, 1971; Johnson et al., 1963). From the second assumption estimates for the 5, 25, 50, 75, and 95 percentiles of an expert's subjective prior probability distribution for a primary variable are elicited. The process of approximating the first four moments of a primary variable stops when either of the following conditions are met. Condition 1 : When a„  Q5  (equation 2.8) is greater than <TQ  025  (equation 2.9),  Condition 2 : When "best fit" distribution is the same as that of the previous cycle. The step by step process for generating the first four moments for a primary variable is as follows.  Step 1 : Subjective Estimates Obtain the estimates for the 5, 25, 50, 75, and 95 percentiles of the expert's subjective prior probability distribution for the primary variable (assumption 2.2).  Step 2 : Expected Value and Standard Deviation Since the time Malcolm et al. (1959) suggested the well known approximations for PERT, a number of different studies have been done on the approximations for the expected value and standard deviation of a continuous random variable (Britney, 1976; Davidson and Cooper, 1976; Hull, 1978; Keefer and Bodily, 1983; Moder and Rodgers, 1968; Pearson and Tukey, 1965; Perry and Greig, 1975). From an extensive empirical study, Pearson and Tukey (1965) developed approximations to the expected value and standard deviation for the Pearson family of distributions. This development was based on the constancy of the ratio of the distances between suitable symmetrical percentage points to the standard deviation.  Chapter 2. Risk Measurement Framework  Step 1 : Elicit Subjective Estimates for the Uncertain Primary Variable  1  *  Step 2 : Approximate Expected Value and Standard Deviation (o = o ) 0X)5  I  Step 3 : Standardize Subjective Estimates  ~  I Step 4: Select "Best Fit" Distribution  Yes (Condition 2)  Step 5 : Obtain 2.5% and 97.5% Estimates Step 6 : Check the Standard Deviation Yes (Condition 1)  Step 7 : The Iterative Cycle (o* = a 0.025)  Re-estimate Percentile Values (see section 3.7)  Step 8 : VB, and B for the Primary Variable 2  Step 9 : The Central Moments for the Primary Variable  Figure 2.2: The Steps of the Iterative Process  Chapter 2. Risk Measurement Framework  27  The approximation for the expected value from percentile values ([P%]) is, = [50%] + 0.185 A  E[X]  (2.5)  where A = [95%] + [5%] - 2 [50%].  (2.6)  The approximation for the standard deviation using percentile values and the iteration scheme suggested by Pearson and Tukey (1965) is, a  =  {<To  max  05  (2.7)  , 0-0.025}  where [95%] - [5%] 0.05  T  max  <3.29 - 0.1  (2.8)  2  00.05  3.08  [97.5%] - [2.5%]  (2.9)  '0.025  max  <3.98 - 0.1  ,3.66 I-00.025-  cr 05 0  and  00.025  are the approximations for the standard deviation from the  previous iteration. For the first iteration  <r .05 0  and  00.025  are defined on the basis of  figure (3) of Pearson and Tukey (1965) as, 0*0.05  00.025  [95%] - [5%] 3.25  (2.10)  [97.5%] - [2.5%] 3.92  (2.11)  Pearson and Tukey. (1965) state that the error in the approximation of the expected value is not more than 0.1% for a large area of the  (\//3i,/32)  plane and not more than  Chapter 2. Risk Measurement Framework  28  0.5% for the rest. The error for the standard deviation is less than 0.5% for a very large area of the  (^/Wiifii)  plane.  After comparing most of the approximations available to estimate expected value and standard deviation of continuous random variables from judgmental (subjective) estimates, Keefer and Bodily (1983) concluded that the approximations suggested by Pearson and Tukey (1965) are more accurate, often by a wide margin, than their competitors. For their study Keefer and Bodily (1983) used only the approximation given by equation (2.8) for the standard deviation because of the difficulty of assessing the 2.5 and 97.5 percentiles subjectively. However, the standard deviation approximated from equation (2.8) alone is an underestimation for a large part of the Pearson family (Pearson and Tukey, 1965). In developing the framework both approximations for the standard deviation (equations  2.8 and 2.9) are included in the iterative approach,  thereby ensuring that the approximated standard deviation for the primary variable is the maximum. The 2.5 and 97.5 percentiles for equations (2.9) and (2.11) are obtained as described in steps 3 through 7. The five subjective estimates from step 1 are used in equations (2.5), (2.6), (2.8) and (2.10) to approximate the expected value and the standard deviation for the primary variable. The process of determining the standard deviation using equation (2.7) starts with cr equal to  <TQ . 05  Step 3 : Standardize the Subjective Estimates  Using the approximations for the expected value and the standard deviation of the primary variable from step 2, the five subjective estimates from step 1 are standardized by, =  *, - EjX] cr  where x is a subjective percentile estimate and X is its standardized value. p  p  Chapter 2. Risk Measurement Framework  29  Step 4 : The "Best Fit" Distribution The standardized estimates from step 3 are then compared with the 5.0, 25.0, 50.0, 75.0, and 95.0 percentage points for the standardized Pearson variable tabulated by Amos and Daniel (1971), by minimizing the sum of squared deviations as suggested by Ord (1972) to approximate the "best fit" distribution. The acceptable error of the approximation (square root of the sum of squared deviations) for the "best fit" distribution should be specified by the user. A maximum cumulative error of 10% of the standard deviation is used as a default value in the computer program.  Step 5 : 2.5% and 97.5% Estimates For the "best fit" distribution from step 4 obtain the standardized Pearson variable values for 2.5% and 97.5% points (see figure 2.3). From these standardized values generate the actual values for the two percentiles from, x  p  = Xa  + E[X]  p  (2.13)  Step 6 : Check for the Standard Deviation From the generated values for 2.5% and 97.5% in step 5 and equations (2.9) and (2.11) evaluate O"o.o25If  o.o5  <T  K  0o.o5  > ^0.025  <  <T  o.o25  8° t° step 8 as Condition 1 is satisfied.  :  :  6° *° ^ P 7 f° * s  e  r  n e  iterative cycle. The standard deviation  for the primary variable cr is now equal to (TQ 025  Step 7 : The Iterative Cj'cle Go back to step 3 to start the iterative cycle. If the "best fit" distribution from step 4 is same as for the previous cycle then go to step 8 as Condition 2 is satisfied. If not continue till either of the conditions are met for a specified number of iterative cycles.  Chapter 2. Risk Measurement Framework  0.025  ^0.975  Standardized Percentile Values  Figure 2.3: The "Best Fit" Distribution  Chapter 2. Risk Measurement Framework  Step 8 :. y/fii  and  #2  for the Primary Variable  Obtain y/fa and B from the Pearson table (Amos and Daniel, 1971) for the 2  selected "bestfit"distribution in step 4. When Condition 1 is satisfied, from equation (2.7) the standard deviation for the variable is  <Tp  05  (the condition used by  Keefer and Bodily, 1983). When Condition 2 is satisfied the standard deviation for the variable is  OQ  0  2  S  .  Then, the requirement specified by Pearson and Tukey (1965)  in equation (2.7) is fulfilled.  Step 9 : The Central Moments From the standard deviation approximated at step 8 and the y/p\ and B for the 2  "best fit" distribution, the second, third and fourth central moments of the primary variable are evaluated from,  p (X)  = a  V*{X)  = \[fhv*  (2.15)  p (X)  = fa a  (2.16)  2  4  2.4  (2.14)  2  4  Moments of the Derived Variable  The method to approximate the moments of the derived variable is based on moment analysis. The moment analysis use the moments of the transformed variables and a truncated Taylor series expansion of the transformed function for g(X) to approximate thefirstfour moments of the derived variable. Assumption 2.3 : A derived variable can be more accurately estimated from a set of primary variables that are functionally related to it than by direct estimation.  Chapter 2. Risk Measurement Framework  32  When a functional form between a derived variable and primary variables is used in stochastic applications, it is based on the premise that it is more accurate to estimate the primary variables individually than to estimate the derived variable directly (Kottas and Lau, 1982). It reflects the engineering penchant to seek more detail as a way of seeking greater precision. The analytical method developed in chapter six does not require this assumption at all levels but allows for elaboration of time and cost estimating relationships to achieve more precision. However, when variables which are sometimes assessed holistically are used in decomposed estimation (duration, productivity) the assumption becomes debatable. Chapterfiveexamines the validity of assumption (2.3) for such variables.  2.4.1  Truncated Taylor Series  For a system of n primary variables described by the function, Y = o(X), which has continuous partial derivatives, the Taylor series expansion of the function g(X) about the mean values X is given by,  g(X) =  g(X) +  9 9  dXi  i=i  + ^ £  ( A W , )  (2.17)  The Taylor series is then truncated at the second order such that the truncation error of the approximation is,  1*1 = S t t h *  - ^(X,  - XX i)(  k  - X  k  )  w  ^  T  k  (2.18)  Chapter 2. Risk Measurement Framework  33  The truncated second order approximation of the expansion is  g(X)  + £  {X  t  t=i  -  X) t  dg  dXi  (2.19)  where the partial derivatives are evaluated at X . The partial derivatives constitute sensitivity coefficients and either increase or decrease the contribution of each term, depending on the importance of each variable to the derived variable, thereby, acting as an in-built sensitivity analysis. The second order approximation provides reasonable mathematical ease for moment analysis.  A third or higher order approximation would give more accurate  results (Tukey, 1954), but mathematical complexities that are involved when treating statistical dependencies prohibit their use. The moments of a derived variable can be evaluated using the truncated Taylor series expansion with the definition of moments (Siddall, 1972). Then, the first four moments of the derived variable are, (2.20)  (2.21)  (2.22)  H{Y)  E (Y  E[Y\Y  (2.23)  Chapter  2.  Risk  Measurement  34  Framework  To evaluate accuratefirstfour moments, correlations between primary variables have to be treated. The standard approach to treat correlations in moment analysis is by expanding the above equations (Ang and Tang, 1975). This approach can include the linear correlations easily only in the approximation for thefirsttwo moments. A variable transformation approach that can include the linear correlations in the higher order moments of the derived variable is used in the development of this framework. Assumption 2.4 : The correlations between primary variables are linear. Generally, when the correlations among primary variables are treated it is the linear correlations. If all the variables in the system are normally distributed then the linear correlations between variables are the true correlations. In general, the primary variables which describe a work package are not normally distributed. Consequently, one is faced with the prospect of non-linear correlations. Obtaining non-linear correlations or treating non-linear correlation in a multivariate situation are still complex and largely unresolved theoretical issues. Most four moment methods (Jackson 1982; Siddall, 1972) avoid the treatment of correlations; their treatment is important, however, if one wishes to establish an accurate measurement of risk (Perry and Hayes, 1985b; Cooper and Chapman, 1987) and a realistic estimate of bounds.  2.4.2  Variable Transformation Method  A set of correlated variables are transformed to a set of uncorrelated variables having mean values and unit variances by, Z = IT  1  D  - 1  X  (2.24)  35  Chapter 2. Risk Measurement Framework  where X is the vector of correlated random variables, X = [ X i , X ] ; T  n  vector of transformed variables with unit covariance matrix; L  -  1  Z is the  is the inverse of the  lower triangular matrix obtained from the Cholesky decomposition of the correlation matrix R ( = L X ) ; and D T  -  1  is the inverse of the diagonal matrix of standard  deviations of the X vector ( D = diag Proof of the Transformation Let X be a vector of correlated random variables with covariance matrix C and correlation matrix  R . Let Z  X  be the vector of transformed variables from  equation (2.24) with covariance matrix C . Then, Z  Var[Z]  C  =  VarfL'  r= L-1 T~»-1 D C  Z  Using the relationship R = D  -  1  D  1  _  1  IT-1 T-k-l |L D - ]^ 1  X  C D  _  D -  = L - LL  1  X  (2.25)  X] 1  (2.26)  and the Cholesky decomposition of the  correlation matrix R = L L , T  L"  1  D" C 1  X  1  1  T  =L  (2.27)  T  Similarly, L-  Since  [L ] T  1  D" C 1  =  X  D"  1  and D L  D  _  I  [L ] T  _  1  C  X  _ 1  = L  = [D  - 1  [L~ D  ] _  T  [L ] T  _ 1  = 1  because D j  T  = I  _  1  (2.28)  is symmetric, (2.29)  From equations (2.26) and (2.29), C  Z  = I  (2.30)  T h e r e f o r e , t h e t r a n s f o r m e d v a r i a b l e s are u n c o r r e l a t e d w i t h u n i t v a r i a n c e s .  Chapter 2. Risk Measurement Framework  36  Even with assumption (2.4) it is not possible to prove that the variable transformation precludes the existence of non-linear correlations amongst the transformed variables. This has implications for the terms treated in approximating the fourth central moment (see section 2.4.5, chapter four and Appendix A). A similar transformation to obtain a set of standard variates with zero means and unit covariance matrix from a set of correlated variables was used by Der Kiureghian and Liu (1986) for applications in structural reliability.  2.4.3  Moments of the Uncorrelated Variables  Since the transformation given by equation (2.24) is linear the first four moments of the transformed uncorrelated variables can be evaluated directly from the moments of the correlated primary variables. Then, the first four moments of a transformed uncorrelated primary variable are, E[Zi]  K(Zi)  = J2 An E[Xj]  p (x )  = £ 4 3=1  + E  n  2  2  j  n  E  *  Aii ik cov(X X ) A  j=i k=j+i  toW  (2.31)  h  k  = 1  (2.32)  (2-33)  E 4 J'=I  «  Y. 4 ^(Xj)  (2.34)  3=1  where A = L D and E[Xj], p (Xj), 1  1  2  of the j  th  p (Xj), 3  p (Xj) 4  correlated primary variable in the X vector.  are the first four moments  Chapter 2. Risk Measurement Framework  2.4.4  37  The Function  T o use m o m e n t s  of the transformed  uncorrelated  first four m o m e n t s o f t h e d e r i v e d variable Y, to the u n c o r r e l a t e d  space.  p r i m a r y variables  t h e f u n c t i o n g(X.)  T h i s transformation  to evaluate t h e  has to be transformed  is d o n e f r o m ,  X = D L Z  Then  the t r a n s f o r m e d  complicated,  function  is Y  this t r a n s f o r m a t i o n  =  (2.35)  G(Z). If t h e o r i g i n a l f u n c t i o n  increases t h e c o m p l e x i t y  c/(X) was  as each variable i n t h e X  vector is r e p l a c e d b y a linear c o m b i n a t i o n o f variables f r o m t h e Z vector. since this t r a n s f o r m a t i o n the c o m p u t e r ,  is linear a n d i n p r a c t i c e  t h e increased c o m p l e x i t y  t h e replacement  of the transformed  However,  w i l l be d o n e b y  f u n c t i o n is n o t a p p a r e n t  to the user.  2.4.5  The First Four Moments  T h e first four m o m e n t s f o r m e d f u n c t i o n , G(Z),  o f t h e d e r i v e d variable  are n o w evaluated  as the f u n c t i o n for t h e d e r i v e d variable.  using the trans-  T h e moment  analysis  considers t h e t e r m s i n v o l v i n g u p to t h e f o u r t h order because m o m e n t i n f o r m a t i o n is available  u p t o t h e f o u r t h order.  n o t available are neglected the expected  T h e cross m o m e n t  terms for w h i c h i n f o r m a t i o n is  (see A p p e n d i x A for derivations).  T h e a p p r o x i m a t i o n for  value is,  E[Y]  * G(Z) + \ £  | ^ p (Z ) 2  {  (2.36)  Chapter 2. Risk Measurement Framework  38  the approximation for the second central moment is,  dG dZi  E t=l "  dG d G 2  1 A 4 4-t  +  i  =l  [  (2.37)  &G_ " dZf  H&)  -  [p (Zi)f 2  t  L  the approximation for the third central moment is,  E  dG 1 dZi  3  t=l L on dG + -y dZ {  (2.38) 8G dZt 2  p {Z ) A  {  -  [^(Z^f  and the approximation for the fourth central moment is,  MY)  «  E i=i  dG dZi  (2.39)  fi (Zi) 4  where Z is the vector of transformed uncorrelated variables and G(Z) is the transdG dZ{  dG dZ± 2  formed function for the derived variable. The first (~zzr) and second (-^j) partial derivatives with respect to the transformed variables are evaluated numerically (Howard, 1971). The sole purpose of approximating the third and fourth central moments of the derived variable is to approximate a cumulative distribution function for it. In the  Chapter 2. Risk Measurement Framework  39  approximation for the fourth central moment it is evident that only the first term of the expansion is used (see Appendix A.5). A second fourth order term which cannot be evaluated except for the case of statistical independence and the special case when there are no non-linear correlations between transformed variables, has been ignored. The underestimation of the fourth central moment can create a problem however, because one may not be able to fit a valid Pearson distribution to the derived variable. A valid distribution requires the relation f3 — 0i — 1 > 0 be satisfied (Johnson 2  et al., 1963).  2.5  Cumulative Distribution Function  The approximated central moments are then used to evaluate the shape characteristics, skewness (y/^i) and kurtosis (f3 ) for the derived variable from, 2  (2.40)  P2  where p (Y), 2  =  H(Y) ^{Yf  (2.41)  Pz(Y) and /^(V) are the approximated central moments for the de-  rived variable. A cumulative distribution function for the derived variable is approximated from the Pearson family of distributions (assumption 2.1) using the method suggested by Johnson et al. (1963). The approximated Pearson distribution is the one which corresponds most closely to the shape characteristics of the derived variable. The cumulative distribution function is the quantification of the uncertainty associated with the derived variable.  Chapter 2. Risk Measurement Framework  2.6  40  Application of the Framework  Three examples are presented to demonstrate the application of the framework. The first is a numerical example for a real construction activity. For the second example, results for the breakeven analysis problem from the framework are compared to those reported by Kottas and Lau (1978). The third is a linear function of the primary variables in the breakeven problem. It is used to highlight some of the reasons for the differences between exact moments and those approximated by the framework.  2.6.1  Example 1 : Activity Duration  The duration toflyform a typical slab of 3000 ft in a single suite per floor high-rise 2  is considered as the derived variable for the numerical example to demonstrate the risk measurement framework. The duration can be estimated from the decomposed relationship given by, =  T  A  + jrz  (- ) 2  42  where T is the duration toflyform a typical slab in days, Q is the estimated quantity in ft , P is the estimated labour productivity in ft /manhour 2  2  once the fly forms are  placed, L is the estimated labour usage in manhour s / day and A is the time required toflythe forms in days. Other authors (Jaafari, 1984; Hendrickson, 1987) have used decomposed relations to compute the activity duration. While the quantity is deterministic, the other three variables are considered as random. Then, equation (2.42) can be re-written as, T = X  X  + ^ Si- 2  = g(X)  (2.43)  ^ 3  The subjective percentile estimates for the random variables and the positive definite correlation matrix (R) elicited from an experienced engineer are given below.  Chapter 2. Risk Measurement Framework  41  Table 2.1: Subjective Percentile Estimates for A, P and L Variable  5%  A(days) 0.25 P{ft /mh) 17.0 L(mh 1 day) 75.0 2  R  =  25% 50% 75% 95% 0.33 0.375 0.42 0.5 19.0 20.0 21.0 22.0 83.0 88.0 92.0 96.0  • 1.0  -0.5  -0.5  1.0  . 0  -0.4  0 -0.4 1.0 .  The negative correlation between X and X suggests the greater the productivity, 1  2  probably the greater the efficiency of flying the forms and vice versa. The negative correlation between X and X implies that the smaller the crew the greater the 2  3  productivity (minimum congestion, all crew members visible and not able to hide). The expected values, standard deviations, and shape characteristics of the approximated Pearson type distributions for the random variables from equations (2.5) to (2.16) are given in Table 2.2. Table 2.2: Statistics for the Random Variables Variable A (X,) P (X ) L (X ) 2  3  Expected Value Standard Deviation 0.375 0.08 1.54 19.815 87.075 6.5  0.0 -0.8 -0.7  P\  9.0 4.1 3.3  The diagonal matrix of standard deviations (D) and lower triangular matrix from the Cholesky decomposition of the correlation matrix (R = L L ) are, T  Chapter 2. Risk Measurement Framework  D =  0.08  0.0  0.01  0.0  1.54 0.0  0.0  0.0  ; L  6.5  42  1.0  0.0  0.0  -0.5  0.866  0.0  0.0  -0.46188 0.88694  The transformed function G(Z), for the duration toflyform a typical slab is obtained using the above in equation (2.35). Z is the vector of transformed uncorrelated variables. The first four moments of the transformed uncorrelated variables, and  dG  dG 2  thefirst(-;r=-) and second  (TTT^)  partial derivatives with respect to the transformed  variables which are evaluated numerically (Howard, 1971) are given in Table 2.3. Table 2.3: First Four Moments and Partial Derivatives of Transformed Variables Variable  E[Zi)  M2(Z»)  z Z  4.68177 17.56525 24.25121  1.0 1.0 1.0  2 z  /*s(2i)  Pi{Zi)  dG dZi  ff'G dZf  0.0 9.0 0.14764 0.00525 -1.23168 8.28889 -0.05705 0.01181 -1.17720 5.94210 -0.11515 0.01525  The expected value, standard deviation, skewness and kurtosis respectively for duration to fly form a typical slab from equations (2.36) to (2.41) are 2.13 days, 0.2045 days, 0.622 and 3.095.  2.6.2  Example 2 : Stochastic Breakeven Analysis  The problem of breakeven (or cost-volume-profit) analysis under uncertainty has had considerable discussion in the management literature (Jaedicke and Robichek, 1964; Hilliard and Leitch, 1975; Starr and Tapiero, 1975; Kottas and Lau, 1978; Cooper and Chapman, 1987). Kottas and Lau (1978) used the breakeven analysis problem reported by Starr and Tapiero (1975) to present an "exact" four moment solution.  Chapter 2. Risk Measurement Framework  43  Their solution to the breakeven equation given by, P(x) = {p-c)x  - K  (2.44)  where p is the unit sale price; c is unit variable cost; x is sales volume; K is fixed cost; and P(x) is profit realized; was shown to be superior to that given by Starr and Tapiero (1975) using Chebyshev's Inequality. The framework is applied to the same numerical example used by Kottas and Lau (1978). In the numerical example p, c, x and K were assumed to be normally distributed with expected values, standard deviations and correlation coefficients as shown below.  fi = 1000; cr = 100; PPC = 0.3; p  p  fi = 600; cr = 60; = -0.4;  p a  e  x  c  x  p  P p x  cx  fi a  = 1000; - 200; = -0.2;  K K  p  Kx  = 250000; = 20000; = 0.2;  Since all the primary variables are normally distributed, there are no non-linear correlations between the transformed variables (see equations A.9, A.15 and A.20). Table 2.4 shows the comparison of the moments for P(x) approximated by the framework with those computed by Kottas and Lau (1978). Figure (2.4) shows the Pearson distributions approximated by Kottas and Lau (1978) and by the framework.  Table 2.4: Comparison of Moments and Shape Characteristics E[P(x)]  Kottas and Lau 144,400 1.2335 * 10 4.3964 * 10 4.5895 * 10 111,063 0.3209 3.0164 10  14  MP) a  P  p\  20  Framework 144,400.68 1.2111 * 10 4.5454 * 10 4.5135 * 10 110,048 0.3411 3.0775  10  14 20  Difference 0% -1.81% 3.39% -1.66% -0.91% 6.29% 2.02%  Chapter 2. Risk Measurement Framework  Figure 2.4: Approximated Pearson Type Distributions for P(x)  Chapter 2. Risk Measurement Framework  45  Kottas and Lau (1978) compared estimates from their approach to (1) what is the probability of at least breaking even ? and (2) what is the probability of realizing more than the expected profit of $ 144,400 ? with those from Chebyshev's Inequality and a simulation with a sample size of 50,000. Table 2.5 shows the comparison of those results with that from the framework. Table 2.5: Comparison of Estimation Approaches  Prob.[P(x) > 0] Prob.[P(x) > 144,400]  Starr and Tapiero > 41% > 0%  Kottas and Lau 90.9% 47.9%  Simulation Framework n = 50,000 91.2% 91.2% 47.7% 47.9%  The comparison of the approximated moments to the exact moments, and of the estimation approaches show that the proposed framework is robust. The next example is presented to highlight some of the reasons for the underestimation of the moments.  2.6.3  Example 3 : Linear Function  Assume a linear functional form given by,  Y=p  + c + x + K  (2.45)  where p, c, x and K are the same variables as in the previous example. In addition, assume that all of the variables are uncorrelated. Since all of the primary variables are normally distributed, they are now statistically independent. Hence, Y is also normally distributed and its exact moments can be computed. Table 2.6 shows the exact moments of Y and those approximated by the framework.  Chapter 2. Risk Measurement Framework  46  Table 2.6: Comparison of Moments and Shape Characteristics Exact 252,600 400053600 0 4.8012 * 10 0.0 3.0  E[Y) t*(Y)  17  VP\  02  Framework 252,600 400004963 0 4.7988 * 10 0.0 2.9992  17  Difference 0% -0.012% 0% -0.050% 0% -0.027%  The variance of Y is underestimated due to numerical differentiation. The first and second partial derivatives in equations (2.36) to (2.39) are computed numerically to provide for generality of the function. Table 2.7 gives thefirstpartial derivatives of Y with respect to the transformed variables. The second partial derivatives of Y with respect to the transformed variables are zero because the transformed functional form is also linear. Table 2.7: Comparison of the First Partial Derivatives of Y SY  5r °&  B  or  Exact Framework Difference 100 99.99392 0.006% 60 0.006% 59.99635 200 0.006% 199.98784 20000 19998.79419 0.006%  Hence, from equation (2.37), P2(Y)  ex  = 100 *1 + 60 *1 + 200 * 1 + 20000 *1 2  2  2  2  = 400053600  = 99.99392 * 1 + 59.99635 * 1 + 199.98784 * 1 + 19998.79419 * 1 2  = 400004963  2  2  2  Chapter 2. Risk Measurement Framework  47  When primary variables are statistically independent equation (2.34) should be,  = E  j=i  K  n{Xt)  + 6£  E  A  j=i fc=j+i  l l A  M*i)  p (X ) 2  k  (2.46)  and equation (2.39) should be,  MY)  =  &Y_  E dZi  MZi)  + eE E 1=1 l=i+l  t=l  &Y_ dZi  dY 2 p-AZ^Zi) dZj  (2.47)  For generaHty, the approximation for Pi(Y) is based on the assumption that transformed variables will only be uncorrelated. This assumption is reasonable because statistical independence will occur only when all the primary variables are normally distributed. Hence, it is evident that the fourth central moment for the derived variable from the framework will always be an approximation. Table 2.8 gives the first four moments and shape characteristics for Y when exact, those approximated by the framework in general and when corrected for this example by using equations (2.46) and (2.47) instead of (2.34) and (2.39). Table 2.8: Comparison of Moments and Shape Characteristics E[Y] MY) MY) MY) ft  Exact 252,600 400053600 0 4.8012 * 10 0.0 3.0  17  Framework 252,600 400004963 0 4.7988 * 10 0.0 2.9992  17  Corrected 252,600 400004963 0 4.8001 * 10 0.0 3.0  17  Chapter 2. Risk Measurement Framework  2.7  48  Summary  The proposed framework requires: a functional relationship, <7(X), between the derived variable and its primary variables; approximation of the first four moments of a primary variable from subjective estimates; approximation of the first four moments of the derived variable from moment analysis using a truncated second order Taylor series expansion of the transformed function and moments of the transformed variables; evaluation of shape characteristics of the derived variable; and approximation of the derived variable to a Pearson type distribution using its shape characteristics. The framework is suitable for systems where pre-determined functions are available, data limitations exist and the decisions are not based on extreme probabilities. The results from the application to the stochastic breakeven problem show that the framework is accurate. The use of a truncated Taylor series expansion of the system function for moment analysis (Ang and Tang, 1975; Benjamin and Cornell, 1970; Jackson, 1982; Siddall, 1972; Smith, 1971) or the four moment approach for the quantification of uncertainty of a derived variable (Kottas and Lau, 1980, 1982; Siddall, 1972; Jackson, 1982) are not unique. The method to approximate the first four moments of a primary variable from subjective probabilities and the variable transformation method to treat correlations between primary variables in the approximation of the first four moments of the derived variable are unique for this framework. The use of subjective probabilities recognizes the lack of input data for most risk analyses performed during the feasibility stage. The variable transformation method permits the inclusion of correlation information in the approximations for higher order moments of the derived variable which is neglected by the standard approach for moment analysis (see section 4.2 and Appendix A).  Chapter 2. Risk Measurement Framework  49  In the context of time and economic feasibility of an engineering project, all of the decision and performance parameters have well defined functional forms (even though the functions for derived variables at the work package/revenue stream level can change from analyst to analyst) and significant data limitations exist. In addition, strategic decisions such as contingencies and tolerances for those parameters rarely require probabihty values beyond the 90  th  percentile.  Therefore, the framework  becomes the foundation for the proposed method. The practical advantages of the framework are the rigor it imparts on the analysis process and the formalized procedure it imparts upon the participants. The analysts and the experts are forced to consider that the inputs are random and to structure their thinking in terms of range estimates. Hence, it quickly becomes apparent what primary variables are the major contributors to the uncertainty of the derived variable.  Chapter 3 E l i c i t a t i o n of S u b j e c t i v e P r o b a b i l i t i e s  3.1  General  The framework developed in the previous chapter to quantify the uncertainty of a derived variable is based on the assumption that experts can provide estimates for percentile values of their subjective prior probabihty distributions for primary variables in construction estimation. This is the measurement of the experts' belief about the uncertainty of primary variables. For the measured belief to be useful in the quantification of uncertainty of the derived variable it has to be accurate, calibrated, coherent and also be converted to moments. While the work described in this chapter is not conclusive, it provides a foundation for obtaining input data necessary to make the analytical method a practical tool for engineering construction. Also, it should be seen as a vital step towards standardizing and computerizing, to the extent possible, elicitation of expert input dealing with uncertainty. Consequently, this chapter achieves the secondary research objective identified in chapter one of this thesis. Developed in this chapter are an approach to elicit the desired percentile values of an expert's subjective prior probabihty distributions for variables in engineering construction and a method to ensure the elicited subjective probabilities are coherent and useful in the quantification of uncertainty of the derived variable.  50  Chapter 3. Elicitation of Subjective Probabilities  3.2  51  Subjective Probabilities  After the detailed work of DeFinetti (1970) and Savage (1954), the use of subjective probabilities - the degree of belief in the occurrence of an event attributed by a given person at a given instant and a given set of information, is considered a quantification of uncertainty, because it represents the extent to which the person believes a statement is true, based on the information available to him at that time (Hampton et al., 1973). Subjective probabilities are generally eh cited for use in Bayesian decision analysis. Lindley et al., (1979) state that to use subjective assessments in decision analysis they have to be accurate, calibrated and coherent. A person is calibrated if for all events assigned a probability, q, the proportion that actually occur is in fact equal (or close) to q (Budescu and Wallsten, 1987). A set of subjective probabilities are coherent if they are compatible with the probability axioms. Coherence is essential if the assessments are to be manipulated according to probabilistic laws (Lindley et al., 1979). Wallsten and Budescu (1983) argue that it is not necessary for encodings to obey axioms of additive probability theory in order to be valid measures of belief. Such conformity is necessary only if the user of the judgements wants to treat them as additive probability measures. Wright and Ayton (1987) were surprised by the lack of significant relations between coherence and forecasting performance (i.e calibration), because the two ways of assessing the adequacy of a forecaster are logically interrelated. They state that if a forecaster is incoherent he cannot be well calibrated, but it does not follow that coherence necessarily produces good calibration. A review of the subjective probability literature show that it can be classified into three broad categories, namely, theoretical, review and empirical. The theoretical literature can be further divided into axioms on subjective probabilities (DeFinetti,  Chapter 3. EHcitation of Subjective Probabilities  52  1970; DeGroot, 1970. 1975, 1979; French, 1980, 1982; Lindley, 1982; Lindley et al., 1979; Pratt et al., 1964; Savage, 1954, 1971; Suppes, 1975), assessment and consensus of subjective probabilities (Ashton and Ashton, 1985; Bacharach, 1975; Bordley, 1982; Bordley and Wolff, 1981; Diaconis and Ylvisaker, 1985; Dickey, 1979; Dickey and Chen, 1985; Dickey et al., 1986; French, 1985; Holt, 1986; Press, 1979; Winkler, 1986b) and expert resolution (Ashton, 1986; Clemen, 1986; Einhorn, 1972; French, 1980, 1986; Lindley, 1986; Lock, 1987; Morris, 1974, 1977, 1983, 1986; Schervish, 1986; Winkler, 1981, 1986a). Some of the review literature on subjective probabilities are (Beach, 1975; Beach et al., 1987; Budescu and Wallsten, 1987; Bunn, 1979a, 1979b; Chesley, 1975; ChristensenSzalanski and Beach, 1984; Cooper and Chapman, 1987; Green, 1967; Hampton et al, 1973; Hogarth 1975; Huber, 1974; Ludke et al., 1977; Moore, 1977; Morrison, 1967; Phillips, 1987; Wallsten and Budescu, 1983; Winkler, 1983; Wright and Ayton, 1987), while the empirical studies are (Bunn, 1975; Gustafson et al., 1973; Hull, 1978; Milkovich et al, 1972; Murphy and Winkler, 1971a, 1971b, 1975, 1984; Murphy and Daan, 1984; Murphy et al., 1985; Pratt and Schlaifer, 1985; Press, 1985; Seaver, 1977; Seaver et al., 1978; Smith, 1967; Spetzler and Stael von Holstein, 1975; Stael von Holstein, 1971, 1972; Tversky and Kahneman, 1984; Winkler, 1967a, 1967b, 1968, 1971). The literature review shows that theoretical investigations on the topic of subjective probabilities have currently far outstripped the empirical studies. ChristensenSzalanski and Beach (1984), after reviewing over 3500 abstracts of articles on probabihty judgements and decision making found only 84 (2.4%) empirical studies. This is unfortunate because available guidance for the elicitation of subjective probabilities is not on a par with the theoretical analyses. Nevertheless, proven techniques from other fields are used for the development of the elicitation approach described herein.  Chapter 3. Elicitation of Subjective Probabilities  3.3  53  Definitions and Assumptions  In this thesis, the terms analyst and expert are used throughout. This section will define these terms and state the assumptions that are central to the development of the eh citation approach.  Analyst  Analyst refers to the individual (or group of individuals) within the firm responsible for conducting economic and financial feasibility, scheduling and cost analyses. He is the key person in the elicitation approach because he must elicit from the expert his belief about the uncertainty of variables as subjective probabilities. To achieve this, the analyst must know the problem, concepts in subjective probabilities and be able to build a rapport with the expert.  Expert  Expert refers to individuals both within and external to the firm who provide key input dealing with economic, revenue, cost, financial, productivity and schedule information and is a person who in his area has some degree of training, experience and/or knowledge significantly greater than that in the general population (Wallsten and Budescu, 1983). These experts are drawn from the fields of economics, finance, design, construction and so forth, when the analyst believes that they possess the most relevant knowledge and information regarding the uncertainty of a primary variable. In general, they are substantive experts, who in a given domain, assess events in their field of expertise (Wallsten and Budescu, 1983). Based on the literature reviewed, three assumptions that are central for the elicitation of subjective probabilities are stated.  Chapter 3. Elicitation of Subjective Probabilities  54  Assumption 3.1 : The experts involved with engineering projects are calibrated. Budescu and Wallsten (1987) state that calibration is the most important criterion for an expert because it directly compares his performance with empirical reality, and while experienced experts are highly calibrated, calibration can be further improved with training. The calibration curve as shown infigure(3.1) is a bivariate plot of the proportion of events occurring versus the expert's probability assigned to the events. It is linear with unit slope and zero intercept for a "perfectly" calibrated expert (Murphy and Winkler, 1984). Phillips (1987) states that calibration of assessments are usually better for future events made by experts in a group when training and feedback are available. Past studies show that when experts are required to encode subjective probabilities within their area of competence, they can be exceedingly well calibrated (Wallsten and Budescu, 1983). Assumption 3.2 : Interaction between the analyst and the expert is an essential part of the process. The main reason for the interaction between the analyst and the expert is to avoid serious misunderstandings and biases. Spetzler and Stael von Holstein (1975) state that even subjects who are well trained in probability or statistics, when having to assign a probability distribution without the help of an analyst often provide poor assignments. Past studies show that interaction is useful (Chesley, 1975; Cooper and Chapman, 1987; Huber, 1974; Hull, 1980; Spetzler and Stael von Holstein, 1975) especially when experts lack experience in providing subjective probabilities. However, the interaction hinders the practicality of the framework. Firstly, it makes the implicit assumption of an additional person. Secondly, it discourages selfelicitation. Thirdly, every problem may not justify the time and cost associated with the interaction during the elicitation. Spetzler and Stael von Holstein (1975) state  Chapter 3. Elicitation of Subjective Probabilities  100  0  0.2  0.4  0.6  Assessed Probability  Figure 3.1: Calibration Curve  0.8  1  Chapter 3. Elicitation of Subjective Probabilities  56  that in such situations or when the firm uses probabilities regularly to communicate about uncertainty, interactive computer interviews might be valuable. Some real applications, such as probabilistic weather forecasting, rarely used interaction for the elicitation of subjective probabilities (Murphy and Winkler, 1975). Due to the need to obtain assessments for a large number of variables required for engineeringriskanalysis, it is necessary to standardize and computerize the elicitation approach, to the extent possible. While Spetzler and Stael von Holstein (1975) and Chapman and Cooper (1987) assert that the role of the computer should be minimized in the elicitation process, Wallsten and Budescu (1983) recommend the study of unaided judgements, because of their applied interest, and because only through studying unaided judgements can the benefits of interaction be determined. Those stages of the approach that can be standardized and computerized to increase the efficiency of the process are explicitly identified in this chapter. Assumption 3.3 : Questions based on those from previous non-construction related applications and studies will elicit accurate subjective percentiles for the construction context. The developments in this research are restricted to the measurement of an expert's belief as percentiles of subjective prior probability distributions. In developing the elicitation approach, many proven techniques are used to compensate for the lack of experience in formal elicitation of subjective probabilities in engineering construction. The elicitation of accurate and calibrated subjective probabilities involves three phases - pre-elicitation, elicitation and feedback.  Chapter 3. Elicitation of Subjective Probabilities  3.4  57  Pre-Elicitation Stage  The objective of pre-elicitation is for the analyst to train the expert in the task of quantifying his belief as subjective probabilities. It is done in the three phase approach of motivating, structuring and conditioning developed by Spetzler and Stael von Holstein (1975).  3.4.1  Motivating Phase  The motivating phase has two purposes. Thefirstis to build a rapport with the expert by introducing him to the elicitation task. The second is to explore whether any motivational biases might operate (Spetzler and Stael von Holstein, 1975). Introduce the expert to the elicitation task  The analyst attempts to build a rapport with the expert by giving an explanation on the importance and purpose of probabihty encoding. This is useful in motivating the expert to become fully involved in the eh citation task (Spetzler and Stael von Holstein, 1975). The need for subjective probabilities in engineering construction, because the variables represent predictions of future events, is emphasized. As most engineers prefer to make deterministic predictions (and then add safety factors for the uncertainty), the difference between deterministic and probabilistic prediction is explained. This discussion is helpful when the expert is asked to respond to probabilistic questions during the elicitation stage. Explore whether any motivational biases are operating  Motivational biases are defined as either conscious or subconscious adjustments in expert's responses motivated by his perceived system of personal rewards for various responses (Spetzler and Stael von Holstein, 1975). The analyst points out to the  Chapter 3. Elicitation of Subjective Probabilities  58  expert that there is no commitment (firm projection) inherent in a probability assessment and that the only aim is to elicit a probability distribution that represents belief of the expert about the uncertain variable.  3.4.2  Structuring Phase  The structuring phase concerns the uncertain variable. It also has two purposes. Define the uncertain variable  The uncertain variable is clearly denned in terms of the structure of the problem. The definition includes relevant units for the variable. The importance of the variable to the decision problem is explained to demonstrate the relevance of the elicitation process to gain the expert's full cooperation. Such cooperation is essential for a successful elicitation (Cooper and Chapman, 1987; Huber, 1974; Hull, 1980; Spetzler and Stael von Holstein, 1975). Expert is asked to think the variable through  Having denned the uncertain variable the expert is then asked to think the variable through carefully. This enables the analyst to find out what background information is relevant to the elicitation process. If relevant historical data are available it is used in the discussion. The meanings of any descriptive terms (such as highest and lowest or shortest and longest) used in the questionnaire are explained. Winkler (1967a) observed that when subjects are asked for a shape of their subjective distribution many try to associate it to a normal distribution. The author's experience is similar, some experts believing that percentiles should be symmetric to be consistent. The expert is made aware of this common mistake, so that features like skewness will be considered during the eh citation.  Chapter 3. Elicitation of Subjective Probabilities  3.4.3  59  Conditioning Phase  The aim of this phase is to condition the expert to think fundamentally about his judgements and to avoid cognitive biases. Cognitive biases are defined as either conscious or subconscious adjustments in the expert's responses that are systematically introduced by the way the expert intellectually processes his perceptions (Spetzler and Stael von Holstein, 1975). For example, a response may be biased towards the most recent piece of information simply because the information is the easiest to recall. Spetzler and Stael von Holstein (1975) state that cognitive biases depend on the expert's "modes of judgement". F i n d out h o w the expert makes probability assignments  The analyst tries to discover what "mode of judgement" the expert might be using to make probabihty assessments and then adapts the interview to minimize possible biases. Spetzler and Stael von Holstein (1975) define five "modes of judgement" and how each might operate in producing bias, based on the work by Tversky and Kahneman (1984). 1. Availability: Probabihty is based on the ease with which relevant information is recalled or visualized. This occurs when recent information or information that made a strong impression at the time it was first presented is given more weight than old information. While availability as a mode of judgement can produce biases due to retrievability of instances or imaginability, it can also be introduced deliberately by the analyst to help compensate for an expert's bias. If the analyst believes that the expert has a central bias, he asks the expert to make up scenarios for extreme outcomes, which become more available and help counteract the central bias. 2. Adjustment and Anchoring : The initial response in an interview often serves as a basis for later responses, especially when the first question concerns a .likely value  Chapter 3. Elicitation of Subjective Probabilities  60  for the uncertain variable. Most often experts' adjustment from such a basis is insufficient. Thus, anchoring occurs from a failure to process information about other points on the distribution independently from the point under consideration. 3. Representativeness : The probability of an event is evaluated according to the degree to which it is considered representative of, or similar to, some specific major characteristics of the process from which it originated (i.e probability judgements are reduced to judgements of similarity). When this mode is operating there is a strong tendency to place more confidence in a single piece of information that is considered representative than in a larger body of more generalized information. 4- Unstated Assumptions : Expert's responses are conditional on various unstated assumptions. Since the expert cannot be held responsible for taking into account all possible eventualities that may effect the variable, the analyst states the assumptions he is making about the uncertain variable. Once identified the experts can assign their probabilities. 5. Coherence : People tend to assign probabilities based on the ease with which they can fabricate a plausible scenario that would lead to an outcome. Therefore any discussion of scenarios leading to possible outcomes for an uncertain variable should be well balanced, since the relative coherence of various arguments can have an effect on the probability assignment. Be alert for biases symptomatic of modes of judgements Asking the expert to specify the most important bases for his judgement, and what information he is taking into account in making his estimates, will indicate possible biases symptomatic of the modes. The first often acts as anchor and possibly leads to central bias while the second will indicate what information is easily available. These observations are also used as checks when obtaining responses for subjective estimates.  Chapter 3. Ehcitation of Subjective Probabilities  3.5  61  Elicitation Stage  With the completion of the pre-elicitation stage the expert is ready to quantify subjectively his belief about the uncertain variable. The ehcitation session is based on a questionnaire that would elicit the desired percentiles of the subjective prior probability distribution for each uncertain variable. This section develops the questionnaire and describes how the ehcitation session is conducted. In developing the questionnaire, central bias (Bunn, 1975, 1979; Chesley, 1975; Hampton et al., 1973; Huber, 1974; Hull, 1978, 1980; Seaver et al., 1978; Spetzler and Stael von Holstein, 1975; Tversky and Kahneman, 1984; Wallsten and Budescu, 1983; Winkler, 1967a) and its effect on the ehcitation of tail probabilities (5 and th  95 percentiles) must be treated. Therefore, the questionnaire begins by establishing th  the extremes of the distribution (Budescu and Wallsten, 1987; Cooper and Chapman, 1987; Hull, 1980; Selvidge, 1975; Spetzler and Stael von Holstein, 1975). This prepares the expert to respond to questions on tail probabilities. The deliberate use of scenarios for extreme outcomes counteracts the effect of central bias that is otherwise likely to occur. Also, this has the overall effect of increasing the range of the assigned distribution for the uncertain variable (Hull, 1980). Estimation of the time required to construct afloorslab is used as the example for an uncertain variable to demonstrate a sample questionnaire. Each question is followed with an explanatory comment. Duration assignments for different percentiles are depicted infigure(3.2).  Question 1 : What in your opinion is the shortest possible duration to construct the floor slab for which the probability is so small as to equal zero for practical purposes ? (say the value is vl)  Chapter 3. Elicitation of Subjective Probabilities  62  Comment : The pre-elicitation stage would have clarified the terms used in the question and explained the range of scenarios the experts should consider in their quantification of judgements. Question 2 : So, A is in your opinion the shortest possible duration, is that correct ? Comment : A check to clarify the expert's thinking about the lower tail value of the distribution. Question 3 : If A in your opinion has a zero probability of not exceeding the actual duration, what is the duration which would not exceed a probability of 0.05 ? (Say the value is C ) Comment : Having established the point for zero probability the expert should be able to give a value for the 5 percentile. This value would be anchored to that of th  zero probability. However, the anchoring is the result of forcing the expert to think of extreme outcomes to counteract central bias. Question 4 : So, you associate a 1 in 20 chance that the actual duration will be less than C. Is that correct ? Comment : Here, odds are used to check the consistency of the elicited 5 percentile. th  This is helpful to verify the expert's thinking. If the expert confirms his estimate, go to Question 6, if not, ask Question 5. Question 5 : If not, what is the value for the actual duration that you consider to have a 1 in 20 chance of not being exceeded ? Comment : A follow up question to the consistency check attempted in Question 4. Question 6 : What in your opinion is the longest possible duration to construct the floor slab for which the probability is so large as to be equal to one for practical purposes ? (Say the value is Z)  Chapter 3. Ehcitation of Subjective Probabilities  63  Comment : Going from one extreme to the other increases the range and would reduce even more the possible effects of the central bias that may occur when the 25  th  and 75 percentiles are elicited after the median value. th  Question 7 : So, Z is in your opinion the longest possible duration, is that correct ? Comment : A check to clarify the expert's thinking about the upper value of the distribution. Question 8 : If Z in your opinion has a unit probabihty of not exceeding the actual duration, what is the duration which would not exceed a probabihty of 0.95 ? (Say the value is X) Comment : Same as for Question 3 Question 9 : So, you associate a 1 in 20 chance that the actual duration will be above X. Is that correct ? Comment : Again, odds are used to check the consistency of the elicited 95" per1  centile. If the expert confirms his estimate, go to Question 11, if not, ask Question 10. Question 10 : If not, what is the value for the actual duration that you consider to have a 1 in 20 chance of being exceeded ? Comment : A follow up question to 9. Question 11 : What in your opinion is the value for actual duration such that it is equally likely to be above as it is to be below ? (Say the value is M) Comment : This question would elicit the median value of the expert's subjective probabihty distribution for duration to construct a floor slab. Question 12 : So, you are willing to bet equal odds that the actual duration is either  Chapter 3. Elicitation of Subjective Probabilities  64  above or below M , is that correct ? Comment : A check to clarify the expert's response to the median. What is the value for duration that you feel will divide the region  Question 13 :  below M equally, thus it is just as likely that duration will fall below this value as it will be between this value and M ? (Say the value is L) Comment : The expert is asked to bisect the area below the median to give an estimate for his 25" percentile value. 1  So, you associate a 1 in 4 chance that the actual duration will be  Question 14 :  below L, is that correct ? Comment : A consistency check to clarify that the expert is thinking about the 25  th  percentile with the bisected value. If the expert confirms his estimate, go to Question 16, if not ask Question 15. If not, what is the value for the actual duration that you consider to  Question 15 :  have a 1 in 4 chance of not being exceeded ? Comment : A follow up question to 14. Now, concentrate on the case where the duration could be above M,  Question 16 :  which you felt would be 50% of the time. What is the value that you feel will divide the region above M equally, thus it is just as likely that duration will be above this value as it will be between this value and M ? (Say the value is N) Comment : The expert is asked to bisect the area above the median to give, an estimate for his 75 percentile value. In addition the expert is reminded of his estimate th  for the median. This gives him a further opportunity to change or confirm his estimate for the median, now that he has given an estimate for the 25 percentile. th  Question 17 :  So, you associate a 1 in 4 chance that the actual duration will be  Chapter 3. Elicitation of Subjective Probabilities  65  above N, is that correct ? Comment : A check to clarify that the expert is thinking about the 75 percentile th  with the bisected value. If the expert confirms his estimate, stop the interview, if not ask Question 18. Question 18 : If not, what is the value for the actual duration that you consider to have a 1 in 4 chance of being exceeded ? Comment : A follow up question to 17. The questionnaire combines direct probability responses and chance responses to provide cross checking for consistency. The basis for direct probability responses is the variable interval method (Huber, 1974), because it elicits the percentiles required by the framework. Hull (1978) and Seaver et al. (1978) have reported that the fixed interval method performed better than the variable interval method because the variable interval method gave distributions that were "too-tight". It must be noted that both studies assessed the median first, giving rise to possible central bias. Murphy and Winkler (1975) studying experienced weather forecasters conclude that the variable interval method performed better than the fixed interval method in probabilistic weather forecasting.  Since the questionnaire starts with the tails of the distribu-  tion, the elicited percentiles should overcome the effects of central bias and display sufficient spread. The elicitation session is based on the questionnaire and the analyst is expected to follow the general format of the questionnaire when conducting the session. However, at his discretion the analyst can adapt the interview to suit different situations. Since the questionnaire is based primarily on the variable interval technique, it is easily standardized and automated to use for those variables selected for interactive computer interviews.  Chapter 3. Elicitation of Subjective  Probabilities  Figure 3.2: Subjective Percentile Estimates  Chapter 3. Ehcitation of Subjective Probabilities  3.6  67  Feedback and Consensus Estimates  Whenever possible a group of experts are used for the ehcitation because it has been found that consensus judgements from a group to be better than the individual judgements (Ashton and Ashton, 1985; Ashton, 1986; Bacharach, 1975; Beach, 1975; Bordley, 1982; Bordley and Wolff, 1981; Cooper and Chapman, 1987; French, 1985; Hampton et al., 1973; Huber, 1974; Stael von Holstein, 1971; Winkler, 1968, 1971). Huber (1974) states that the aggregation of responses from several experts improve subjective judgements because aggregation from a statistical viewpoint tends to reduce random error as well as reduce the impact of biases. This view is confirmed by Beach (1975) who in addition states that combining the opinions of several experts would aid in eliminating conservatism and/or extremism and promote more nearly optimal decisions. Once the initial subjective estimates'are made by the expert, he is provided feedback on his assessments in the form of a discussion (graphically if necessary) between the analyst and expert, and expert and expert. The expert can revise his prior judgements after the discussion. This process is based on the nominal group technique (Delbecq et al., 1975). While there is no consensus in the literature as to which is the best method to provide feedback, there is agreement that feedback improves the original estimates (Beach, 1975; Chesley, 1975; Gustafson et al., 1973; Lock, 1987; Stael von Holstein, 1971; Winkler, 1971). However, Gustafson et al. (1973) have shown that assessments from the nominal group technique (estimate-talk-estimate) to be more accurate than those from Delphi technique (estimate-feedback-estimate), conventional group technique or individual estimates. Lock (1987) in proposing a general approach to group judgmental forecasting concludes that there are benefits to communication and discussion between group members, so long as these are structured as in nominal group approaches.  Chapter 3. Elicitation of Subjective Probabilities  68  Winkler (1968) used several mathematical and behavioral approaches for arriving at consensus subjective probability distributions. The mathematical approaches were either using a weighted average or Bayes' theorem. The behavioral approaches, Delphi and nominal group led the group to arrive at the final probability distribution. He could not determine which method was most accurate, because there was no "correct" opinion, but he did find that different methods produced different results. Makridakis and Winkler (1983) used ten different forecasting methods to combine forecasts. Performance was compared in terms of the mean average percentage error (MAPE). They found that the accuracy increased when additional methods were added to the forecast. However, the gains tailed off after about four or five were combined. Ashton and Ashton (1985) compared equal weighting with four differential weighting methods to examine the impact of aggregation in forecasting of annual advertising sales at TIME magazine. They found: aggregates of subjective forecasts to be more accurate than the individual forecasts that comprised the aggregates; incremental accuracy of differential weighting methods over equal weighting was small; regardless of the weighting method, accuracy attributable to aggregation was achieved by combining a small number of individual forecasts. They concluded that equal weighting appears to be the solution to the problem of choosing a weighting method for subjective forecasting. Lock (1987) states that for most purposes linear models are adequate for aggregation and differential weighting do not offer any real advantages in practical terms. Since the experts are provided feedback, their subjective estimates would be close to consensus. Also, because of the difficulty in measuring the variability in expert accuracy, consensus subjective estimates are obtained by assigning equal weights to all the experts. The routine aspects of feedback such as exchanging individual estimates,  Chapter 3. Elicitation of Subjective Probabilities  69  obtaining revised estimates and the task of combining subjectives estimates can be readily automated.  3.7  Analysis Stage  The requirement for eliciting coherent subjective probabilities has been discussed previously. An automated approach to ensure the coherence of subjective probabilities has been developed as a part of this research effort. It is documented in the form of an interactive computer program called "ELICIT" (see Appendix D). This program, based on the method to convert subjective estimates to moments (section 2.3.2), enables the analyst to approximate the subjective estimates for a variable to a Pearson type distribution. The high flexibility of the Pearson family (Amos and Daniel, 1971) approximates most of the subjective estimates to Pearson type distributions. However, in some instances subjective estimates may not approximate to a Pearson distribution for the specified maximum cumulative error. In these situations the expert is made aware of the necessity for subjective probabilities to be coherent and is asked to modify the 25 and 75 percentile estimates. These two estimates are th  th  ehcited only to approximate a Pearson type distribution. The expected value and standard deviation for the uncertain variable are derived from the 5 , 50 and 95 th  th  th  percentile values and are initially independent of the approximated distribution. In addition, the conversion of subjective estimates to moments ensures that the measured belief is useful in the quantification of uncertainty of the derived variable. For example, assume that the five percentile values ehcited for a variable are 1.0, 2.5, 5.0, 7.5 and 9.0. The expected value and standard deviation from step 2 of section (2.3.2) is 5.0 and 2.432. However, the five subjective estimates do not approximate to a Pearson type distribution. It is obvious from the 5 and 95 estimates that the expert th  th  Chapter 3. Elicitation of Subjective Probabilities  70  is thinking of a symmetric distribution. If a value for the 25 percentile between 2.9 th  and 3.5 with a symmetrical value for the 75" percentile between 6.5 and 7.1 is accept1  able to the expert, a Pearson distribution with E[X] = 5.0, cr = 2.432, y/% = 0 and /3 between 2.0 and 4.8 can be approximated. In the author's limited experience 2  in eliciting subjective estimates, the consensus among experts is that if necessary they would be willing to change within reason the 25' and 75 percentile estimates h  th  because those two are the least important to them. Ideally, the interactive program should give guidance for the change as in the case of correlation coefficients for a positive definite correlation matrix. However, at present it is the responsibility of the analyst to guide the expert. The analyst can recognize the shape of the distribution (symmetric, positively or negatively skewed) by observing the 5 , 50 and 95 percentile estimates and guide the expert to th  th  th  acceptable estimates for the 25 and 75 percentiles. It is planned that this facility th  th  be added to "ELICIT" as a future improvement.  3.8  Verification  As the final stage of the elicitation, the subjective prior probability distribution is verified to see if the expert is in total agreement with it (i.e it reflects his belief). Cooper and Chapman (1987) state that verification can be conducted by: using cross checking for consistency between values; using different elicitation methods especially when indirect methods have been used; and having the expert examine and confirm thefinalresult. Since the questionnaire has performed cross checking for consistency of all the percentile values, as verification, the computer program "ELICIT" informs the user of the expected value, standard deviation and shape characteristics of the approximated  71  Chapter 3. Ehcitation of Subjective ProbabUities  Pearson type distribution. While the skewness and the kurtosis give an indication of the shape of the distribution, a better verification method is to provide a graphical display of the approximated density function. The next step in the development of "ELICIT" is to display the probabihty density function of the approximated Pearson type to be viewed by the expert. Incorporating such a verification process to the ehcitation technique would provide the analyst and expert with greater confidence that the approximated distribution represents the expert's belief.  In addition, it  would eliminate approximating variables with bell shaped probabihty distributions to Pearson distributions that are U or J shaped.  3.9  Summary  An approach to elicit an expert's belief of uncertainty as subjective probabilities has been described in this chapter. The approach combines the theoretical requirements of subjective probabilities with a practical process.  The process is developed by  transforming proven techniques from other fields of study to the requirements of risk quantification in engineering construction. The role of the computer and the use of a standard approach to expedite the process is identified at every stage. The pre-elicitation stage based on the developments by Spetzler and Stael von Holstein (1975) trains and prepares the expert to quantify his belief as subjective probabilities. This stage requires a high level of person to person interaction. Hence, there is little use of the computer during pre-elicitation. The ehcitation stage elicits the percentile values of an expert's subjective prior probabihty distributions for uncertain variables using the developed questionnaire. These subjective probabilities are accurate and calibrated. Since the questionnaire is based primarily on the variable  Chapter 3. Ehcitation of Subjective Probabilities  72  interval technique, the ehcitation stage can be standardized and automated for interactive computer interviews. If more than one expert participates in the ehcitation, consensus subjective estimates are obtained by assigning equal weights to all the experts. The routine aspects of feedback and obtaining consensus subjective estimates can be done by the computer. The coherence of subjective probabilities is ensured by the interactive computer program "ELICIT" (see Appendix D) , using the Pearson family of distributions. The moments for the uncertain primary variable are used to verify whether the shape of the approximated distribution is similar to that which the expert has in mind. Distinct roles for computerization and standardization exist to expedite the process of ehcitation and verification of an expert's belief about uncertainty. At present, the experience in using these approaches in field applications is limited. The next stage of this research will concentrate on building up experience from field applications and on refining and validating the ehcitation approach. This is essential for the proposed method to become a practical tool in risk quantification for large engineering projects.  Chapter 4 Correlations Between Variables  4.1  General  The risk measurement framework developed in chapter two was based on the assumption that the correlations between variables were linear. From that assumption a variable transformation method was developed to treat linear correlations among the primary variables when evaluating the moments of the derived variable. This transformation was based on the correlation matrix for the primary variables. A correlation matrix is denned by Graybill (1983) as follows. Let X be an raxl random vector with positive definite covariance matrix denoted by C =  [vij].  The  correlation matrix of X is R = [pij] where pij is defined by,  Pii  =-7^=  (4-1)  for all i and j. This chapter addresses some of the issues that arise in treating analytically the linear correlations between variables (these are equally relevant when treating correlations for Monte Carlo simulation). In the next section the correlations between primary variables are discussed. The discussion highlights an often ignored theoretical requirement of the correlation matrix and thereby develops a method to elicit a positive definite correlation matrix for primary variables.  73  Chapter 4. Correlations Between Variables  74  Developed in the third section is a method to obtain a positive definite correlation matrix for derived variables. The method is developed by extending the approximation for the covariance between two functions suggested by Kendall and Stuart (1969) to the multivariate case. The fourth section address the issue of multicoUinearity in the correlation matrix and suggests a mathematical manipulation to overcome the effect of multicollinearity for practical applications. A numerical study is presented in the fifth section. Thefirstpart of the study compares the variable transformation method to the standard approach used in moment analysis to treat correlation among primary variables (Ang and Tang, 1975; Benjamin and Cornell, 1970) under general conditions. The second part explores the behavior of the two methods in the presence of multicoUinearity. This study demonstrates that while the variable transformation method is stable in the presence of multicoUinearity, the standard approach could fail. The intention of the third part is to study the susceptibility of the transformation to the effect of multicoUinearity.  4.2  Correlation between Primary Variables  The correlation information between primary variables, required by the framework, wiU have to be obtained subjectively from experts because of data limitations. A number of authors in both simulation and approximate applications have recognized this necessity (Eilon and Fowkes, 1973; Inyang, 1983; Howard, 1971; HuU, 1977, 1980; Kadane et al., 1980; Keefer and Bodily, 1983; Kryzanowski et al., 1972; Wagle 1967). Other than for Kadane et al., (1980), who develop an approach to elicit a positive definite correlation matrix, aU of the others obtain only the correlation coefficients between variables.  Chapter 4. Correlations Between Variables  4.2.1  75  Positive Definite Correlation Matrix  A positive definite correlation matrix ensures theoretical consistency of a system. A correlation matrix is positive definite if there are no linear dependencies among the primary variables. If an elicited correlation matrix is not positive definite, then it has to be positive semi-definite because the variance of a vector of random variables is always greater than or equal to zero.  Proof that a Correlation Matrix is Positive Definite Let X be the vector of n random variables with covariance matrix C  x  and  correlation matrix R. Let a be a vector of n scalars. From definition, Var [a X] > 0  (4.2)  T  a  Therefore, covariance matrix C  C a > 0  T  (4.3)  x  is always positive definite (i.e > 0) or positive  x  semi definite (i.e = 0). Rewriting equation (4.3) as, a  Let  b — (X — X )  T  C a  T  =  x  a  T  (X - X) (X - X )  (4.4)  a  T  a, where fc is a number and b = b . When 6 = 0 (positive T  semi definite condition), (X - X ) a = 0  (4.5)  T  (Xi-X^d!  variable X  n  + (X -X )a 2  2  2  +  + (X -X )a n  n  n  =  0  (4.6)  is a linear combination of the others.  If there are no linear dependencies (combinations) then the covariance matrix is always positive definite.  For the correlation matrix, starting from the relationship R = D where D  _ 1  _ 1  C D x  _ 1  ,  is the inverse of the diagonal matrix of standard deviations of the X  Chapter 4.  Correlations Between Variables  76  vector, it follows that,  a  T  R a = a  D" C  T  1  D" a  (4.7)  1  X  T Since  D  =  _ 1  fl} ]  because D  -1  a where b = D Since D  _ 1  1  T  [D- ] 1  C  1  is symmetric,  _ 1  D- a = b 1  x  C  T  X  b  (4.8)  a.  is non-singular and symmetric, when C  b  T  C  X  X  is positive definite,  b > 0  (4.9)  If the covariance matrix is positive definite then the correlation matrix is always positive definite. A correlation matrix could be positive semi-definite even when all the variables are not perfectly correlated. For example, consider the correlation matrix for a three variable system given by Ro,  Ro =  "1.0  0.5  0.5  1.0  .0.5  -0.5  0.5 " -0.5  (4.10)  1.0 .  At first glance, the correlation coefficients between the variables seem reasonable. However, the determinant of matrix Ro is equal to zero (i.e positive semi-definite). A further investigation shows that a linear combination of variables 2 and 3 is perfectly correlated with variable 1 (see Appendix B).  4.2.2  Elicitation of a Correlation Matrix  The proposed method of ehcitation is a combination of a two stage process. The first is the ehcitation of the linear correlation coefficients between the primary variables, while the second is ensuring the positive definiteness of the correlation matrix.  77  Chapter 4. Correlations Between Variables  Linear Correlation Coefficients The hnear correlation coefficient between two primary variables Xi and Xj can be approximated from the conditional expected value of Xj\X{ = Q. The conditional expected value of Xj\X{ = Q from Bury (1975) is,  E[Xj\Xi = Q] = E[Xj] + p^ %-(Q- E[Xi})  (4.11)  Hence,  (E[Xj\Xj = Q} - E[Xj})cTi (Q - E[Xi]) o-j  Pij =  where -E[X;] and  (4.12)  are the expected values and o~i and <Tj are the standard  deviations for Xi and XJ; Q is the conditional value for Xi] and pij is the hnear correlation coefficient between primary variables Xi and Xj. Then, similarly to the method suggested by Hull (1977) for risk simulation, the correlation coefficient between Xi four values for pij.  and Xj  is approximated by averaging three or  The values for p^ are evaluated from equation (4.12). The  conditional expected value of Xj, E\Xj\Xi = Q], is ehcited by asking the question "What is the expected value for Xj, when Xj = Q ?"  from the experts.  Different percentile values of X - can be used for the conditional value Q. t  T h e Elicitation Procedure Let R  n  be a subjectively ehcited nxn correlation matrix partitioned as,  Rn =  •n-1  b  b  1  T  where R - i is a (n — l)x(n — 1) correlation matrix for n = 2,3,.... and n  b  T  = [pin p2n  Pn-ln]-  (4.13)  Chapter 4. Correlations Between Variables  Then R  n  78  is positive definite i f R - i is positive definite and, n  b  T  R ^  b < 1  (4.14)  (Kadane et al., 1980; for proof see A p p e n d i x C )  First, the primary variables i n the function for the derived variable are ordered according to the expert's confidence i n them and their relationship with the other variables. T h e variable that is selected as the "best" is numbered one and the "worst" numbered n (when there are n variables i n the functional form). Then, pi is elicited 2  as suggested i n the previous section. This value is assumed to be consistent with the expert's belief because the 2x2 matrix is always positive definite. Thereafter,  p  i3  and p  23  are elicited.  If the condition given by equation  (4.14)  is satisfied, the correlation values are accepted because the 3x3 matrix is positive definite. If the condition is violated, the expert is made aware of the inconsistency and given the option to change one of the correlation coefficient values i n the b vector. W h e n a value is selected (say p ) the expert is informed of the real bounds for p 23  23  in  which the 3x3 matrix will be positive definite. T h e bounds, r \ and r , (if they exist 2  - see figure 4.1) are the real roots of the quadratic equation (see A p p e n d i x C for the derivation),  j-1  + [Cu  + £ i=\  (C  u  +C  2j  +  + C) B 2i  u  n-1  £ % B + i =Y. * *} t=l j+l S  B  (4.15)  R  u  + £ (C-, i=j+l  -  1 <  0  Chapter 4. Correlations Between Variables  79  Si  where R * n  x  =  ; r  s  2  = B ^ S i ; and C  Ci  2  = Bj S . 2  T is the correlation coefficient (pj ) for which bounds are required (for p 3, j = 2 2  n  and n — 3), S i is a (j — l)x(ra — 1) matrix and S is a (n — 1 — j)x(n — 1) matrix, 2  B ^ and B ^ are lx(_7 — 1) and lx(n — 1 — j) row matrices, and Sj, C i and C are lx(ra — 1) row matrices. 2  This procedure, of introducing the next ordered variable, eliciting correlation coefficients between that and the previous variables and ensuring that the correlation matrix is positive definite is continued until the R is positive definite. n  Once accepted, the elicitation procedure does not permit the positive definite R - i to be changed. If at any stage the expert refuses to change a value from the n  b vector when R  n  is not positive definite, he is implying that the function for the  derived variable is not consistent with his belief and it should be changed by removing one or more of the already used ordered variables from the function.  4.3  Correlation between Derived Variables  Assumption 4.1 : Correlation between two derived variables arise only from common (shared) variables in their functional forms. The common (shared) primary variables are defined as those of the same type having  Chapter 4.  I  Correlations Between Variables  / / / / / / / /  / /'IT-  Infeasible  I  I  +1 Feasible  / / / / / / / / / / / / / / / / / / / /  !  ,  Infeasible  / / / / / / / / / / / / / / / / / / / / / / / /  ii  1  'Xy / / / / / / / / / / / / /  Feasible  Infeasible  !?.  +1  /'*~  Infeasible  Feasible  r Feasible  —  ^  Infeasible Point  Y,  P , n  I  +1 Feasible  Figure 4.1: Feasible Regions for V for R to be Positive Definite. n  Chapter 4.  Correlations Between Variables  81  the same first four moments in the functional forms for two or more derived variables (see figure 4.2). Hence, correlation between two derived variables arise only from those primary variables that are quantified for the functions.  Correlation arising due  to unquantifiable variables in construction such as management, methods, or weather are ignored by assumption (4.1). The correlation coefficient between two derived variables Y and Yb can be evalua  ated from,  cov(Y ,Y ) a  pab = I  b  =====  I- ) 4  16  Jp (Y ) u (Y ) 2  where  cov(Y ,Yb) is a  a  2  Y  the covariance between  variance of Y and Yb] and p a  ab  a  b  and Yj,;  ^2(^1)  is the correlation coefficient  and  p (Yb) 2  are the  between Y and Yb- T h e a  covariance between two derived variables can be approximated from the approximation given by Kendall and Stuart (1969), using only the hnear correlation information between primary variables. T h e approximation is,  cov{Y.,Y„)  «  « > » ( * , X,-) +  ,  where  Y = g (X.) a  a  has  m random  i^^  -  mix>iXi)  f  V*  variables;  d  9  a  Yb  d  =  9  b  (4 i7) tY  ^b(X) has  Y\  n random  variables;  I is the number of common (shared) primary variables in the functions # (X) and a  <7b(X), (i.e X X , 1}  2  ....,Xi)]  and  cov(Xi,Xj)  is the covariance between two primary  variables X - and Xj. First order Taylor series expansions of the functions for derived t  variables are used in equations  (4.16) and (4.17). For a vector of derived variables  Y in a system, the correlation matrix must also be positive  definite.  Chapter 4. Correlations Between Variables  Common (Shared) Primary Variables  .Y.Y.Y.T.V.Y/.Y.Y.Y.Y/.Y.Y.Y.Y.Y.I-.  1  >  ' YXYXY//>XY/!YX-X\YXY'  .Y.Y.Y.|.Y.Y.Y. Y.Y.Y.J.Y.Y.Y.-  i  I I I  i t  •  i i t i ' . . . 1 .  ,  . . . . ..  V.Y/.V/AV.Y/.JV.V/.VJ —  1  t  j  The required correlation information for the system  [  I The available correlation information for the system  Figure 4.2: Correlation from Common (Shared) Primary Variables  Chapter 4. Correlations Between Variables  83  Proof that Correlation Matrix for Y is Positive Definite Let  Y be a vector of derived variables where,  Y = g (X),  Yi = <?i(X),  a  Y = [Y" ....Y Yj ....Y ] and T  1  Y = g (X), b  a  a  )  r  Y = g {X). Let C be the z  b  z  y  covariance matrix, R be the correlation matrix and D be the diagonal matrix of y  y  standard deviations of the vector Y . The covariance matrix of vector Y is positive definite if there are no hnear dependencies among the derived variables. The hnear dependencies can occur if the functional form for two or more derived variables are identical and all the primary variables are shared. However, since the models are not perfect and unquantifiable variables exist in all systems, the true models are,  Y{ = ( X ) + e , Y ; 5 l  = g (X) + e , Y  x  a  a  = g (X) + e ,.., Y* = g (X) + e .  b  b  fc  z  z  where e is a vector of independent error variables to represent the unquantifiable variables in the systems.  For simplicity assume all error variables have the same  variance c r . Then, 2  Y*  = Y -f e  (4.18)  Let a be a vector of scalars. From the definition for variance, Var [a Y*] > 0  (4.19)  T  a Since E[e]  = 0,  T  (Y* -  (Y* a  Y") = (Y -  (Y -  T  Y*) (Y* -  Y*) a > 0  Y + e).  Y + e) (Y -  (4.20)  T  Then,  Y + e)  T  a > 0  (4.21)  Since error variables are statistically independent, from mathematical induction, (Y  -  Y + e) (Y -  Y + e)  T  = (Y -  Y ) (Y -  Y)  T  + a I 2  (4.22)  Substituting (4.22) in (4.21), a  T  (Y  -  Y ) (Y -  Y)  T  +  cr  2  I a > 0  (4.23)  Chapter 4. Correlations Between Variables  84  Expanding equation (4.23),  a  T  (Y - Y) (Y - Y ) a + a a I a > 0 T  T  2  (4.24)  For the covariance matrix to be positive semi-definite both terms in (4.24) have to be equal to zero at the same time. But, a a I a > 0. Therefore, T  a  2  (Y" - Y*) (Y* - Y") a > 0  T  (4.25)  T  Since no linear dependencies exist in the vector of derived variables, a  T  (Y - Y) (Y - Y ) a > 0  (4.26)  T  Hence, a  T  C a >0  (4.27)  y  The covariance matrix for derived variables is always positive definite. In a correlation matrix the following requirements must hold: (Graybill, 1983) (1) pa = 1 ;  i = 1, ...,a,b, ...,z ;  (2) -1  <  P  i  j  < 1;  for all i ^ j  Since only the first order Taylor series expansions of the vector Y are used to evaluate correlation and covariance, the first requirement is obtained by evaluating the i  th  diagonal element of D" C D 1  y  ment is obtained by setting the i  th  -fl, the j  th  b  T  - 1  (=  R-y)- The second require-  element of the vector of scalars b equal to  element equal to +1, and the other elements equal to zero. From  R b > 0, pa + ij + pji + pjj > 0 or pij > —1. Similarly, by y  changing the j"  P  1  element of b to —1, pa —  — pji + jj > 0 or P  < 1.  Since the covariance matrix is positive definite and the requirements hold, the correlation matrix for derived variables is always positive definite.  Chapter 4. Correlations Between Variables  4.4  85  MulticoUinearity  For a successful transformation, the ehcited correlation matrix, in addition to being positive definite, should also be stable because of matrix inversion. The instabihty can occur when the determinant of the correlation matrix is close to zero. This problem is called multicoUinearity . The term multicoUinearity defines itself, multi implying many and collinear implying hnear dependencies (Myers, 1986). MulticoUinearity occurs when there are near hnear dependencies among the columns of a correlation matrix. That is, there is a vector of constants c (not all zero) for which, n  Y  c Xj =t 0 T  (4.28)  where Xj are the columns of the n x n correlation matrix. If the right hand side of equation (4.28) is identicaUy zero then the correlation matrix is positive semi-definite. Thus, the Hnear dependencies are exact and the inverse of the correlation matrix and hence L  _ 1  does not exist.  Myers (1986) states that if multicoUinearity is present, then there exists at least one A,- = 0, where A^ are the eigenvalues of the correlation matrix. While the nearness to zero of the smaUest eigenvalue is a measure of the strength of a hnear dependency, the ratio, 4> = ^ A  (4.29)  min  which is caUed the condition number of the correlation matrix is the true measure of multicoUinearity. As a rule of thumb, a correlation matrix with <f> < 100 is considered to be stable. However, when <b exceeds 1000 then one should be concerned about the effect of multicoUinearity (z.e instabihty in the correlation matrix) (Myers, 1986).  Chapter 4. Correlations Between Variables  86  The instability in the correlation matrix can hamper a successful transformation. It is suggested that for practical applications the concept called the "k value" be utilized. The k value is used in ridge regression as a mathematical manipulation to stabilize an unstable correlation matrix (Myers, 1986). The stability is achieved by replacing the correlation matrix R  by  (R +  k I), where k is a small  positive quantity. Similarly, an elicited unstable correlation matrix can be stabilized by introducing a k I matrix to the correlation matrix. The k value would be the smallest value that would make the correlation matrix stable. Stability is denned in terms of the desired stabilizing condition number  given  by, +-  =  \  m a X  1 \  ( - °) 4  3  Therefore, >  k =  A  m a i  (f> A j  ^ _  B  m  n  (4.31)  l  would stabilize the correlation matrix to the desired condition number <6„. An upper bound on the stabilizing k value can be established in terms of the number of the variables in the functional form (n) and the desired stabilizing condition number (</>„), from the fact that the largest eigenvalue of a correlation matrix is always less than n (Graybill, 1983). Hence, h is always less than  n  For example,  if a condition number <p = 100 is desired (the empirical limit at which regression t  analysis considers a correlation matrix to be stable) the k value for the function described by equation (4.33) is less than 0.030303. Therefore, the k value that stabilizes a correlation matrix to a desired c6„ can be bound as,  Chapter 4. Correlations Between Variables  4.5  87  Numerical Study  T h e first p a r t o f t h e n u m e r i c a l s t u d y c o m p a r e s t h e v a r i a b l e t r a n s f o r m a t i o n m e t h o d to t h e s t a n d a r d a p p r o a c h used i n m o m e n t analysis u n d e r general c o n d i t i o n s (i.e correlation  matrices).  T h e s e c o n d p a r t explores  i n t h e presence o f multicoUinearity. susceptibility  stable  the behavior of the two methods  T h e i n t e n t i o n o f t h e t h i r d p a r t is t o s t u d y t h e  o f t h e t r a n s f o r m a t i o n to t h e effect o f m u l t i c o U i n e a r i t y .  T h e d u r a t i o n o f a w o r k package i n a c o n s t r u c t i o n project v a r i a b l e for t h e study.  T h e d u r a t i o n o f a w o r k package  (T)  is u s e d as t h e d e r i v e d can be evaluated  from  the s i m p l e r e l a t i o n s h i p given by,  T  where Q is t h e q u a n t i t y  =  PTL  =  ^  (  X  )  ( 4  -  3 3 )  d e s c r i p t o r , PL is t h e l a b o u r p r o d u c t i v i t y rate a n d L is t h e  l a b o u r usage, t h e p r i m a r y variables o f t h e work package d u r a t i o n m o d e l .  4.5.1  Variable Transformation Method  F o r t h e w o r k package d u r a t i o n m o d e l d e s c r i b e d b y e q u a t i o n (4.33),  X  T  =  [Q P  L  L] =  [Xy X  2  and  X) 3  D  =  diagto].  If t h e c o r r e l a t i o n m a t r i x for t h e w o r k package d u r a t i o n m o d e l is R,  R =  1-0  pn  P\2  1-0  -Pl3  P23  where  prs'  P23 1-0.  (4.34)  Chapter 4. Correlations Between Variables  88  then the lower triangular matrix obtained from the Cholesky decomposition of the correlation matrix (R = L L ) is, T  0.0  'in  0.0" 0.0  L -  -Ll3 where r  '23  Ln  =  1.0  P23 — Pl2 Pl3  L\2  and  L23  P12 ;  — L33  £33-  L13  P13 ;  Pl2  -^22  2 23  T  2 — y^l — ~L  n  13  P12  (4.35)  The transformation for the uncorrelated variables is Z = L  1  D  1  X and for the  functional form is X = D L Z. Hence, the first four moments of the work package duration are approximated from equations (2.36) to (2.39). The expected value of the work package duration is,  E[T] * G(Z) + \ £  g  p (Z ) 2  i  (4.36)  the second central moment is,  OG  E i=l »  P2(Zi)  dZi dG d G  E t=i  2  dG dZ},  (4.37)  2  p (Z ) - [piiZi)}' 4  {  Chapter 4. Correlations Between Variables  89  the third central moment is,  E i=l L  dG dZi  z  dG  3 + 2 E 3  2  dZi  t=i  (4.38)  M i) dG dZf 2  p {Z ) A  {  -> (^)f 2  and the fourth central moment is,  p {T) A  where  Z Z2,Z 1}  3  »  Y,  i=l  dG dZi  P4(Zi)  are the transformed uncorrelated variables of Q,PT,,L  (4.39)  and G(Z) is  the transformed function for work package duration.  4.5.2  The Standard Approach  The first four moments of the work package duration from the standard approach are derived by expanding equations (2.20) to (2.23) (see Appendix A for the general derivation). The boxed terms are those due to the linear correlations between primary variables. Then the expected value of the work package duration is,  m  -  &  +  \ ii=l m >»<*> cov(Xi,Xj)  (4.40)  Chapter 4. Correlations Between Variables  90  the second central moment is,  dg_  E  dXi  ^3  dg d g  i=i  2  fr[  dxi  dXi  dg 2  3  3  -EE  i=i j=i+i  (4.41)  [ 1  2  [ccn^X^Xj)]  dXidX^  2  the third central moment is,  E  A*a(T)  »=i 3 *  + 2 z  - 6  ^{Xi)  0X;  E  i=l L dXi  SJ  + 1  <9X,  2  ^^^;  \n (Zi) 4  -  [ C 0 t ; ( X i  [u {Zi)X 2  '  (4.42)  A i ) ]  and the fourth central moment is,  u (T) 4  *  2 i=i  where X i is Q, X  2  is Pj,  a n  d X 3 is L .  <9X;  Pi(Xi)  (4.43)  Chapter 4. Correlations Between Variables  91  Table 4.1: Quantity Descriptors (Q) (ft ) 3  W.P 01 02 03 04 05 06 07 08 09 10  4.5.3  E[Q] 38397.3 60555.0 76850.0 16185.0 32429.2 38397.3 21998.0 76850.0 20413.0 76850.0  12186.1 8829.3 24440.5 3527.4 7030.8 12186.1 2621.4 24440.5 5782.4 24440.5  0.5 0.9 0.5 0.8 0.8 0.5 0.2 0.5 0.7 0.5  ft  3.3 9.0 3.2 7.8 7.8 3.3 2.4 3.2 8.5 3.2  The Comparison  The moment analyses for both approaches consider terms up to the fourth order. While both methods treat the same correlations, the variable transformation method simplifies the approximations by the transformation (see the boxed terms in equations 4.40, 4.41 and 4.42). The two approaches are compared for ten hypothetical work package durations. The values for the primary variables and correlation coefficients used for the numerical study are given in Tables 4.1 to 4.4. Table 4.5 shows the moments for work package durations evaluated from the two approaches when the correlation matrices are stable. The time unit is in years. When the primary variables are assumed to be uncorrelated, both methods give identical moments indicating that they are comparable (see Table 4.5). When there is correlation between the primary variables the expected values from both methods are identical. The second and third central moments from the variable transformation method are larger for all work packages. The fourth central moments from the standard approach are same as when uncorrelated or highly correlated (see Table 4.6) because there are no covariance terms in equation (4.43). If the moment analysis  Chapter 4. Correlations Between Variables  92  Table 4.2: Labour Productivity Rates, PT,; (ft /m.d) 3  W.P  E[PL]  01 02 03 04 05 06 07 08 09 10  9.0 9.0 9.0 10.1 8.4 9.0 10.1 9.0 9.9 10.2  ft  1.25 1.25 1.25 2.28 1.28 1.25 2.28 1.25 2.22 2.23  0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.0 0.9 0.8  5.6 5.6 5.6 2.2 8.8 5.6 2.2 5.6 9.0 8.0  Table 4.3: Labour Usage, L; (m.d/year) W.P 01 02 03 04 05 06 07 08 09 10  E[L] 6833.2 15185.0 15185.0 6074.0 7777.5 9055.5 6074.0 15092.5 3850.8 15092.5  ft  O-L  692.7 1539.5 1539.5 615.8 2339.8 832.9 615.8 1388.1 393.4 1388.1  0.4 0.4 0.4 0.4 1.1 0.4 0.4 0.4 0.4 0.4  2.4 2.3 2.3 2.4 5.7 4.3 2.4 4.3 2.3 4.3  Chapter  4.  Correlations  Between  Variables  93  Table 4.4: Condition Number (<f>) and Correlation Coefficients W.P 01 02 03 04 05 06 07 08 09 10  <t> PQPL  6.77 9.05 8.60 6.77 8.60 8.22 6.77 11.4 7.94 8.60  -0.48 -0.55 -0.53 -0.48 -0.53 -0.48 -0.48 -0.53 -0.48 -0.53  PP L  POL  L  0.42 0.62 0.56 0.42 0.56 0.62 0.42 0.56 0.62 0.56  -0.69 -0.74 -0.74 -0.69 -0.74 -0.70 -0.69 -0.80 -0.69 -0.74  considered only the terms up to the third order (Bury, 1975; Siddall, 1972), there will be no covariance term in equation (4.42). Then the third central moments from the standard approach will also be same as when uncorrelated or highly correlated.  4.5.4  Transformation under MulticoUinearity  Two numerical studies are done to demonstrate the behavior of the transformation in the presence of multicoUinearity. The first, compares the variable transformation and the standard method using the same correlation matrix for aU the work package durations. The correlation matrix used is, • R-m —  1.0  -0.999  -0.999  1.0  . 0.999  0.999 ' -0.999  -0.999  1.0 .  which has a condition number <f> equal to 2998.04. Table (4.6) shows the moments from the two methods. Again, the expected values are identical, but some of the central moments are different.  While some of the  variances were comparable, the others showed considerable differences.  The most  E[T) WP 01 02 03 04 05 06 07 08 09 10  Uncor .64294 .45628 .57906 .28024 .55286 .48430 .38089 .58158 .56646 .52807  Trans  Stdrd  Uncor  MT) Trans  .64168 .45254 .57625 .27986 .52657 .48156 .37804 .57981 .56472 .53086  .64168 .45254 .57625 .27986 .52657 .48156 .37804 .57981 .56472 .53086  .05127 .01022 .04172 .00762 .03521 .02886 .00979 .04175 .03997 .03906  .05109 .00727 .03951 .00842 .01447 .02669 .00801 .04091 .04224 .04559  Stdrd .04942 .00684 .03825 .00736 .00842 .02609 .00769 .03946 .03768 .04139  Uncor .00517 .00069 .00379 .00031 .01296 .00226 .00043 .00393 .01011 .00701  MT) Trans .00750 .00088 .00500 .00116 .01721 .00266 .00043 .00556 .01584 .00974  Stdrd .00410 .00053 .00273 .00022 .01092 .00172 .00031 .00293 .00874 .00562  Uncor .00545 .00025 .00351 .00012 .00417 .00177 .00011 .00360 .00632 .00320  Table 4 . 5 : First Four Moments of the Work Package Durations  MT) Trans .00692 .00025 .00409 .00026 .00361 .00190 .00008 .00447 .00866 .00465  Stdrd .00545 .00025 .00351 .00012 .00417 .00177 .00011 .00360 .00632 .00321  Chapter 4. Correlations Between Variables  95  Table 4.6: Moments of the Duration with an Unstable Correlation Matrix E[T]  WP 01 02 03 04 05 06 07 08 09 10  Trans Stdrd .6417 .6417 .4525 .4525 .5779 .5779 .2814 .2814 .5142 .5142 .4854 .4854 .3780 .3780 .5829 .5829 .5727 .5727 .5381 .5381  MT)  Trans Stdrd .05392 .04845 .00885 .00669 .04386 .03945 .01304 .00798 .00642 -.00505 .02921 .03258 .00754 .00929 .04711 .04227 .07155 .04508 .06099 .04761  MT)  Trans Stdrd .01217 .00051 .00244 .00026 .00874 .00037 .00413 -.00006 .01732 .00751 .00585 .00039 .00154 .00005 .00995 .00069 .05648 .00519 .02216 .00245  MT)  Trans .00814 .00045 .00524 .00057 .00336 .00293 .00022 .00596 .01994 .00845  Stdrd .00545 .00025 .00351 .00012 .00417 .00177 .00011 .00360 .00632 .00320  startling observation is the negative variance for the fifth work package duration, indicating that in the presence of multicollinearity the standard approach could fail. When the moments from the variable transformation method in Tables (4.5) and (4.6) are compared, the expected values compare well while the central moments are reasonably close, considering the fact that they have different correlation values. This study indicates that the transformation is not too susceptible to the instability in the correlation matrix for this example. The intention of the second study is to see how susceptible the transformation is to the effect of multicollinearity. Thek value" concept discussed in section (4.4) is u  used to study the percentage change in moments of a work package duration. If the percentage change from the base value moment (i.e at k = 0), for stable (small and unstable (large </>) correlation matrices, are similar with increasing k values (i.e as the matrices got more and more stable), then the transformation is not susceptible to instability in the correlation matrix. Figures (4.3) to (4.6) show the absolute percentage changes in the first four moments from the base values, for increasing k values. The percentage changes in the  Chapter 4. Correlations Between Variables  96  moments are similar when the condition number (c6) for the correlation matrices vary from 50 to 2998, indicating that the transformation is not susceptible to the instabihty (i.e effect of multicoUinearity) in the correlation matrix for this study. However, it must be noted that in another situation it is possible for multicoUinearity to effect the transformation. It is suggested that in practical applications of the variable transformation method (or the standard method) the condition number (<f>) of the correlation matrix be checked for multicoUinearity. If unstable correlation matrices have been ehcited, they can be stabilized using a smaU k value at the discretion of the analyst. This check is equally valid for the treatment of correlations in Monte Carlo simulation.  4.6  Summary  The correlations between the primary variables and between the derived variables is addressed in this chapter. The second section highlighted the often ignored requirement for the correlation matrix to be positive definite and developed a subjective ehcitation method to obtain a positive definite correlation matrix for primary variables. A positive definite correlation matrix recognizes the existence of multivariates in a system. The third section suggested a method to obtain a positive definite correlation matrix for derived variables when only the hnear correlations between the primary variables are available. The theoretical developments in these two sections are the basis for the part in the computer program "ELICIT" (see Appendix D) to obtain interactively the correlations between variables. The fourth section highlighted the concept of multicoUinearity and its possible effects on the variable transformation. A mathematical manipulation that could provide stabihty to the correlation matrix for practical applications was suggested.  Chapter 4. Correlations Between Variables  97  T h e final section utilizing the example of the work package duration showed numerically that the variable transformation  method is comparable to the  standard  approach i n treating correlation between primary variables under general conditions (stable correlation matrices).  Also, that the transformation simplifies the approx-  imations for the first four moments and treats the linear correlations consistently. T h e other two numerical studies explored the behavior of the transformation i n the presence of multicollinearity.  T h e first showed that the standard approach can fail  i n the presence of multicollinearity while the variable transformation method was more stable. T h e second study showed that the transformation was not susceptible to instabilities i n the correlation matrix.  Chapter 4. Correlations Between Variables  0.00  0.01  0.02  0.03  k value  Figure 4.3: Expected Values  0.04  o.c  CJiapter 4. Correlations Between Variables  0.00  0.01  0.02  0.03 k value  Figure 4.4: Second Central Moment  0.04  o.c  Chapter 4. Correlations Between Variables  =2998 = 600 = 300 = 100 - 50  0.00  0.01  0.02  0.03 k value  Figure 4.5: Third Central Moment  0.04  0.05  Chapter 4. Correlations Between Variables  0.00  0.01  0.02  0.03  k value  Figure 4.6: Fourth Central Moment  0.04  0.05  Chapter 5 Decomposition of a D e r i v e d Variable  5.1  General  In developing the risk measurement framework to quantify the uncertainty of a derived variable it was assumed that a derived variable can be more accurately estimated from a set of primary variables that are functionally related to it than by direct estimation (assumption 2.3). This reflects the engineering penchant to seek more detail as a way of seeking greater precision. For most derived variables in engineering construction, assumption (2.3) is reasonable. However, this assumption becomes debatable when variables which are sometimes estimated holistically in the ehcitation of subjective judgments (probabilities) - (eg. duration, productivity) are decomposed. This chapter describes a study on the decomposition of such a derived variable. The duration of an activity is used as the example for the derived variable to compare holistic versus decomposed methods of estimation. The objective of this chapter is to make a small step towards exploring an issue that is largely ignored in the estimation literature and to provide the motivation for a more extensive study on the ehcitation of subjective probabilities for continuous random primary variables.  102  Chapter 5. Decomposition of a Derived Variable  5.2  103  Decomposition  Ravinder et al. (1988) state that decomposition is often regarded as a useful technique for reducing the complexity of difficult judgment problems. They studied the application of decomposition to the elicitation of subjective probabilities for discrete events. A target event for which probability judgments were required was decomposed into background events in the form of conditional probabilities of the target event. Once the individual conditional distributions for background events were elicited, the law of total probability was used for the aggregation. The probability of the target event Pr(A) was defined as,  Pr(A)  = £  Pr(A\Bi)  Pr(Bi)  (5.1)  t=i  where the background events denoted B\,  ,B  n  form a mutually exclusive and n  exhaustive partition of the relevant event space (i.e ^  Pr(Bi) = 1).  They concluded: if the component probabilities can be assessed with no greater precision than holistic assessment, decomposition reduces random errors associated with probability encoding; but as the number of events increases, error reduction will only occur up to a point (i.e a limit for decomposition exists). While it is not possible to generalize their conclusions to the decomposition of a derived variable to a functionally related set of primary variables, they are used as guidance for this study. In the context of this research, the main reason for decomposing work package variables to their primary variables is to develop a link between cost and time of the work package for economic analysis. It is incorrect to assume that cost is independent of time, because when a work package duration is either reduced (more resources) or increased (less resources) the net result is a change in the cost. The link between cost  Chapter 5. Decomposition of a Derived Variable  104  and time permits the use of net present value and internal rate of return as decision variables. The second reason is the basis for assumption (2.3), to reduce the complexity of holistic estimation because experts in construction (engineers) find it easier to quantify the decomposed primary variables. The same reasoning has been used by other authors for decomposing the activity duration into its primary variables (Jaafari, 1984 ; Hendrickson et al., 1987). This raises another question; won't it be more accurate if the primary variables are further decomposed. The main disadvantage of decomposition is the loss of the mental awareness of interdependencies between primary variables that exists when estimating from a holistic approach. While it may be possible to relate the primary variables functionally to the derived variable, it is also difficult to model all the interdependencies (Inyang, 1983). Secondly, even if accurate estimates for primary variables are obtained, as the decomposition is continued a model which can link them to provide a reliable estimate of the derived variable may be lacking. As Ravinder et al. (1988) have shown a definite limit exists for decomposition. Thirdly, unless decomposition improves the system significantly, it would be hard to convince an expert that decomposition is necessary for the ehcitation of subjective probabilities. It was stated in chapter three that convincing experts about the relevance of the primary variables was essential to gain their full cooperation during the ehcitation (Cooper and Chapman, 1987; Huber, 1974; Hull, 1980; Spetzler and Stael von Holstein, 1975). The next four sections propose hypotheses, test statistics, an experiment and the analysis to study a derived variable that is sometimes estimated holistically. In addition, some of the beliefs that exist in engineering construction regarding decomposed versus holistic estimation of judgments are explored.  Chapter 5. Decomposition of a Derived Variable  5.3  105  Hypotheses  Nine hypotheses are suggested to study decomposed versus holistic estimation of an activity duration. The first hypothesis is about the precision of assessments for an activity duration from the two approaches. The other eight are for assessed expected values and standard deviations for an activity duration to compare holistic versus decomposed estimation. Hypothesis 5.1  The precision of assessments for an activity duration from holistic or decomposed estimation are similar. The precision of assessments from the two approaches are measured using the coefficient of variation for duration of an activity, a non-dimensional measure of variation. Ho : E[Vi\ = 0  ;  H  x  : E [Vi] ? 0  where Vi = Vu — Vw , the difference between a pair of coefficients of variation of i  t  the assessed activity duration from decomposed (V^) and holistic (Vwi) estimation.  Hypothesis 5.2  When experts are asked to assess the expected value for duration of an activity from the holistic approach that assessment will be the true value.  Ho : E[T] = E[T ] W  ;  #i  :  E[T] ^  E[T ] W  Hypothesis 5.3  When experts are asked to assess the standard deviation for duration of an activity  Chapter 5. Decomposition of a Derived Variable  106  from the holistic approach, that assessment will be the true value.  H  : o~  0  T  =  <T  TW  ;  H-i  : cr  T  ^  a  Tw  Hypothesis 5.4 When experts are asked to assess the expected value for duration of an activity from the decomposed approach that assessment will be the true value.  H  0  : E[T] = E[T ] D  ;  H, : E[T] + E [T ] D  Hypothesis 5.5 When experts are asked to assess the standard deviation for duration of an activity from the decomposed approach, that assessment will be the true value. H : a — CT ; H : a ^ CT 0  T  TD  x  T  TD  Hypothesis 5.6 When experts are asked to assess the expected value for duration of an activity from the holistic approach, that assessment will be an underestimation of the true value.  Ho : E[T] = E[T ] W  ;  H  x  : E[T] > E[T ) W  Hypothesis 5.7 When experts are asked to assess the standard deviation for duration of an activity from the holistic approach, that assessment will be an underestimation of the true value. H  0  :  CTT  =  (TT  W  ;  Hi  :  CTT  >  CTT  W  Chapter 5. Decomposition of a Derived Variable  107  Hypothesis 5.8 When experts are asked to assess the expected value for duration of an activity from the decomposed approach, that assessment will be an underestimation of the true value.  Ho : E[T]  = E[T ) D  ;  if  :  : E[T] > E [TD]  Hypothesis 5.9 When experts are asked to assess the standard deviation for duration of an activity from the decomposed approach, that assessment will be an underestimation of the true value. Ho  •  cr  T  =  O-  TD  ;  Hi  :  ar  >  <TT  B  where E [TV] and E [TD] are the expected values and O~T and ar W  D  are the  standard deviations assessed for the activity duration from holistic and decomposed subjective estimation. While a hypothesis test is done for the first hypothesis, only significance tests (i.e a hypothesis can only be rejected) are done for the next eight because there is only one sample to test all of the hypotheses. Hypothesis (5.1) tests whether there is a difference between the precision of assessments (i.e coefficients of variation) from the two approaches. The next four hypotheses (5.2 to 5.5) provide the basis to compare the two approaches. By calculating the percentages of the number of times an individual hypothesis is rejected, given the group of experts and the amount of information available during the elicitation, the two approaches are compared. Hypotheses (5.6) to (5.9) are included because of the traditional belief in engineering construction that holistic approach underestimates duration more regularly than the  Chapter 5. Decomposition of a Derived Variable  108  decomposed approach (i.e holistic will be rejected more times than the decomposed). It must be stressed that the objective of this study is not to select the "better" method, but to compare the two methods available for estimating duration when experts participate in subjective ehcitation. The "better" method is a consensus approach after estimating from both approaches. However, it is not practical as a subjective ehcitation technique.  5.4  Test Statistics  Assumption  5.1 : A sample of durations to complete an activity constitutes a  random sample from a normal distribution with both u and cr unknown. Since the sample of the measured durations are for the same activity it is reasonable to expect the measurements to be symmetric around the mean value. Then the test statistic for the difference between paired coefficients of variation of the assessed activity duration is (Devore, 1982), ^paired  V S /y/n  (5.2)  v  where V and S are sample mean and standard deviation, respectively for V{'s and v  n is the sample size. The rejection regions for level a tests are (see figure 5.1), Hypothesis 5.1  Rejection Region tpaired  > *f,n-l  °r  ^paired  <  —  £ |  )  T  l  _ i  Chapter 5. Decomposition of a Derived Variable  109  Test statistic for the assessed expected value for activity duration is (Devore, 1982) t =  f  - E [To] 5/Vn  (5.3)  where T and S are the sample mean and standard deviation, E [T ] is either the 0  assessed E [TV] or E [Try]. The rejection regions for level a tests are (see figures 5.1 and 5.2), Hypothesis  Rejection Region  5.2 and 5.4  *  >  or t < - * ! , „ - !  *f,n-i  5.6 and 5.8  t  >  £ ,n-l a  Test statistic for the assessed standard deviation for activity duration is (Devore, 1982), *> = ^  («.4)  where S is the sample standard deviation and cry is the assessed 0  O~T  W  The rejection regions for level ct tests are (see figures 5.3 and 5.4), Hypothesis 5.3 and 5.5  5.7 and 5.9  Rejection Region X  2  > x|,„_i  X  or  x  2  <  — Xa,n — 1  Xi-f.n-1  or  <TT  D  •  Chapter 5. Decomposition of a Derived Variable  Figure 5.1: t Distribution for Two Tailed Test  Figure 5.2: t Distribution for Upper Tailed Test  110  Chapter 5. Decomposition of a Derived Variable  0.06  i  ( ; )  f  x  v  0.05 0.04 h 0.03 0.02 Rejection egion  Rejection Regio  0.01  20  10  30  40  50  Figure 5.3: x Distribution for Two Tailed Test 2  0.06  i  f  ( ; ) x  v  Figure 5.4: x Distribution for Upper Tailed Test 2  60  Chapter 5. Decomposition of a Derived Variable  5.5  Experiment  5.5.1  The Activity  112  The activity to obtain a sample of durations to test the hypotheses should: permit the assessment of duration from hohstic and decomposed subjective estimation; permit the measurement of actual duration; be repetitive; utihze the expertise of construction engineers - read, interpret and visualize construction drawings. While an activity such as the repetitive construction of a column, a beam or a footing is ideal, the inherent difficulties of field experiments such as: free access to a construction site; measurement of actual duration; and time constraints; makes the selection infeasible. Instead, the assembly of a LEGOLAND wheel loader (model #6658) was selected as the activity for the experiment. In addition to satisfying the requirements of an activity for the experiment, the LEGOLAND model permitted the experiment to be conducted in a laboratory setting.  5.5.2  Procedure  First, the objectives of the experiment were explained to the participants. This explanation was based on the procedure of pre-elicitation discussed in section (3.3). Thereafter, using two questionnaires the desired subjective percentile values for the activity duration were ehcited from all the participants. The first questionnaire ehcited the duration in minutes to assemble the complete model in accordance with the drawings (i.e hohstic estimation). The second ehcited the duration in seconds to identify and attach one component to the model in accordance with the drawings (i.e decomposed estimation). Finally, the actual duration to assemble the model by each participant was measured (see Table 5.1).  Chapter 5. Decomposition of a Derived Variabie  113  The participants were graduate students and final year undergraduates in civil engineering who had followed courses in engineering economics and risk analysis. The subjective elicitation based on the drawings for the LEGOLAND model and its assembly in accordance with drawings utilized their expertise as civil engineers. Each participant is considered as an independent source for hypotheses testing. That is, evaluated expected values, standard deviations and coefficients of variation from the two approaches for each participant are the basis for a set of hypotheses. The measured actual durations constitute the sample to obtain statistics to test each set of hypotheses.  5.6  Analysis  The expected values, standard deviations and coefficients of variation for duration (holistic - duration to assemble the complete model; decomposed - duration to identify and attach one component to the model) are evaluated from equations (2.5) to (2.11) using the elicited subjective percentile values. However, for hypotheses on decomposed estimation, the moments for duration to assemble the complete model have to be evaluated.  5.6.1  Moments from Decomposition  For a LEGOLAND model consisting of I components, the duration to assemble the complete model is, T  D  = £  t  (5.5)  i=l  where t is the duration to identify and attach one component to the model. It is assumed that t is identical for all the components.  Chapter 5. Decomposition of a Derived Variable  114  The expected value for duration to assemble the complete model from decomposed estimation is, E[T ]  (5.6)  = lE[t]  D  where E[t] is the evaluated expected value for duration to identify and attach one component to the model and assumed to be identical for all the components. Assumption 5.2 : The estimated coefficients of variation for duration for an activity from hohstic and decomposed estimation are similar (i.e VD = Vw =  V).  Assumption (5.2) is tested by hypothesis (5.1). Therefore, from the definition for coefficient of variation,  cr  Tw  E[T ] W  _  a  ~  TD  E[T ]  _  a  t  E[t]  D  =  ^  y  The standard deviation for duration to assemble the complete model depends on the assumption regarding the correlation between duration to assemble individual components. The variance for duration to assemble the complete model is,  MI(TJD) =  From definition,  ^ 0.  P2(TD)  p (T ) 2  D  £ £ i=i j=i  covltittj)  (5.8)  Hence,  = la  2  + pl(l-l)a  2  >  0  (5.9)  where o~ is the evaluated standard deviation for duration to identify and attach one t  115  Chapter 5. Decomposition of a Derived Variable  component to the model and p is the correlation coefficient between two component durations. Rewriting equation (5.9),  p (T ) 2  Since  a\  > 0, for  D  P (TD) 2  =  a  [l + p(l -l)}  2  2  t  >  0  (5.10)  to exist > 0  I + p(l -l)} 2  (5.11)  Therefore,  Hence,  hm p = 0. From definition  p < 1.  I—too  A t t h e extremes, component durations  are either uncorrelated  or perfect  positive correlated.  When the component durations are assumed to be uncorrelated, the variance for duration to assemble the complete model from equation (5.9) is,  = I af  p {T ) 2  D  (5.13)  Hence, the standard deviation is, a  TD  =  Vla  (5.14)  t  When the component durations are assumed to be perfect positive correlated, the variance for duration to assemble the complete model from equation (5.9) is,  p (T ) 2  D  = I a 2  2  (5.15)  Chapter 5. Decomposition of a Derived Variable  116  Hence, the standard deviation is, °~Tr> =  1  °t  (5.16)  where cr is the evaluated standard deviation for duration to identify and attach one t  component to the model and assumed to be identical for-all the components. Comparing equations given by equation  (5.16)  (5.6), (5.7), (5.14)  and (5.16) it is evident that relationship  evaluates the standard deviation for duration to assemble  the complete model from decomposed estimation.  5.6.2  Experimental Results  The actual duration to assemble the LEGOLAND model and the expected values, standard deviations and coefficients of variation for duration from holistic and decomposed estimation are given in Table 5.1. All of the participant are given an identification  The subjective estimates of participant # 15 were rejected.  The sample mean and standard deviation for the sample of actual measured durations to assemble the LEGOLAND model in minutes are, f — 15.95 and S = 7.99. The sample mean and standard deviation for K's, the difference between paired coefficients of variation are, V for  27  =  0.0518  participants from equation  (5.2)  and S  v  =  is equal to  0.1714.  The test statistic  ipai d Pe  1.5698.  The t values for assessed expected values and % values for assessed standard 2  deviations for holistic and decomposed estimation evaluated from equations (5.3) and (5.4) are given in Table 5.2. Table 5.1, Table 5.2, sample means and standard deviations are obtained from a computer program called "LEGO".  Chapter 5. Decomposition of a Derived Variable  117  Table 5.1: Actual and Estimated Statistics for the Activity Duration (minutes)  #  01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  Actual Duration 10.0 13.0 16.0 16.0 24.0 18.0 21.0 7.75 16.83 13.75 25.5 13.42 47.0 24.5 9.25 9.5 18.92 16.5 9.92 10.05 10.82 21.3 9.08 16.88 17.0 11.9 10.78 8.09  Hohstic Estimation E[T ) W  12.00 3.18 28.70 15.55 17.44 30.00 11.81 5.37 29.07 20.92 6.81 6.28 30.00 14.18 10.18 10.00 25.00 19.07 10.55 3.00 30.00 9.18 18.52 5.99 12.74 10.00 9.63  °~T  W  4.02 1.41 13.94 6.33 7.03 12.56 5.91 2.57 8.89 8.89 2.53 2.05 10.05 3.96 1.78 3.77 4.27 9.56 3.12 0.75 7.54 3.53 6.14 3.65 3.19 3.02 2.81  V  w  Decom D o s e d Estimation E[T ]  V  D  0.335 15.01 0.442 10.67 0.485 24.29 0.407 24.49 0.403 5.29 0.418 16.00 0.500 11.26 0.478 6.99 0.306 52.43 0.425 11.54 0.371 15.80 0.326 5.23 0.335 16.00 0.279 10.47 0.175 39.86 0.377 10.86 0.171 32.99 0.502 5.48 0.295 6.30 0.251 4.27 0.251 32.00 0.384 7.57 0.332 9.88 0.610 13.13 0.250 26.86 0.302 5.33 0.292 3.40  D  6.32 3.75 15.84 15.65 3.03 7.50 5.57 3.58 34.00 4.03 5.37 0.95 8.43 2.96 1.96 7.91 5.54 0.96 0.68 1.07 8.04 3.75 2.90 8.31 18.01 1.61 2.03  0.421 0.352 0.652 0.639 0.572 0.469 0.495 0.512 0.636 0.349 0.340 0.181 0.527 0.283 0.049 0.728 0.168 0.175 0.109 0.251 0.251 0.496 0.293 0.632 0.671 0.302 0.597  Chapter 5. Decomposition of a Derived Variable  Table 5.2: Test Statistics for Expected Values and Standard Deviations Holistic Estimation  #  01 02 03 04 05 06 07 08 09 10 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 27 28  t  2.6204 8.4610 -8.4479 0.2649 -0.9873 -9.3060 2.7430 7.0133 -8.6931 -3.2931 6.0559 6.4120 -9.3060 1.1727 3.8230 3.9456 -5.9931 -2.0640 3.5778 8.5836 -9.3060 4.4856 -1.6996 6.6033 2.1.301 3.9456 4.1907  Decomposed Estimation t  106.55 870.43 8.86 42.97 34.85 10.91 49.29 261.42 21.78 21.78 269.82 410.52 17.05 109.87 544.55 121.24 94.39 18.83 177.44 3030.90 30.31 138.40 45.67 129.07 169.33 189.43 217.61  0.6238 3.5038 -5.5249 -5.6556 7.0641 -0.0299 3.1116 5.9386 -24.8280 2.9278 0.1008 7.1030 -0.0299 3.6346 -15.8399 3.3731 -11.2849 6.9395 6.3962 7.7443 -10.6311 5.5587 4.0268 1.8695 -7.2246 7.0376 8.3203  x  2  43.11 122.32 6.87 7.03 187.63 30.58 55.55 134.12 1.49 106.04 59.77 1914.45 24.23 196.36 449.77 27.51 56.14 1864.32 3676.43 1498.43 26.64 122.15 205.07 24.96 5.31 665.97 418.09  Chapter 5. Decomposition of a Derived Variable  5.6.3  119  Hypotheses Testing  All of the hypotheses are tested at confidence level a = 95%. Then, *«,„-! = 1.703, xl,„-i = 43.194, 2  n = 28. Since  fpai d r e  xL-„_! 2  = 14.573,  a  n  d  £  i|,n-i  =  = 2.052,  40.113 for  '  is within the acceptance region, hypothesis (5.1) is accepted at  95% confidence level. Hence, the assumption that coefficients of variation for activity duration from hohstic and decomposed estimation are similar is verified. The results of the significance tests for hypotheses (5.2) to (5.9), total and percentages of the number of times an individual hypothesis is rejected at 95% confidence level are given in Table 5.3. 'R' indicates that the hypothesis is rejected, while 'NR' indicates that it is not rejected at 95% confidence level. The significance tests show high rejection rates and similar percentage values for both methods of estimation. The classical approaches for hypotheses testing and the generally accepted confidence levels used in this study may not be the most suitable to test human ability to predict future events because of the high variability in predictions from individual to individual. While broader confidence levels reduce the rejection rates they may not be acceptable from a statistical view point. This highhghts the inherent difficulties in developing experiments to measure human abihty to predict future events. Similar rejection percentages of individual hypothesis confirm the view that neither is the "better" method. Those for hypotheses (5.6) to (5.9) contradict the traditional belief that hohstic estimation underestimates duration more regularly than decomposed estimation. If decomposition is not critical to the decision problem when only work package and project duration estimates are desired; the approach preferred by the analyst and experts can be used for subjective ehcitation. However, if decomposition is important to the decision problem - Ravinder et al., (1988); decomposed estimation alone can be used with confidence that the precision of assessments are similar to those from hohstic estimation and that it can reduce random  Chapter 5. Decomposition of a Derived Variable  120  Table 5.3: Significance Tests for Hypotheses (5.2) to (5.9) at 95% confidence level  # 01 02 03 04 05 06 07 08 09 10 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 27 28 Total  Hypotheses 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 R R NR NR R R NR R R R R R R R R R R R R R NR NR NR NR NR NR R R NR R NR NR NR NR R R NR NR R R R R NR NR NR NR NR NR R R R R R R R R R R R R R R R R R NR R R NR NR NR NR R NR R R NR NR R R R R NR R R R NR R R R R R R R R R R NR NR NR NR NR NR NR NR R R R NR R R R R R R R R R NR R R R NR R R R R NR R R R R NR R NR R R NR R R NR NR R R R R R R R R R R R R R R R R R R R NR R NR NR NR NR NR R R R R R R R R R R R R R R R R R NR R NR R R R NR R R R R R R NR NR R R R R R R R R R R R R R R R R 24R 20R 22R 21R 16R 19R 16R 18R 88.89% 74.07% 81.48% 77.77% 59.26% 70.37% 59.26% 66.66%  Chapter 5. Decomposition of a Derived Variable  121  errors (Ravinder et al., 1988).  5.7  Summary  This chapter described a study on the decomposition of a derived variable that is sometimes estimated holistically. The study consists of a set of hypotheses, test statistics, an experiment and analysis to compare holistic versus decomposed methods for estimating duration when experts participate in subjective elicitation. While classical approaches and confidence levels used in this study may be too restrictive to test the human ability to predict future events, they provide a statistically accepted framework. The interpretation of the results as percentages of rejection rates reduce some of the restrictions. The first hypothesis verified the assumption that coefficients of variation in subjective assessments for duration from holistic and decomposed estimation are similar. The next four support the view that neither is the "better" estimation approach to elicit subjective assessments for duration. The last four while contradicting the traditional belief in construction about holistic estimation of duration confirm the view regarding the "better" approach. It must be stressed that these are observations based on this study and in no way can they be generalized to the decomposition of derived variables that are sometimes estimated holistically. The recognition that some of the implicit assumptions and beliefs in engineering construction (assumption 2.3; holistic estimation underestimates duration more regularly) should be explored when dealing with the human ability to predict future events and the inherent difficulties in developing experiments and methods to test such beliefs are some of the benefits of this study. It is recommended that this topic be explored further.  Chapter 6 The Analytical Method  6.1  General  The generation of economic benefits is one of the fundamental objectives of an investment in a project. Hence, the initial decision to invest is governed by the ability of the project to generate a return that would justify the investment. Figure (6.1) shows the generalized cash flow diagram for an engineering project. However, a more simplified cash flow diagram as shown in figure (6.2) is used for the development of the analytical method. In this simplified scenario, the expenditure for design and construction comes from a combination of equity and interim financing. It is assumed that repayment of interim financing is due at the end of the construction period. No attempt is made to include permanent financing in the analytical method because of the numerous financing alternatives available in the market. The analytical method described herein is developed by applying the framework to quantify the uncertainty of a derived variable to the three levels of the project economic structure as shown in figure (6.3). At the work package/revenue stream level the derived variables are work package duration, start time, cost and net revenue streams. The primary variables at the work package/revenue stream are those variables in the functions specified by the analyst. At the project performance level the derived variables are the project duration, cost and revenue while the primary  122  Chapter 6. The Analytical Method  $/year  /  ^ Permanent Financing  Interim Financing  /  123  Salvage Value  \ —  \  Current Dollar \  Expenditure  /  Amortization of Permanent Financing  /  Time (yrs)  Ope^ratin^  Repayment of interim Financing  Discharge of Loan Balance  Figure 6.1: Generalized Cash Flow Diagram for an Engineering Project  n  $/year Salvage'> Value  ' - Interim Financing Due  Figure 6.2: Cash Flow Diagram for the Analytical Method  Chapter 6. The Analytical Method  124  variables are the derived variables at the work package/revenue stream level. At the project decision level the derived variables are project net present value and internal rate of return while the primary variables are discounted project cost and revenue. This apphcation combines all of the developments and studies from chapters two to five. For generality, the analytical method treats cost and revenue as continuous cash flows under continuous discounting (Tanchoco et al., 1981; Buck, 1989).  6.2  Work Package/Revenue Stream Level  The work package/revenue stream is the first level of apphcation. At this level, the framework is applied as developed, permitting the analyst to use general functional forms for work package durations, costs and revenue streams.  6.2.1  Work Package Duration  Work package duration can be estimated directly as a hohstic value or derived using a functional relationship which treats work scope, anticipated job conditions, likely construction methods, productivity and resource levels or a sub-network of activities. When the estimation is hohstic, the analyst/experts provide percentile values for their subjective prior probabihty distributions and the correlation matrix for work package durations. The first four moments for a work package duration are evaluated from the method described in section (2.3.2) using these percentile values. When the estimation is decomposed, the analyst must specify the functional forms for work package durations. The analyst/experts provide percentile values for their subjective prior probabihty distributions and the correlation coefficients for primary variables in the functions for work package durations and identify common (shared)  125  Chapter 6. The Analytical Method  Analyst/Expert Input * Precedence Relations among Work Packages and Revenue Streams * Functions for Work Package Duration, Cost and Revenue Streams * Subjective Estimates for Percentiles of Primary Variables and Correlation Matrices, and Shared Variables in Functions for Work Package Durations, Costs and Revenue Streams  t W.P Durations  1  W.P Costs and Revenue Streams  W.P Start Times  I WORK PACKAGE/REVENUE STREAM LEVEL  I First Four Moments for Work Package and Revenue Stream Start Times, Work Package Durations, Costs and Net Revenue Streams  PROJECT PERFORMANCE LEVEL  I First Four Moments for Project Duration, Cost and Revenue  PROJECT DECISION LEVEL  I First Four Moments for Project Net Present Value and Cumulative Distribution Function for Project Internal Rate of Return  Figure 6.3: Flowchart for the Analytical Method  Chapter 6. The Analytical Method  126  primary variables among the functions. The correlation matrix for work package durations is evaluated from this information. The specified function for a work package duration is treated as <j(X) in equation (2.19). The first four moments for a work package duration are evaluated from equations (2.36) to (2.39) using the ehcited positive definite correlation matrix for the variable transformation.  6.2.2  Work Package Start Time  Since start time positions the work package with respect to time it is the variable that links time and cost. Consequently, it is important to have accurate estimates of the moments for start times. In most analytical methods, the start time of a work package is determined by the longest path to that work package. Let Tf be the start time of the i  th  be the duration of the preceding h  th  work package, Tf be the start time and Th  work package. The start time of the i work th  package from the longest path is denned as,  Tf  = maxi  [Tf + T ]  (6.1)  h  where maxV implies that the maximization is to be over all the hnks h to i" teru  minating at the i  th  value for the i  th  work package. While equation (6.1) gives the maximum expected  work package start time, it does not necessarily evaluate the max-  imum uncertainty because it ignores shorter but more uncertain (higher variance or skewed) paths. This is the main drawback in using the longest path approach in stochastic network analysis. In theory, an accurate estimate of the moments for start times would involve the analysis of all paths leading to the work packages. Ang et al., (1975), proposed an analytical technique called "Probabilistic Network Evaluation Technique (PNET)" to evaluate the completion time probabihty of project  Chapter 6. The Analytical Method  127  duration by considering multiple paths to complete the project. Since project duration is the start time of the finish work package of the precedence network the developments in PNET can be generalized to the work package start time.  PNET For a project network with a specified number of activities and a set of n possible paths from the start node to the end node, Ang et al., (1975) state the probability of completing the project in time t, denoted p(t) is,  p(t) = 1 +  where Ti,T , 2  [P(T >t) 1  +  P(T <t,T >t) 1  + P(Ti <t,T < 2  t,  2  , r _ ! < t,T > t)] n  n  (6.2)  , T are the durations of the respective n paths. The bounds on the n  completion time probability p(t) are (Ang et al., 1975), n  TT P(Ti <t) < p{t) < min P(T < t) {  (6.3)  When all the paths are assumed to be statistically independent, (i.e all possible paths to a node or work package are used to evaluate p(t)), the value for p(t) is the most pessimistic (lower bound) and when all the paths are assumed to be perfectly correlated (so that one path is representative of all paths), the value for p(t) is the most optimistic (upper bound). The lower bound of p(i) is the upper bound for duration (see figure 6.5). When all the paths are perfectly correlated, duration is represented by the longest path. The longest path duration always gives an optimistic estimate for completion time probability (Ang et al., 1975). In other words, the longest path always yields the most  Chapter 6. The Analytical Method  128  optimistic mean duration for work package start time. Since work package cost and revenue stream calculations are linked to start time, longest path based analytical solutions do not adequately estimate the statistics of the derived variables. This is the rationale for a "better" solution from Monte Carlo simulation. On the other hand, if the work package start times are based on the lower bound of p(t), it yields the most pessimistic mean duration. Therefore, when an alternative is evaluated at the bounds of equation (6.3), the resulting solutions are the bounds for the derived variables in the project economic structure. The start times on which the derived variables should be estimated can be obtained from equation (6.2) if the joint probabilities between the path durations are evaluated. However, the evaluation of joint probabilities for equation (6.2) is complex (Ang et al., 1975). Instead, PNET works around this problem by considering all major paths for estimating p(t) while avoiding the evaluation of joint probabilities. PNET assumes that the activity durations are statistically independent. Also, it is limited to treating finish to start = 0 relationships between activities to evaluate the expected values and variances of individual path durations. Although individual activities are considered to be statistically independent, two different paths are considered to be correlated as a result of common activities. Then, the correlation between two paths i and j having m common activities is defined as (Ang et al., 1975),  PH =  k  -^—~  (6.4)  (Ti (Tj  where a\j is the variance of the k  th  k  common activity on paths i and j, ai and aj  are the standard deviations for duration of paths i and j and p^ is the correlation coefficient between paths i and j. An approximation for computing p(i) was derived by Ang et al., (1975) from the  Chapter 6. The Analytical Method  129  following observations: (1) paths with long mean durations and high coefficients of variations have the greatest impact on p(i) (defined as major paths); (2) if several paths are each highly correlated with a major path, then those paths can be represented by that major path (upper bound of p(t))] (3) if representative paths have low correlations, p(t) can be approximated by the product of the respective path probabilities (lower bound). Consequently, PNET approximates the project completion time probabihty, p(t) by,  p(t) «  where P(Ty < t), P(T < t), 2  P(T  X  < t)  P(T  2  < t)  P(T  r  < t)  (6.5)  , P(T < t) are the probabilities of each representative T  path completing the project in time t, for r representative paths. Those paths with p^ > p are represented by path i (the longer path because it has a lower p(t)) from the assumption that p represents the transition between high and low correlation. When p = 1, the estimate for p(t) is the lowest (upper bound on duration), whereas when p = 0, p(t) is the highest (lower bound on duration). If all the major paths are correlated with the longest path, PNET reduces to PERT. In applying PNET, Ang et al. (1975) estimate p(t) from equation  (6.5) using a transitional  correlation value of p = 0.5 and assuming the representative path durations to be normally distributed. Some of the shortcomings of PNET are: (1) by assuming the individual activities (or work packages) to be statistically independent it ignores the correlation brought about by the use of shared resources such as manpower, equipment, management, etc; (2) p(t) is dependent upon the level of interdependence between various paths, i.e the selection of the most suitable transitional correlation p (Crandall, 1977); (3) a representative path duration may not be normally distributed if a few skewed  Chapter 6. The Analytical Method  130  work package durations dominate the path to a work package or if the work packages appear early in the network.  Modified P N E T  The PNET algorithm developed by Ang et al., (1975), is modified to overcome some of the shortcomings in applying it to work package start time. The modifications are: (1) include the Hnear correlations between work package durations in evaluating the first four moments of path durations; (2) include the shape characteristics (skewness and kurtosis) of representative paths in evaluating the first four moments of the work package start time. The modified PNET approach to compute the first four moments of a work package start time are as follows. First, the first four moments for duration of each path to a work package are evaluated using equations (6.11) to (6.14), thereby including the hnear correlations between work package durations. To facihtate the treatment of correlations between work package durations on a path, only finish to start = 0 relationships are permitted. Then, in order of decreasing mean path durations all of the individual paths are sequentially ordered. Second, representative paths to a work package are identified as in PNET. Similar to PNET, the transitional correlation p must be specified by the analyst. Third, the first four moments of the representative path durations are used to approximate cumulative distribution functions from the Pearson family of distributions. This ensures that shape characteristics of a representative path are not ignored. However, as discussed in section (2.4.5), it may not always be possible to approximate a Pearson type distribution. In such a situation, modified PNET defaults to PNET. Fourth, the cumulative distribution function for start time of a work package is developed by evaluating p(t) from equation (6.5) for a  Chapter 6. The Analytical Method  131  range of durations. The starting duration for the distribution range is obtained from, tetart  where E[Ti]  max  = E[Ti]  max  - 3 <T  (6.6)  imaz  is the largest expected value from all the path durations (i.e the  expected value of the longest path) and tr^^. is the largest standard deviation for all the path durations. If p(t) > 0 for the starting duration, then t  3tart  in equation (6.6) is  reduced until the starting p(t) — 0. The duration range for the cumulative distribution function is complete when p{t) — 1 is obtained. Finally, given the tableau of values for p(t) versus t, the first four moments for work package start times are evaluated similar to section (2.3.2). In the author's experience, the developed cumulative distribution functions for start times have always approximated to Pearson type distributions. However, the default is the PNET algorithm. The improvements to the work package start time by applying modified PNET instead of PNET are: (1) since the work package start time is always a primary variable in the functional form for work package cost, the treatment of correlation at two levels, - between work package durations on an individual path and between paths due to common work packages makes the evaluation of first four moments for work package start times, costs and their bounds more precise; (2) considering skewness and kurtosis of the individual paths makes the first four moments for start time of work packages at the beginning of a project more precise, because the number of predecessor work packages on an individual path are too few to invoke the central limit theorem. When there are sufficient predecessor work packages on a path to invoke the central limit theorem (as done by PNET), the approximation of the path durations to the Pearson family of distributions will reflect it because the normal distribution is a member of the Pearson family. The drawbacks of modified PNET are: (1) it is also dependent upon the level of interdependence between various paths (Crandall, 1977). However, the estimation of  Chapter 6. The Analytical Method  132  upper and lower bounds provides a sensitivity analysis on the transitional correlation specified by the analyst; (2) allows only single (finish to start — 0) logic relationships to sequence work packages. Ability to sequence work packages in overlapping and/or compound relationships will enhance the practicality of the application. However, the treatment of correlation between work packages in overlapping and/or compound relationships on a path or between paths are still theoretically complex. Harris (1978) has shown that overlapping relationships can be transformed to single relationships (finish to start = 0) using one or two additional work packages, and compound relationships using two additional work packages with time-discontinuous assumption.  6.2.3  Work Package Cost  The estimate for expenditure to design and construct a work package is defined as the work package cost. External economic variables have a strong influence on the work package cost estimate. Escalation primarily due to inflation and interest payments for the construction loan (interimfinancing)are a significant portion of the cost estimate. In estimating the escalation during construction in work package cost, the analytical method allows different rates for different categories of cost. For the simplification of the derivation, it is assumed that the inflation rates and interest rate forfinancingof work package cost are constant over the construction period. However, if necessary both of these quantities can be expressed as functions of time. The generalized discounted work package cost is represented by (see figure 6.4),  WPd  =  f eVc'-vPsa  + (1 -  /)  e  f Jo  Coi(r) ( ^ - v K dr  Tci  (r-v)T  P  (6.7)  e  e T  e  Ci  Sci  f  Tci  Jo  c  ,  r  )  (e -r)r  e  Ci  d  r  Chapter 6. The Analytical Method  where WPCi is the discounted i stant dollar cashflowfor the i  th  133  work package cost, work package, Ts  Ci  COJ(T)  is the function for con-  and Ta are work package start  time and duration, T is the time at which the repayment of interim financing is p  due for all work packages, / is the equity fraction, 8c ,r and y are inflation, interest {  and discount rates respectively. The time r is measured from the start of the i  th  work package. Coi(r) can be either hohstic or a decomposed function of work scope, resources applied, and productivity. The estimation of discounted work package cost is always decomposed. The analyst specifies the functional form Coi(t) for equation (6.7). The analyst/experts provide percentile values for their subjective prior probabihty distributions and the correlation coefficients for primary variables in the functions for discounted work package costs and identify common primary variables among the functions. The correlation matrix for work package costs is evaluated from this information. The system function #(X) to approximate the first four moments for a discounted work package cost is equation (6.7). The first four moments for a discounted work package cost are evaluated from equations (2.36) to (2.39) using the ehcited positive definite correlation matrix for the variable transformation. The bounds for work package costs are obtained when the transitional correlation p = 1 and p — 0.  6.2.4  Net Revenue Stream  The possibility of generating a number of revenue streams at different points in time is typical of large engineering projects. Therefore, the ability to study the economic effects of projected revenue with respect to time is essential. The start time of a revenue stream is its link to the precedence network describing the development and operation phases. The analyst must specify the work package and the fraction of that  Chapter 6. The Analytical Method  Figure 6.4: Generalized Discounted Work Package Cost  Chapter 6. The Analytical Method  135  work package duration after which the revenue stream is projected to begin. The start time of the revenue stream is then evaluated from network analysis. To link revenue streams beginning after construction, the operation period is specified as the duration for finish work package. The duration for an individual revenue stream is a primary variable of the function for discounted net revenue. The net revenue stream is defined as the difference between gross revenue and its operation and maintenance cost. Both, the gross revenue and the operation and maintenance cost are inflated with different rates, and revenues are assumed to inflate T  once operation starts. The discounted net revenue stream is represented by,  NRSi  =  /  T S a + r f i  Mi2oi(0e  Ri  where NRSi is the discounted i  e R i ( t _ T s H i )  1  th  -^oi(f)e  i  e M i  e'^dt  (6.8)  1  net revenue stream, Roi(t) and M i(t) are the func0  tions for constant dollar cash flow for i gross revenue and operation and maintenance th  cost, T$ and Tm are early start time and duration of the revenue stream, 0^,6]^. m  and y are inflation and discount rates respectively. The estimation for discounted net revenue stream is also decomposed. The analyst specifies Roi(t) and Moi(t) for equation (6.8) as functional forms or holistic constant dollar values. The analyst/experts provide percentile values for their subjective prior probability distributions and the correlation coefficients for primary variables in the functions for discounted net revenue streams and identify common primary variables among the functions. The correlation matrix for net revenue streams is evaluated from this information. The system function #(X) to approximate the first four moments for a discounted net revenue stream is equation (6.8). The first four moments for a discounted net  Chapter 6. The Analytical Method  136  revenue stream are evaluated from equations (2.36) to (2.39) using the ehcited positive definite correlation matrix for the variable transformation. The bounds for net revenue streams are obtained when the transitional correlation p = 1 and p = 0.  6.3  Project Performance Level  The functions for all the derived variables at the project performance level are hnear additive. The derived variables at this level are project duration, project cost and project revenue, while the primary variables are the derived variables at the work package/revenue stream level. Assumption 6.1 : There are no non-linear correlations between the transformed variables at the project performance level. Let Y be a derived variable at the project performance level. Then, Y  (6.9)  = g(X) = £, X  (  where X is the vector of derived variables from the work package/revenue stream level. Let Z be the vector of transformed variables at project performance level (from equation 2.24). Since g(X) is always hnear, the transformed functional form G(Z) at the project performance level from equation (2.35) is,  Y  = G(Z) =  £ %  £ i=l  Zi  (6.10)  i=i  where B = D L . The expected value of the derived variable Y is,  E[Y) =  £  E  H  B  E[Zi]  (6.11)  Chapter 6.  The Analytical  137  Method  the second central moment of Y is,  P*{Y) =  dG  £  1  2  (6.12)  dZi  t=i  the third central moment of Y is, dG_  = £ i=l L  (6.13)  dZi  the fourth central moment of Y is,  M4(n  =£  dG  1  4  /x (Zi) 4  t=i  + 6 £ t=i  where  dG  £  dZi  moments of the i  Bji  ; and  £ j=i+i  E[Zi],  dG'  2  dG'  p (Zi), 2  p (Zi) 2  dZi  ps(Zi),  p (Zi) 4  p {Zj) 2  (6.14)  are the first four  3=1 th  transformed uncorrelated variable.  The first two moments of the derived variable are exact with or without assumption (6.1) because the transformed function G(Z) is hnear. With assumption (6.1), the third and fourth moments are also exact. The correlations between primary variables at the project performance level are hnear because correlations for derived variables approximated from section (4.3) are always hnear. These correlations are included in the moments for Z, and therefore in thefirstfour moments for the derived variable. Even if there are no non-hnear correlations among the primary variables, it is not possible to conclude that the transformed variables are free of non-hnear correlations.  Chapter 6. The Analytical Method  138  Hence, third and fourth moments will be in error only if non-linear correlations develop between the transformed variables. Since the measurement and treatment of non-linear correlations are still theoretically complex this assumption is reasonable. In addition, it permits the computation of exact first four moments for a derived variable at the project performance level.  6.3.1  Project Duration  The project duration is the start time of the finish work package of the precedence network. The first four moments of project duration are obtained from the modified PNET algorithm. The upper bound for project duration is computed when the transitional correlation p = 1 while the lower bound is when p = 0 (see figure 6.5).  6.3.2  Project Cost  The project cost is the summation of all the work package costs. When there are n work packages in the construction project, the discounted project cost is given by, n  DPC =  where WPCi  1S  the discounted i  th  WPd  (6.15)  work package cost from equation (6.7). The func-  tion g(X) for discounted project cost is equation (6.15). The first four moments for discounted project cost are computed from equations (6.11) to (6.14). The project cost is expressed in discounted dollars for generality. When required the project cost can be expressed in total, current or constant dollars. The bounds for project cost in total, current or constant dollars are obtained when the transitional correlation p = 1 and p = 0. A typical example for upper and lower bounds of project cost in current dollars is depicted in figure (6.6).  Chapter 6.  The Analytical Method  Figure 6.5: Upper and Lower Bounds for Project Duration  Longest Path (p =0.0) All Paths (p =1-0)  /  '  /  /  /  /  /  /  /  /  /  /  /  s  V- -r.,7?---r'  Cost (current dollars)  Figure 6.6: Upper and Lower Bounds for Project Cost  Chapter 6. The Analytical Method  6.3.3  140  Project Revenue  The project revenue is the summation of all the revenue streams. When there are m revenue streams in the construction project, the discounted project revenue is given by,  DPR  where NRSi is the discounted i  th  m = £ NRSi  (6.16)  net revenue stream from equation (6.8). The func-  tion g(X) for discounted project revenue is equation (6.16). The first four moments for discounted project revenue are computed from equations  6.4  (6.11) to (6.14).  Project Decision Level  The project decision level is the top of the hierarchy of the project economic structure. The derived variables at this level, project net present value and internal rate of return are the decision criteria for an investment. To quantify their uncertainty, the analytical method exploits the fact that the functions for these derived variables are the same for all engineering projects.  6.4.1  Project Net Present Value  The net present value of a project is the difference between the project revenue and the project cost discounted at minimum attractive rate of return.  The first four  moments for project net present value are computed by assuming discounted project cost and discounted project revenue to be independent. Then the first four moments of project net present value are,  Chapter 6. The Analytical Method  141  E[NPV]  = E[DPR]  u {NPV)  = u {DPR)  +  p {DPC)  (6.18)  u (NPV)  = u (DPR)  -  p {DPC)  (6.19)  2  3  2  PA(NPV)  where DPR  and DPC  3  PA(DPR)  -  E[DPC]  (6.17)  2  3  + p {DPC) 4  + 6 p (DPR) 2  p {DPC) 2  (6.20)  are discounted project revenue and discounted project cost  respectively.  6.4.2  Project Internal Rate of Return  The internal rate of return of a project is the discount rate at which the discounted project revenue is equal to the discounted project cost. In other words, the discount rate at which the project net present value is zero.  The internal rate of return is  an imphcit function of the net present value and therefore does not provide a direct functional form to apply the framework. Hillier (1963) proposed a method to develop the cumulative distribution function for internal rate of return utihzing its definition. A number of authors have since discussed this method for applications (Bonini, 1975; Davidson and Cooper, 1976; Wagle, 1967; Zinn et al., 1977). T h e analytical method develops the expected value, standard deviation and cumulative distribution function for internal rate of return by using a variation of the method suggested by Hillier (1963).  Chapter 6. The Analytical Method  142  Initially, first four moments for net present value at a discount rate, r — 0.01, are evaluated. Using these first four moments a Pearson type distribution is approximated for net present value. (The author's experience is that it is always possible to approximate a Pearson distribution for net present value because the first four moments for net present value, discounted project revenue and cost are exact. However, the default is Hillier's approach.) NPV  < 0\r is obtained.  From this distribution the probability for  This is the probability that IRR  <  r.  Summarizing in  equation form (equation 9 from Hillier, 1963),  P{IRR  <  r)  =  P(NPV  <  0\r)  (6.21)  T h e cumulative distribution function for internal rate of return is developed, by repeating the above process while incrementing the discount rate by 0.01, until the range 0 <  P(IRR  <r)  <  1 is obtained from equation (6.21). T h e n using the 2.5%, 5%,  50%, 95% and 97.5% values of the developed cumulative distribution function, the expected value and standard deviation for internal rate of return are computed from equations (2.5) to (2.11). Hillier (1963) approximated the cumulative distribution functions for net present value to the normal distribution to develop the cumulative distribution function for internal rate of return. T h e cumulative distribution function for internal rate of return was also approximated to the normal distribution to obtain the expected value and the standard deviation for internal rate of return.  Inyang (1983) showed that the  assumption of normality made by Hillier (1963),(1969) and Wagle (1967) is in error because skewness develops for situations where input variables are skewed; response of the decision criterion to changes in input variables are non-linear; input variables are insufficient; discontinuity in cash flow occurs (staged construction).  Chapter 6. The Analytical Method  143  The analytical method utilizes the first four moments for net present values at different discount rates to approximate Pearson type distributions in developing the cumulative distribution function for internal rate of return, thus allowing for the treatment of skewness. Also, since equations (2.5) to (2.11) are used to compute the expected value and standard deviation for internal rate of return, there is no necessity to approximate the developed cumulative distribution function to a normal distribution. The upper and lower bounds for the project net present value at minimum attractive rate of return and the project internal rate of return are computed when the transitional correlation p — 1 and p = 0 respectively. Typical examples of bounds for the project net present value and the project internal rate of return are depicted in figures (6.7) and (6.8).  6.5  Discussion  Cooper and Chapman (1987) state that four moment methods (those using the first four moments of primary variables to calculate the first four moments of a derived variable) forriskanalysis achieve a large increase in generality over two moment methods (mean and variance) and are more versatile because: they allow primary variable distributions to be quite general; the moments are related to the distributions shape characteristics; computational requirements are modest. However, they question the computational accuracy of the four moment methods because: calculations for the first four moments of a derived variable require the central moments of primary variables higher than the fourth order, which can be numerically significant; and the restrictions generally imposed on the possible forms of interdependence relationships between primary variables.  Chapter 6. The Analytical Method  Longest Path  F  0.8  z  /  (P =0.0) All Paths  V  t  0.6  g'  0.4  /  rt  0.2 /  /  /  /  /  /  /  /  / / /  •  /  /  /  '  s  '  '  /  (P-1.0)  Qu  q  /  /  /  /  Net Present Value ($)  Figure 6.7: Bounds for the Project Net Present Value  Longest Path  o 13  (P =0.0) All Paths  CC  0.8  (p=1.0)  /  /  V  rr £  t5 o '2  /  0.4  ./ •y  /  /  /  /  /  s i  _  Discount Rate (%)  fc. 0.2 ri p  Figure 6.8: Bounds for the Project Internal Rate of Return  Chapter 6. The Analytical Method  145  This section will discuss how some of the issues raised by Cooper and Chapman (1987) affect the analytical method, and what can be done to increase computational accuracy where possible. In addition, this analytical solution is compared to that which can be obtained from the currently available moment analysis approach (standard approach) to show the improvement of the derivation.  6.5.1  Computational Accuracy  The first four moments for derived variables at project performance and decision levels computed by the analytical method are exact because of the linear functional forms. Therefore, at these two levels only the first four moments of primary variables are required. However, at work package/revenue stream level the issue raised by Cooper and Chapman (1987) regarding higher order moments are valid because general functional forms are permitted for derived variables. The second, third and fourth central moments for the derived variables require up to fourth, sixth and eighth order moments of primary variables. Since the framework considers moments up to the fourth order, the approximation for the second central moment has considered the necessary central moments of primary variables. As all the primary variables are approximated to Pearson type distributions it is possible to generate moments up to the eighth order from the recurrence property of the Pearson family (Kendall and Stuart, 1969 - see Appendix A.6). Then the approximations for third and fourth central moments for a derived variable can consider the necessary central moments of primary variables. However, until more practical experience is gained in the elicitation of subjective probabilities from experts, it is prudent to use only the first four moments for primary variables.  With experience, higher order  moments of primary variables can be included in the approximations for third and fourth central moments of a derived variable.  Chapter 6. The Analytical Method  146  The question whether the fifth and higher order central moments of primary variables are numerically significant in the approximations for the third and fourth central moments is neither proved nor disproved in the literature, possibly because of the difficulty of the exercise.  After a rigorous theoretical study, Tukey (1954) concluded  that the approximations for first four moments of a derived variable are much better than seems to be usually realized. His study used terms up to the fifth order. When generalized four moment methods are suggested for risk analysis, primary variables are assumed to be statistically independent (Siddall, 1972; Jackson, 1982). The variable transformation approach used by the analytical method treats hnear correlations at all levels of the project economic structure in a consistent manner. The concern raised by Cooper and Chapman (1987) regarding treating interdependencies between primary variables is overcome to the extent that the analytical method treats the correlation information that is generally available during feasibihty analysis.  6.5.2  Standard Approach  In the fourth chapter, the variable transformation method was compared numerically to the standard approach to show that it treats correlation information more consistently at the work package/revenue stream level. Similarly, when the solution for derived variables at the project performance level using standard approach is compared, it is evident that the analytical method using variable transformation treats hnear correlations accurately and consistently. The correlations between primary variables at project performance level (i.e derived variables at work package/revenue stream) are restricted to hnear correlations from section (4.3). Assuming that there are no non-hnear correlations between primary variables at project performance level and using equation (6.9) as flr(X), the first four moments for a derived variable from the standard approach are,  Chapter 6. The Analytical Method  E[Y]  =  £ E[Xi]  p (Y)  = £ p (Xi) i=l  p (Y)  «  2  3  3  147  £  (6.22)  + 2£ Y i=l j=i+l  (6.23)  coviX^Xj)  (6.24)  MXi)  i=i n  where E[Xi], p (X{), 2  p (X{), 3  p (Xi) 4  TI  (6.25)  are the first four moments of the i  th  primary  variable at project performance level. Consider an engineering project consisting of five work packages, with expected values, standard deviations and shape characteristics for work package costs as shown in Table 6.1.  The correlation matrix for work package costs is R  w p c  -  Table 6.2  shows the first four moments and shape characteristics for project cost computed by the analytical method (equations 6.11 to 6.14), standard approach (equations 6.22 to 6.25), and when work package costs are assumed to be statistically independent (Siddall, 1972). Table 6.1: Statistics for Work Package Costs W.P # 01 02 03 04 05  E[WPC] 107.40 194.82 305.55 411.10 492.60  ft  0~wpc  43.67 22.92 28.32 50.78 37.76  0.5 -0.8 0.6 0.7 -0.6  2.2 2.8 2.4 2.5 2.4  Chapter 6. The Analytical Method  148  "1.00  0.41  0.58  0.67  0.51"  0.41  1.00  0.28  0.48  0.39  0.58  0.28  1.00  0.61  0.60  0.67  0.48  0.61  1.00  0.48  .0.51  0.39  0.60  0.48  1.00.  Table 6.2: First Four Moments and Shape Characteristics for Project Cost Moments E[PC] p (PC) 2  PA{PC)  02  Analytical Method 1511.47 21184.46 1018621. 1202791440. 0.3303 2.6801  Standard Approach 1511.47 21184.46 105012. 149342432. 0.0341 0.3328  Statistically Independent 1511.47 7239.68 105012. 149342432. 0.1705 2.8493  The expected value (equations 6.11 and 6.22) and second central moment (equations 6.12 and 6.23) for Y are identical, indicating that hnear correlation is treated accurately by the analytical method because equations (6.22) and (6.23) are exact when g(X) is hnear (Kendall and Stuart, 1969). Since third and fourth central moments for Y from the standard approach do not contain any hnear correlation terms, they are same as when the primary variables are assumed to be statistically independent (Siddall, 1972). Where as, the third and fourth central moments computed from the analytical method contain the hnear correlations because the variable transformation ensures that they are included in equations (6.13) and (6.14). When primary variables are statistically independent and the number of variables is large, from the central limit theorem the derived variable should approach normality. Even with five work package costs the shape characteristics for project cost for  Chapter 6. The Analytical Method  149  the independent case are close to a normal distribution. When shape characteristics for project cost from the analytical method and standard approach are compared, those from the standard approach do not reflect the skewness of the work package costs, and the kurtosis is in the impossible range for a distribution. Those from the analytical method reflects skewness and kurtosis because it has included the hnear correlation between the work package costs.  6.6  Summary  This chapter combined all of the developments and studies done in the previous chapters with the project economic structure to propose an analytical method for time and economic risk quantification during feasibility analysis for large engineering projects. The method computes the first four moments of derived variables at work package/revenue stream level (work package duration, cost and net revenue), project performance level (project duration, cost and revenue) and project decision level (net present value) using the moments of primary variables in their functional forms. The shape characteristics of the derived variables are used to approximate Pearson type distributions for them to quantify their uncertainty. The bounds for derived variables are obtained when transitional correlation p — 1 and p = 0. The computed moments for derived variables at project decision and project performance level are exact. The approximations for moments are only for the derived variables at the work package/revenue stream level. The expected value, standard deviation and cumulative distribution function for project duration are obtained from modified PNET while those for project internal rate of return are obtained from a variation of Hillier's method. The concerns raised by Cooper and Chapman (1987) regarding the computational accuracy of the four moment method and treatment of interdependence between primary variables have been discussed with suggestions for  Chapter 6. The Analytical Method  150  further improvements. One of the objectives of this research is to computerize the analytical method to explore its behavior, to validate it and to test its practicality in the measurement of uncertainty of performance and decision parameters. The source code for the analytical method is available in a file called TIERA (Time and Economic Risk Analysis). It has been developed as a generalized numerical processor that has the flexibility to model general functional forms for work package durations, costs and revenue streams. See Appendix D for more details. The developed method, while providing a consistent analytical approximation to T  a problem that has long relied on Monte Carlo simulations for solutions, shows that it is more appropriate for time and economic risk quantification of large engineering projects. It includes the features of a good simulation model such as: interaction of time, cost, and revenue by using a precedence network; performing sensitivity and probability analysis; treating multiple paths in network analysis; treating correlation between variables at the input level; and the quantification of risks of decision variables by developing cumulative distribution functions. In addition, it overcomes most of the constraints that exist during feasibility stage for realistic modeling of an engineering project by: requiring expert judgements as input; treating correlation between primary variables and between derived variables at all levels; obtaining intermediate milestone information necessary to set realistic targets for performance; permitting the use of unlimited number of variables to model a project; estimating bounds for decision variables; and above all having the capability to evaluate a range of alternatives economically to select the most suitable strategy to develop a project.  Chapter 7 Validations and Applications 7.1  General  The analytical method to estimate bounds on and to quantify the uncertainty in time and economic risks for large engineering projects was developed in the previous chapter. This chapter describes validation and applications of the analytical method. In most of the examples presented in this chapter it is difficult to separate the vahdation studies from the apphcations. Therefore, it will be helpful to the reader if the results from the analytical method are viewed as apphcations and those from Monte Carlo simulations are viewed as validations. Monte Carlo simulations are used to validate the analytical method because at present, simulation based models are considered to be the "state-of-the-art" for quantification of time and economic risks in large engineering projects (Cain, 1980; Diekmann, 1985; Flanagan et al., 1987; Hayes et al., 1986; Jaafari, 1988a; Newendorp, 1976; Perry and Hayes, 1985b; Thompson and Wilmer, 1985). When the variables are uncorrelated, a successful vahdation should demonstrate that given the same problem structure, primary variables and probabihty distributions, the quantified uncertainty of time and economic variables from the simulation lie within the upper and lower bounds approximated from the analytical method. Since, the analytical method treats correlations efficiently, correlations must be  151  Chapter 7. Validations and Apphcations  152  treated in the simulation process to permit comparisons for validations. The treatment of correlations in Monte Carlo simulations is a non-trivial task (Johnson, 1987). Even though a number of methods have been suggested for treating correlations in simulations, no method has been validated rigorously (eg. compared to known analytical solutions) to be considered as a bench mark for these validations. Nevertheless, a method which the author considers as the best approximation for treating correlations between variables in simulations is adopted. However, rigor in the validations similar to that of the uncorrelated situations cannot be achieved. The next section contains a brief description on Monte Carlo simulation, the theoretical basis for the method used to include correlations between primary variables, and the "acceptable" number of iterations for the simulation. In the third section, the modified PNET algorithm is applied to the two numerical examples presented by Ang et al. (1975). The first is a road pavement project, while the second is an industrial building project. The apphcations show that the modified PNET algorithm which is based on the precedence network reproduces the results obtained by Ang et al. (1975) using the arrow network, thereby validating the modified algorithm. The flowchart for the modified PNET algorithm is illustrated in Appendix D. Sections four to six describe the validation studies that were performed. In the fourth, a parallel network of identical work packages is used to validate the simulation process. This is the first of the two hmiting cases that are used to validate the Monte Carlo simulation process. The fifth section uses data from an actual deterministic feasibility analysis as the first example to validate the analytical method. The first example contains the second limiting case for the Monte Carlo simulation and four simulations to validate the analytical method. The second hmiting case is a single  Chapter  7.  Validations  and  Apphcations  153  dominant path of a highly interrelated precedence network. In the first two simulations, low coefficients of variation for work package durations are used. In addition to the vahdation, this permits a realistic comparison with the deterministic study. The third and fourth simulations use the same numerical example with high coefficients of variations for work package durations. Since derived economic variables are dependent upon the start times, this increase permits the study of the effect of high variance on the quantification and bounding of their risks. In the sixth section the second example that is used for the vahdation is presented. It is a hypothetical engineering project developed to demonstrate the full potential of the analytical method. Two complete simulation were performed. The first assumed that all the primary variables are uncorrelated, while the second assumed that the primary variables at the input level are correlated. This is the correlation treatment that can be duphcated by simulation. The example is extended to a third level where correlations at all levels of the project economic structure are treated. In the seventh section, the different ways in which the analytical method can perform sensitivity analysis are explored. This discussion outlines how one of the sensitivity analyses can be used to distribute the contingency allocated to a derived variable at a desired probabihty of success, to its primary variables. The current dollar estimate for project cost is used as the derived variable.  7.2  Monte Carlo Simulation  Conceptually, performing a Monte Carlo simulation is simple. It requires a deterministic model, identification of the random variables, a probabihty distribution for each random variable, a random number generator, and then a sample value from each distribution for each iteration using a random number from the uniform distribution on the interval [0.1], (i.e £7(0,1)), as the entry point in a cumulative distribution function  Chapter 7. Validations and Apphcations  154  of the variables (see figure 7.1). The larger the number of iterations, the more reliable are the results from the simulation (Cain, 1980; Eilon and Fowkes, 1973; Flanagan et al., 1987; Inyang, 1983; Jaafari, 1988a; Johnson, 1987; Hertz, 1964; Hull, 1977, 1980; Kalos and Whitlock, 1986; Kryzanowski et al., 1972; Newendorp, 1976; Riggs, 1989; Van Tetterode, 1971). The procedure described above however, implies that each random variable is independent of the others. In the current problem most variables are dependent (Cooper and Chapman, 1987; Inyang, 1983; Perry and Hayes, 1985b).  7.2.1  Treatment of Correlations  The importance of treating correlations between variables in Monte Carlo simulations has been long recognized (Eilon and Fowkes, 1973; Inyang, 1983; Hertz, 1964; Hull, 1977, 1980; Kryzanowski et al., 1972; Newendorp, 1976; Thompson and Wilmer, 1986; Van Tetterode, 1971). None of the suggested methods however, has been rigorously validated. After an extensive review of the available techniques, Inyang (1983) proposed the following approach to model correlations in Monte Carlo simulations for risk analysis of engineering projects. 1. Random numbers are generated for each of the variables that make up the risk analysis model. A column of random numbers is thus generated. 2. The correlation factors between variables have to be input as a matrix. The random numbers are modified depending on the correlation with each other. Any type of correlation factor (total, partial or no correlation) can be handled. 3. The value of a variable is obtained depending on the value of its modified random number as a result of the correlation between the variables. The author agrees with Inyang (1983), that the above procedure is the most suitable approach to model correlations in simulations. However, two shortcomings have to be highlighted. First, the algorithm used for the modification of random numbers  Chapter 7. Validations and Apphcations  155  was not derived in the thesis by Inyang (1983). Second, the correlation matrices elicited for the simulation have to be positive definite (see section 4.2). The process that was used for the validation model is based on the above procedure (Inyang, 1983). However, since it is not known whether the method for treating correlations in the simulation overestimates or underestimates the effects of correlation, the simulation results provide only an approximate bench mark for the analytical treatment of correlation. The possibility thus exists that the simulation results may not be contained within the upper and lower bounds predicted by the analytical method. The random numbers were modified by extending the algorithm developed by Van Tetterode (1971) to the multivariate situation. The random number correction is pairwise. Since the positive definite correlation matrix is used to modify the random numbers assigned to the primary variables, the multivariate situation is recognized. The random number correction is as follows (Van Tetterode, 1971).  RNij  = RNj + ay (RNi -  where RNi and RNj are the i  th  and j  RNj)  (7.1)  random numbers in the column generated  th  from U(0,1), (step 1, Inyang, 1983), RN^ is the ij  th  random number corrected for  the correlation between variables i and j in the matrix of corrected random numbers, and  is the correction factor given by,  ±±^IZA 2  where  Pij -  .  (7 2) 1  is the correlation coefficient between variables i andj. The correction factor  is lies in the interval,  0 < a,j < 1 (see figure 7.2 and Appendix E for proof)  for all correlation values. The modification from equation (7.1) and (7.2) ensures  156  Chapter 7. Validations and Apphcations  that the corrected random numbers are within the interval [0,1]. A small numerical example is presented to demonstrate the random number modification process. Assume a three variable model having the following correlation matrix, R, where  R  1.0  -0.48  0.42  -0.48  1.0  -0.69  L 0.42  -0.69  1.0 J  and the column of random numbers generated from U(0, 1) as [ 0.32 0.75 0.14] . r  Then, the matrix of ctij values from equation (7.2) and the matrix of random numbers corrected for the correlation values from equation (7.1) are given below.  1.(1 a  0.35365  0.35365 0.31638 1.0  0.31638 0.48804  0.48804  0.32 RN  1.0  0.4721  0.5979 0.1969 0.75  0.2631 0.4523  0.4377 0.14  For each iteration, matrix R N is computed from the generated column of random numbers. Then, a row selected from the matrix R N at random can be used as the random numbers for that iteration of the simulation.  7.2.2  The Number of Iterations  The literature is diverse on the number of iterations that should be performed for an "acceptable" simulation (Flanagan et al., 1987; Jaafari, 1988; Inyang 1983; Perry and Hayes, 1985b). The recommended numbers range from 100 to 1000 iterations. However, most of these recommendations are not supported theoretically or empirically, and may not be applicable in all situations (Inyang, 1983).  Chapter 7. Validations and Applications  1  RN  0  X  Figure 7.1: Random Variate Generation  Correction Factor - or Figure 7.2: The Correction Factor a for Different Values of p  Chapter 7. Validations and Apphcations  158  Bury (1975) has shown that a simulation of 1000 iterations has an error band of 4.3% at 95% confidence level. Error band is the accuracy to which the cumulative distribution function generated from the simulation approximates to the unknown cumulative distribution function of the derived variable. That is, the error band brackets the unknown cumulative distribution function in 95% (or (1 — a) 100%) of all simulation samples. At 95% confidence level, for an error band of 2% at least 4600 iterations are required. Inyang (1983) states that at 95% probability, 1000 iterations will give a level of accuracy of 6% and 8.5% for the expected value (mean) and the standard deviation respectively. Since simulation generates a random sample to represent the derived variable, irrespective of the number of primary variables, the larger the size of the sample the more accurate are the estimates for the expected value, standard deviation and the cumulative distribution function generated from simulation. In this thesis, when duration was the only derived variable 15,000 to 20,000 iterations were used for the simulation. For complete time and economic risk quantification 4,000 to 6,000 iterations were used. Larger simulations were used for duration because of its smaller problem structure and because of its importance as the linking variable in economic risk quantification. The comparatively large size of the simulations also permits the study of the stabihty of the expected value and standard deviation with increasing number of iterations.  7.3  Modified P N E T Algorithm  The modified PNET algorithm is applied to the two numerical examples that were presented by Ang et al., (1975). The first example is a road pavement project, while the second is an industrial building project.  Chapter 7. Validations and Apphcations  7.3.1  159  Road Pavement Project  This project involves the paving of 2.2 miles of roadway pavement and the construction of appurtenant drainage structures, excavation to grade, placement of macadam shoulders, erection of guardrails, and landscaping (Ang et al., 1975). The precedence network for the project used by the modified PNET, based on the logic of the arrow network given by Fig.2 of Ang et al. (1975) is shown in figure (7.3). The various activities of the project, respective mean durations and standard deviations for the activities from Table 1 of Ang et al. (1975), are given in Appendix F. Table 2 from Ang et al., (1975), containing all nine paths of the network arranged in order of decreasing mean path durations, mean path durations (pr) and standard deviations (<TT)  a r e  listed in Table 7.1. The nine paths, mean path durations and  standard deviations from the modified PNET algorithm are given in Table 7.2. Table 7.1: Ordered Paths and Duration Statistics - Table 2, Ang et al., (1975) Path  #  1 2 3 4 5 6 7 8 9  Activities in the Path •4, 7,-12, 13, 18, 20, 22, 25, 27 6, 10, 15, 19, 21, 23, 24, 26, 27 6, 11, 16, 19, 21, 23, 24, 26, 27 5, 9, 14, 19, 21, 23, 24, 26, 27 5, 8, 13, 18, 20, 22, 25, 27 3, 28, 20, 22, 25, 27 3, 1, 23, 24, 26, 27 2, 17, 28, 20, 22, 25, 27 2, 17, 1, 23, 24, 26, 27  PT  0~T  days 61 57 52 49 42 29 29 28 28  days 5.00 9.00 7.94 6.54 4.00 3.24 5.19 3.16 5.12  The dummy activities required for the arrow network (activities 1 and 28) are not necessary for the precedence network used by the modified PNET (see paths 6, 7, 8 and 9 in Tables 7.1 and 7.2). In addition to ordering the paths in decreasing mean durations, the modified PNET orders the paths in decreasing standard deviations  Chapter 7. Validations and Applications  £ CM  ^ CM  WP  ^CM  o  £CM  CM  IS  CM  I-  3?  I"  Is  WP  1 WP  :co  CD T—  CL.  Figure 7.3: The Precedence Network for the Road Pavement Project  Chapter 7. Validations and Applications  161  Table 7.2: Ordered Paths and Duration Statistics from Modified PNET Path  #  1 2 3 4 5 6 7 8 9  Ang et al 1 2 3 4 5 7 6 9 8  4, 6, 6, 5, 5, 3, 3, 2, 2,  Activities in the Path 7, 12, 13, 18, 20, 22, 25, 27 10, 15, 19, 21, 23, 24, 26, 27 11, 16, 19, 21, 23, 24, 26, 27 9, 14, 19, 21, 23, 24, 26, 27 8, 13, 18, 20, 22, 25, 27 23, 24, 26, 27 20, 22, 25, 27 17, 23, 24, 26, 27 17, 20, 22, 25, 27  PT  0~T  days 61 57 52 49 42 29 29 28 28  days 5.00 9.00 7.93 6.59 4.00 5.17 3.24 5.12 3.16  when mean path durations are equal. This ensures the selection of the path with the highest variance as the representative path from the paths having the same mean duration (see paths 6, 7, 8 and 9 in Tables 7.1 and 7.2). The representative paths for the transitional correlation p = 0.5 are paths 1 and 2 from PNET (Ang et al., 1975) and the modified PNET. The comparison shows that modified PNET identifies the paths correctly, evaluates the expected value (mean) and standard deviation for path durations accurately, and selects the representative paths correctly. The ordering of paths may differ because the modified PNET gives priority to the path with the higher variance when the mean durations are identical.  7.3.2  Industrial Building Project  This project involves the construction of a single-story industrial building. The building is comprised of reinforced concrete piers, frost walls, structural steel columns, and a precast roof (Ang et al., 1975). The precedence network for the project used by the modified PNET, based on the logic of the arrow network given by Fig.5 of Ang et al. (1975) is shown in figure (7.4). The various activities of the project, respective mean durations and standard deviations for the activities from Table 3 of Ang et al.  Chapter 7. Validations and Applications  JT 4fc co  ^  CO  3 "  J3.  I  *8  sj. CO ^fe CM ^fe CM  =tfecU  *J3 CM ^ CM *  ICM"  I?i  8  I T * 8  JT  • 1  Ife £  in  zr  1  *  CO  I  CD  ? 1  CO  TT.,  =tfe co  Ife u)  I =*fe CO  =tfe CVJ  =*fe CO  I3 Ife  T-  -  I3Z  3fe  g  Figure 7.4: The Precedence Network for the Industrial Building Project  Chapter 7. Validations and Applications  163  (1975), are given in Appendix F. Ang et al. (1975), listed only the first ten paths arranged in decreasing mean path durations (Table 4, Ang et al., 1975). Table 7.3 lists all 33 paths in the project network as ordered by the modified PNET algorithm. The second column in Table 7.3 contains the path numbers of the ten paths listed in Table 4, Ang et al., (1975). Even though path 7 from modified PNET had the largest variance of the paths with mean duration of 66 days, PNET had not considered it as a major path. The representative paths for the transitional correlation p = 0.5 are paths 1, 3 and 5 from PNET (Ang et al, 1975), while the modified PNET algorithm identifies paths 1, 3, 5, and 32. PNET considered only thefirstten paths as the major paths. Even though path 32 is also a representative path by definition, it does not play a role in the completion time probability calculations because its mean path duration is insignificant when compared to the other representative paths. While PNET neglects those paths with low mean path durations, the modified PNET considers all the paths in the selection of representative paths. As shown later in the validations of the analytical method, the difference in execution time for the modified PNET routine to evaluate a single path (longest path approach) or all the paths in the project network is negligible. The two comparisons validate the modified PNET algorithm used in the analytical method for time and economic risk quantification.  7.4  Parallel Network  A parallel network consisting of thirty five identical work packages infiveparallel paths as shown infigure(7.5) is used as thefirstHmiting case to validate the Monte Carlo simulation process. Since simulations are used to validate the analytical approach, it is essential to validate the simulation process first.  Chapter 7. Vahdations and Apphcations  164  Table 7.3: Ordered Paths and Duration Statistics for the Industrial Building Path  #  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  Ang et al 1 2 3 4 5 6 -  8 9 10 7 -  -  -  Activities in the Path 17, 18, 32 33, 35 17, 18, 32 34, 35 9, 13, 15, 20, . ., 28, 36 9, 13, 15, 20, .. , 27, 31, 35 1, 2, 5, 7, 8, 10, 13, 15, 20, 28, 36 1, 3, 4, 5, 7, 8, 10, . .., 13, 15, 20, 28, 36 9, 12, 14, 18, 32, 33, 35 9, 13, 15, 20, .. ., 24, 29, 30, 36 1, 2, 5, 7, 8, 10, . . , 13, 15, 20, 27, 31, 35 1, 3, 4, 5, 7, 8, 10, . .., 13, 15, 20, 27, 31, 35 1, 3, 6, 7, 8, 10, 13, 15, 20, 28, 36 1, 3, 6, 7, 8, 10 13, 15, 20, 27, 31, 35 9, 12, 14, 18, 32, 34, 35 1, 3, 4, 5, 7, 8, 10, . .., 12, 14, 18, 32, 33, 35 1, 2, 5, 7, 8, 10, . . , 12, 14, 18, 32, 33, 35 1, 3, 4, 5, 7, 8, 10, . .., 13, 15, 20, 24, 29, 30, 36 1, 2, 5, 7, 8, 10, 13, 15, 20, 24, 29, 30, 36 1, 3, 6, 7, 8, 10, 12, 14, 18, 32, 33, 35 1, 3, 6, 7, 8, 10, 13, 15, 20, 24, 29, 30, 36 1, 3, 4, 5, 7, 8, 10, . .., 12, 14, 18, 32, 34, 35 1, 2, 5, 7, 8, 10, 12, 14, 18, 32, 34, 35 1, 3, 6, 7, 8, 10, . . , 12, 14, 18, 32, 34, 35 1, 3, 4, 5, 7, 16, 20, 28, 36 1, 2, 5, 7, 16, 20, .. , 28, 36 1, 3, 4, 5, 7, 16, 20, 27, 31, 35 1, 2, 5, 7, 16, 20, .. ., 27, 31, 35 1, 3, 6, 7, 16, 20, .. , 28, 36 1, 3, 6, 7, 16, 20, .. ., 27, 31, 35 1, 3, 4, 5, 7, 16, 20, 24, 29, 30, 36 1, 2, 5, 7, 16, 20, .. , 24, 29, 30, 36 1, 3, 6, 7, 16, 20, .. ., 24, 29, 30, 36 19, 33, 35 19, 34, 35 Includes all intervening activities  PT  days 78 76 69 68 67 67 66 66 66 66 66 65 64 64 64 64 64 63 63 62 62 61 59 59 58 58 58 57 56 56 55 38 36  0~T  days 12.20 12.20 12.12 12.14 3.85 3.85 12.25 12.09 3.87 3.87 3.85 3.87 12.25 4.22 4.22 3.79 3.79 4.22 3.79 4.22 4.22 4.22 3.73 3.73 3.75 3.75 3.73 3.75 3.67 3.67 3.67 6.14 6.14  Chapter 7. Validations and Applications  165  1* "c il  i Zl  CO  •5 *  Q_ CD  •5 *  t  • CO  •5 *  ~] CM  •5 *  Q_ CO CM  r•5 *  •5 *  Q_ CO  Q_ CO  Bi *  Bi *  a.  *-  3i *  ^  CO  B: *  QL  , _  Bi *  i  1  Q_ CM  Q.  B: *  Si *  o_  Bi  $ *  *  CO  ^ CM  ^  CO  5  cn  5 5  co  Bi *  B= *  • CO  Bi  5  $ *  t i I I 1 r  Figure 7.5: The Parallel Network  Q_ l O  r- CM •5 *  QL  O  ~] CM  B= *  Q_  lO  Q_  O  Bi *  Chapter 7. Validations and Apphcations  166  From the longest path (or PERT) approach every path in the parallel network is a critical path. Therefore, the cumulative distribution function for project duration is the cumulative distribution function from any path duration. This is the lower bound for completion time probabihty. From the modified PNET algorithm, completion time probabihty for project duration for any transitional correlation p (i.e 0 < p < 1) is the same as the upper bound (p = 1). Therefore, the cumulative distribution function for project duration for the parallel network from a valid Monte Carlo simulation process should give the same cumulative distribution function as for the upper bound from the PNET algorithm. The expected value, standard deviation, skewness and kurtosis for duration for all the work packages are E[WPD]  — 3.644 months, CTWPD = 0.67 months, y/fa — 0.3  and 82 = 3.5. The statistics for project duration from the longest path (lower bound p = 0), for any transitional correlation, 0 < p < 1, and the expected value and standard deviation from the simulation are given in Table 7.4. Table 7.4: Statistics for Project Duration for First Limiting Case Project Duration (months) Longest Path (p — 0) When 0 < p < 1 Monte Carlo Simulation  Expected Value 25.51 27.63 27.60  Standard Deviation 1.76 1.24 1.30  y/Fi  fa  0.11 0.30  3.07 3.2  The cumulative distribution functions for project duration from the longest path, when 0 < p < 1 and a Monte Carlo simulation of 20,000 iterations is depicted in figure (7.6). This simple hmiting case, while validating the Monte Carlo simulation process that is used to vahdate the analytical method, also confirms the theoretical postulations made by the modified PNET algorithm.  Chapter 7. Validations and Applications  20  22  24  26  28  30  32  Duration (months)  Figure 7.6: CDFs for Project Duration for the Parallel Network  34  Chapter 7. Validations and Apphcations  7.5  168  First Example  This section demonstrates the second hmiting case to validate the Monte Carlo simulation and the first two validations of the analytical method. The data for this example is obtained from an actual deterministic feasibility analysis conducted for a mineral project in South America. The starting point for the analysis is at the work package level. For study purposes herein, the original construction program is modified as shown in figure (7.7). The logic of the original program is maintained throughout. The work package durations are developed to correspond to the modified construction schedule. The deterministic estimates and statistics for work package durations are given in Appendix F.  7.5.1  Second Limiting Case  The precedence network depicted in figure (7.7) is highly interrelated. However, if there is one dominant path in the network then that path will dominate completion time probability of the project. Therefore, the project duration from the longest path (lower bound), all the paths (upper bound) and from the simulation should be similar. Such a path can be created by changing the statistics for duration for work package #7 to E[WPD]  = 20.01 months,  <T  WPD  = 1.609 months, v^i = 0.2 and 8 = 2.6. 2  Then the dominant path consists of work packages #2, #7, #20, #24, #30, #31. The expected value, standard deviation, skewness and kurtosis for project duration for the dominant path (lower bound p = 0), from all paths (p = 1) and the expected value and standard deviation from simulation are given in Table 7.5. The cumulative distribution functions for project duration from lower and upper bounds, and a Monte Carlo simulation of 15000 iterations are depicted in figure (7.8). This limiting case also validates the Monte Carlo simulation process. In addition, it  Chapter 7. Validations and Applications  169  Ft  CM  CO CM  Q_  0.  >  CM  CO CM  8  Q.  OL  o CO OL  I  t  Si OL  I CM  CO  if)  OL  Q_  0.  CO  CO  a.  OL  CO  0) 0_  O CM OL  CM  CM CM  0-  OL  CM CO OL  0-  OL  CO  CO OL  CO  Q.  0.  OL  5  CM OL  0. CO  Figure 7.7: The Project Network for the First Example  Chapter 7. Validations and Applications  38  40  42  44  170  46  48  50  Duration (months)  Figure 7.8: CDFs for Project Duration for the Single Dominant Path  52  Chapter 7. Validations and Applications  171  Table 7.5: Statistics for Project Duration for Second Limiting Case Project Duration (months) Longest Path (p — 0) All the Paths (p = 1) Monte Carlo Simulation  Expected Value 45.01 45.01 44.96  Standard Deviation 1.87 1.88 1.53  ft  0.15 0.1  2.85 2.8  confirms accuracy of the modified PNET algorithm.  7.5.2  First Validation  Two simulations were done as the first validation. The derived variable for the first simulation was only project duration. For the first validation, low coefficients of variation for work package durations are assumed. Table 7.6 contains the expected values and standard deviations for project duration from the simulation at 1000 iteration intervals, and the statistics evaluated from the analytical approach at different transitional correlations. Figure (7.9) illustrates the cumulative distribution functions for upper and lower bounds approximated from the analytical method and that generated from a simulation of 15,000 iterations. Figure (7.10) depicts, in addition to those infigure(7.9), the cumulative distribution functions for project duration at different transitional correlation (p) values. The second simulation is a complete time and economic risk quantification. However, the statistics and cumulative distribution function generated for project duration are not considered because thefirstsimulation is much larger. The work package costs of the project network depicted byfigure(7.7) are estimated such that the sum of the work package costs is equivalent to the constant dollar cost estimate of the deterministic feasibility analysis. The deterministic estimates for work package costs are given in Appendix F.  Chapter 7. Validations and Apphcations  172  Table 7.6: Statistics for Project Duration from First Validation - Ex #1 Simulation  #  E[PD] mths  1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000  37.29 37.30 37.28 37.27 37.27 37.26 37.24 37.25 37.25 37.25 37.25 37.25 37.25 37.26 37.26  Analytical Method E[PD] mths  P  0~PD  mths  1.11 1.15 1.40 1.51 1.56 1.59 1.61 1.63 1.65 1.66 1.66 1.67 1.51 1.34 1.19  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  <?PD  mths  36.08 36.11 36.11 36.92 36.92 36.92 37.31 37.31 37.74 37.95 38.34  1.30 0.13 2.95 1.27 0.2 2.8 0.2 1.27 2.8 1.03 0.3 3.2 1.03 0.3 3.2 1.03 0.3 3.2 1.02 0.3 3.3 1.02 0.3 3.3 0.91 0.4 3.4 0.82 0.5 3.5 0.68 0.5 3.4  The function for discounted work package cost (WPCi) used for the analysis is as follows. WPd  =  f e^-^d  f  C i  C (r)  dr  oi  (7.3)  Jo +(1  - f^-v^e **** 6  [  Coi(T)e^- ^dT  Tci  r  Jo  where WPCi is the discounted i cash flow for the i  th  th  work package cost,  work package, Ts  ci  C i(r) 0  is the constant dollar  and Ta are work package start time and  duration, T is the time at which the repayment of interim financing is due for all p  work packages (assumed as the end of the construction phase), / is the equity fraction, and 6c , r and y are inflation, interest and discount rates respectively. The time r is t  measured from the start of the i  th  work package.  Chapter 7. Validations and Apphcations  173  The function for revenue streams (NRSi) is as follows. R {t)e ' - ^ 6R (t  oi  where NRSi is the discounted i  th  Ts  )  -  M (t)e*"i K  (7.4)  e~ dt yt  revenue stream, Roi(t) and Moi(t) are the constant  dollar cashflowfor the i gross revenue and operation and maintenance cost, Ts  Ri  and TR{ are start time and duration of the revenue stream, and 6^, 9M and y are {  inflation and discount rates respectively. The deterministic values for the respective primary variables (i.e work package durations and costs, annual revenues and operating costs, inflation and financing rates) are assumed to be the median values of their frequency distributions. The expected value, standard deviation, skewness and kurtosis for work package durations, costs, annual gross revenues and operation and maintenance costs for the revenue streams are given in Appendix F. For illustrative purposes herein, uniform constant dollar expenditure profiles for work package costs and annual operating costs were assumed. Similarly, uniform constant dollar revenue profiles were assumed for gross annual revenue streams. A common inflation rate with the following statistics, E[6c] = 5.837%, cre = 0.395%, \/W\ — c  0.1 and 62 = 2.6 is assumed for all work package costs. A construction loan for 85% (/ = 0.15) of the current dollar expenditure on construction is assumed. The statistics for the interest rate on the construction loan are E[r] = 8.631%, o~ = 0.704%, \f0[ = T  0.0 and 3 = 3.6. The minimum attractive rate of return used for the analysis and 2  vahdations is 20%. All the variables in the analysis are assumed to be uncorrelated. Tables 7.7, 7.8 and 7.9 contain results from the second simulation at 500 iteration intervals and statistics from the analytical method at different transitional correlation values for discounted project cost, discounted project revenue and project net present value.  Chapter 7. Validations and Apphcations  174  Table 7.7: Statistics for Discounted Project Cost from First Validation - Ex #1 Simulation  #  E[DPC]  500 1000 1500 2000 2500 3000 3500 4000  Analytical Method °~DPC $  P  $  87054064 86791152 86764320 86775360 86727216 86785520 86804592 86805648  9588990 9586503 9739846 9789910 9819083 9834827 9757994 9705724  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[DPC] $  °~DPC $  87792088 87767561 87766524 87168757 87168757 87166313 86901802 86900640 86598834 86445916 86130819  9658726 9655823 9655819 9591506 9591506 9591480 9562288 9562167 9529379 9512848 9480193  ft  0.097 0.097 0.097 0.097 0.097 0.097 0.097 0.097 0.097 0.097 0.097  2.617 2.617 2.617 2.617 2.617 2.617 2.617 2.617 2.617 2.617 2.617  Table 7.8: Statistics for Discounted Project Revenue from First Validation-Ex #1 Simulation  # 500 1000 1500 2000 2500 3000 3500 4000  E[DPR)  Analytical Method °~DPR  %  %  143957280 143692784 144041696 144331664 144558464 144623264 144699216 144820096  12268435 12273304 12045838 12135314 12128094 11984054 11889887 11871214  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[DPR]  ft  O'DPR  $  %  147761732 147689326 147689326 146044842 146056516 146056516 145282056 145278674 144429474 144015454 143232846  11912759 11906346 11906346 11790591 11790591 11790591 11740939 11740788 11682179 11652676 11598742  -0.051 -0.051 -0.051 -0.051 -0.051 -0.051 -0.051 -0.051 -0.051 -0.051 -0.051  2.744 2.744 2.744 2.744 2.744 2.744 2.744 2.744 2.744 2.744 2.744  Chapter 7. Validations and Apphcations  175  Table 7.9: Statistics for Project NPV from First Validation - Ex #1 Simulation  # 500 1000 1500 2000 2500 3000 3500 4000  E[NPV]  Analytical Method VNPV  $  $  56903152 56909520 57275088 57546720 57817088 57820480 57875184 57993648  15845556 15262957 15202756 15573732 15527935 15474244 15318487 15290463  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[NPV]  a NPV  $  $  59969644 59921765 59922801 58879177 58887759 58890203 58380254 58378034 57830640 57569539 57102027  15336388 15329579 15329576 15199179 15199179 15199162 15142226 15142032 15075887 15042578 14980150  ft  -0.048 -0.048 -0.048 -0.048 -0.048 -0.048 -0.048 -0.048 -0.048 -0.048 -0.048  2.847 2.847 2.847 2.847 2.847 2.847 2.847 2.847 2.847 2.847 2.847  Table 7.10 contains the expected value and standard deviation for project internal rate of return from the simulation and from the analytical method at different transitional correlation values. The analytical method develops the cumulative distribution function for internal rate of return using cumulative distribution functions for net present value at incremental discount rates. The expected value and standard deviation for internal rate of return are approximated using percentiles from that cumulative distribution function. Figures (7.11), (7.12), (7.13) and (7.14) illustrate the cumulative distribution functions for upper and lower bounds approximated from the analytical method and those generated from a simulation of 4000 iterations for discounted project cost, discounted project revenue, net present value and internal rate of return. The cumulative distribution functions and the estimates for expected values for derived time and economic variables demonstrate that the results generated from Monte Carlo simulation are within the upper and lower bounds predicted by the analytical approximations, thereby validating the analytical method.  Chapter 7. Validations and Apphcations  176  Table 7.10: Statistics for Project IRR from First Validation - Ex #1 Simulation  #  E[IRR]  500 1000 1500 2000 2500 3000 3500 4000  32.71 32.69 32.76 32.81 32.87 32.88 32.88 32.89  Analytical Method &IRR  4.14 3.99 4.01 4.11 4.13 4.11 4.07 4.03  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[IRR]  33.241 33.231 33.231 33.019 33.019 33.020 32.930 32.930 32.836 32.791 32.720  VlRR  4.094 4.091 4.091 4.034 4.034 4.034 4.037 4.037 4.033 4.029 4.020  Table 7.11: Comparison of CPU times from First Vahdation - Ex #1 Simulation CPU Sec. # 500 511 1000 1021 1500 1531 2000 2042 2500 2550 3000 3059 3500 3567 4000 4079  Analytical Method CPU Sec. P 0.0 34.45 0.1 35.03 0.2 35.09 0.3 35.18 0.4 35.56 0.5 34.89 0.6 35.22 0.7 35.75 0.8 35.86 0.9 36.54 1.0 37.78  Chapter 7. Vahdations and Apphcations  177  Table 7.11 contains a comparison of the execution time for simulation and the analytical method. The computational economy of the analytical method is clearly highhghted. For this example, the analytical method is about thirty times faster when compared to the generally recommended number of iterations (1000) for risk quantification using Monte Carlo simulation (Inyang, 1983; Perry and Hayes, 1985b). Both analyses were done on an IBM 3081 mainframe computer. There are seventy three possible paths to complete the project network depicted byfigure(7.7). When p = 0 only the moments on the longest path are considered to evaluate the statistics for project duration. When p = 1 the moments of all 73 paths are considered to evaluate the statistics for project duration. The comparison of the execution times however, show that the time difference to evaluate statistics and cumulative distribution functions for upper and lower bounds from the analytical method are negligible. While 73 paths is not a significant number for a large engineering project, it still demonstrates that evaluating the bounds for an alternative is not an excessive burden in terms of the computational economy when compared to simulation.  7.5.3  Second Validation  The same numerical values as for the previous case are used for the second validation. The only difference is that the coefficients of variation for work package durations are approximately 40% instead of the 3% to 13% used in the previous case. Since the derived economic variables are dependent upon time, this increase permits us to study its effect on their risk quantification. The statistics for the revised work package durations are given in Appendix F. Two simulations were done. The derived variable for the first simulation was only project duration. Table 7.12 contains the expected values and standard deviations from the simulation and the statistics evaluated from the analytical method.  Chapter 7. Validations and Applications  32  34  36  38  40  Duration (months) Figure 7.9: CDFs for Project Duration - First Validation - Ex #1  Figure 7.10: CDFs for Project Duration - First Validation - Ex #1  42  Chapter 7. Validations and Applications  179  Cost (discounted $*10 ) 8  Figure 7.11: CDFs for Discounted Project Cost - First Validation - Ex #1  100  120  140 160 Revenue (discounted $*10 )  180  6  Figure 7.12: CDFs for Discounted Project Revenue - First Validation - Ex #1  Chapter 7. Validations and Applications  0  20  180  40 60 80 Net Present Value ($*10 )  100  120  6  Figure 7.13: CDFs for Project Net Present Value - First Validation - Ex #1  20  25  30  35 40 Discount Rate (%)  45  50  Figure 7.14: CDFs for Project Internal Rate of Return - First Validation - Ex #1  Chapter 7. Validations and Apphcations  181  Table 7.12: Statistics for Project Duration from Second Validation - Ex #1 Simulation  # 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000  E[PD] mths  46.01 45.89 45.93 45.91 45.88 45.87 45.83 45.84 45.83 45.84 45.87 45.89 45.89 45.91 45.91  mths  5.19 5.00 5.08 5.09 5.10 5.11 5.12 5.10 5.02 4.95 4.92 4.90 4.88 4.86 4.85  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  Analytical Method E[PD) °~PD Vft mths  36.31 42.38 42.38 44.12 44.45 45.55 46.01 46.45 46.84 48.61 49.12  ft  mths  6.56 0.12 3.09 5.91 0.7 3.9 5.91 0.7 3.9 5.17 0.8 4.6 5.02 0.8 4.5 4.50 0.8 4.5 4.28 0.9 5.0 4.11 0.8 4.5 3.91 0.8 4.4 3.19 0.7 4.2 2.97 0.8 5.2  Figure (7.15) illustrates the cumulative distribution functions for upper and lower bounds approximated from the analytical method and that generated from a simulation of 15,000 iterations. Figure (7.16) depicts, in addition to those in figure (7.15), the cumulative distribution functions for project duration at different transitional correlation values. The second simulation is again a complete time and economic risk quantification. Tables 7.13, 7.14 and 7.15 contain results from the simulation at 500 iteration intervals and statistics from the analytical method at different transitional correlation values for discounted project cost, discounted project revenue and project net present value. Tables 7.16 contains the expected value and standard deviation for project internal rate of return from the simulation and the analytical method at different transitional correlation values.  Chapter 7. Vahdations and Apphcations  182  Table 7.13: Statistics for Discounted Project Cost from Second Validation - Ex #1 Simulation  #  E[DPC]  500 1000 1500 2000 2500 3000 3500 4000  Analytical Method &DPC  $  %  81021792 80835632 80844544 80826096 80795552 80809072 80816496 80828448  9462825 9537916 9699357 9752933 9797531 9791703 9750850 9728706  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[DPC)  fa  0~DPC  %  %  87795608 83698131 83438139 82318999 82090258 81377056 80958126 80600925 80348004 79175607 78815314  9800603 9322582 9276978 9166055 9138546 9049674 9004202 8966904 8935649 8800934 8760869  0.092 0.092 0.094 0.093 0.093 0.094 0.094 0.094 0.094 0.095 0.095  2.557 2.564 2.574 2.569 2.570 2.574 2.574 2.574 2.575 2.579 2.580  Table 7.14: Statistics for Discounted Project Revenue from Second Vahdation-Ex #1 Simulation  # 500 1000 1500 2000 2500 3000 3500 4000  E[DPR]  Analytical Method &DPR  $  $  127760688 127718064 128100240 128309872 128541968 128504048 128558912 128685376  14264898 13930838 13516248 13668063 13602110 13480204 13500583 13480615  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[DPR)  02  &DPR  $  $  147861348 135939654 135668030 132635839 132007641 129969465 129129937 128343894 127624844 124503245 123611279  12781663 11762603 11502775 11390905 11322201 11101722 11010403 10936793 10861321 10569469 10487196  -0.038 -0.046 -0.045 -0.046 -0.046 -0.046 -0.046 -0.046 -0.046 -0.045 -0.045  2.720 2.724 2.728 2.727 2.727 2.728 2.729 2.729 2.730 2.731 2.732  Chapter 7. Validations and Apphcations  183  Table 7.15: Statistics for Project NPV from Second Validation - Ex #1 Simulation  # 500 1000 1500 2000 2500 3000 3500 4000  E[NPV]  Analytical Method aNPV  $  %  46737200 46889744 47259008 47478240 47735504 47680688 47725680 47838080  15634686 14909846 14681378 15058231 15001127 14979397 14848319 14796526  9 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[NPV]  °~NPV  $  %  60065740 52241523 52229892 50316839 49917383 48592410 48171811 47742969 47276840 45327638 44795965  16106605 15008997 14777556 14620851 14550094 14322878 14223384 14142800 14064641 13753913 13665069  VFi  -0.040 -0.044 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045  A 2.828 2.831 2.834 2.833 2.833 2.834 2.834 2.834 2.835 2.836 2.836  Table 7.16: Statistics for Project IRR from Second Vahdation - Ex #1 Simulation  #  500 1000 1500 2000 2500 3000 3500 4000  E[IRR]  30.83 30.86 30.9.3 30.97 31.03 31.02 31.02 31.03  Analytical Method &IRR  3.94 3.77 3.76 3.87 3.89 3.88 3.82 3.78  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[IRR]  33.457 31.887 31.886 31.551 31.472 31.176 31.075 31.019 30.929 30.571 30.477  °~IRR  4.756 4.233 4.073 4.087 4.056 3.908 3.971 3.957 3.927 3.708 3.702  Chapter 7. Validations and Apphcations  184  Table 7.17: Comparison of CPU times from Second Validation - Ex #1 Simulation # CPU Sec. 500 551 1000 1100 1500 1642 2000 2185 2500 2728 3272 3000 3500 3816 4000 4361  Analytical Method CPU Sec. P 0.0 35.04 0.1 35.19 0.2 35.25 0.3 35.98 0.4 36.20 0.5 36.31 0.6 36.27 0.7 36.34 0.8 36.32 0.9 36.89 1.0 39.65  Figures (7.17), (7.18), (7.19) and (7.20) illustrate the cumulative distribution functions for the transitional correlation p — 0.5, upper and lower bounds approximated from the analytical method and those generated from a simulation of 4,000 iterations for discounted project cost, revenue, net present value and internal rate of return. The second validation also demonstrates that cumulative distribution functions and the estimates for time and economic variables generated from the simulation are within the upper and lower bounds predicted by the analytical method. Table 7.17 contains a comparison of the execution time for the simulation and the analytical method. Again, the computational economy of the analytical method is highlighted. The more significant observation is the wider bounds for derived variables when compared to the previous case. The only change from the first to second validation is an increase in the coefficients for variation for work package durations. Therefore, wider bounds are a direct result of the increase in the variance for work package durations and start times. This observation highlights the significance of work package duration and start time in economic risk quantification.  Chapter 7. Validations and Applications  Duration (months) Figure 7.15: CDFs for Project Duration - Second Validation - Ex #1  Duration (months) Figure 7.16: CDFs for Project Duration - Second Validation - Ex #1  185  Chapter 7. Validations and Applications  50  60  70  186  80 90 100 Cost (discounted $*10 )  110  120  6  Figure 7.17: CDFs for Discounted Project Cost - Second Validation - Ex #1  80  100  120 140 160 Revenue (discounted $*10 )  180  6  Figure 7.18: CDFs for Discounted Project Revenue - Second Validation - Ex #1  Chapter 7. Validations and Applications  Net Present Value  187  ($*10 ) 6  Figure 7.19: CDFs for Project Net Present Value - Second Validation - Ex #1  Discount Rate (%) Figure 7.20: CDFs for Project Internal Rate of Return - Second Validation - Ex #1  Chapter 7. Validations and Apphcations  188  The analytical method permits the analyst to specify a transitional correlation (p) for decision making. The cumulative distribution functions for time and economic variables when p — 0.5 are included to demonstrate how, in addition to providing a risk quantification at the specified p, the analytical method can perform the sensitivity of that quantification by approximating the bounds. It must be stressed that p = 0.5 is used only as an example, because it is not possible to recommend a single value for p that can be used for all risk analyses of engineering projects. The analysis however, can be conducted using the hmiting values for p, (0,1), as well as an intermediate value (say p — 0.5). This approach provides the analyst with additional insights and as demonstrated by this example, it is still ten times faster than Monte Carlo simulation.  7.5.4  Discussion  The examples presented in this section vahdated the analytical method. In addition, the validations clearly demonstrated the computational economy of the analytical method when compared to Monte Carlo simulations. There were 164 random primary variables at the input level for both approaches. The two hmiting cases that were used to validate the simulation process also confirmed the theoretical postulations made by the modified PNET algorithm. Thefirstvalidation demonstrated the abihty of the analytical method to fit easily into the existing deterministic estimation approaches prevalent in the construction industry. Thisflexibilityis important for a theoretical development to become a practical tool in the industry. By considering the deterministic estimates as the median values for the work packages, subjective probabihties can be ehcited. This permits the analyst/experts in engineering construction to begin the risk quantification process from the familiar deterministic structure.  Chapter 7. Validations and Apphcations  189  Table 7.18 contains a comparison of the deterministic and probabilistic estimates for constant, current and total dollar estimates for project cost. While the deterministic values and the expected values are comparable, it demonstrates that the deterministic values on which most of the decisions are based at present, only have about a 50% probability of success. The quantification of the uncertainty associated with the estimates for project cost permits the contingency to be allocated on the probability of success of the project. Table 7.18: Deterministic and Probabilistic Analyses of Project Cost Deterministic  Probabilistic E[PC]  Constant Dollar Cost Current Dollar Cost Total Dollar Cost  0~PC  VP\  ft  124450100 126394711 14041896 0.095 2.61 137628834 139737616 15742136 0.093 2.59 151287416 153804634 17036142 0.096 2.61  In addition to validating the analytical method, the second validation demonstrated the significance of the variance of work package durations and start times to the derived economic variables. The bounds of the derived variables are wider when compared to the first validation. The cumulative distribution functions at the transitional correlation p = 0.5, illustrate the ability of the analytical method to quantify the economic variables for decision making. Figures (7.21) and (7.22) depict the cumulative distribution functions for p — 0.5, upper and lower bounds for current and total project costs. Even though, project duration has wide bounds (see figure 7.15), the bounds for current and total dollar project costs are relatively tight. The reason for this phenomena is that, since the start time is one of the six variables in the function for work package cost its significance (sensitivity) is reduced. This is further highlighted in the next example when the start time is one of seventeen variables in the work package cost function.  190  Chapter 7. Validations and Applications  Cost (current $*10 ) 6  Figure 7.21: CDFs for Current Dollar Project Cost - Second Validation - Ex #1  220  Cost (total $*10 ) e  Figure 7.22: CDFs for Total Dollar Project Cost - Second Validation - Ex #1  Chapter 7. Validations and Apphcations  7.6  191  Second Example  The second example is a hypothetical engineering project of thirteen work packages and three revenue streams. The precedence network of the work packages is shown in figure (7.23). For illustrative purposes herein the primary variables in the functions for the work package durations, costs and revenue streams are assumed to be stationary over the duration of the work package or revenue stream. In reality these primary variables (labor usage, productivity, inflation and interest rates, etc) are time dependent. The assumption allows the development of simplified but realistic models. The function for work package durations used in this example is as follows. Qi  WPDi  (7.5)  Pu  Li  where Qi is the quantity descriptor, P^. is the labour productivity rate and Li is the labour usage. The function for discounted work package cost (WPCi) is as follows. WPCi  f  ,(B .-y)T . L  Sc  f '  +  el'Mi-yVs*  +  e  + +  C.  TC  Jo  Li e^i-"*  L  [ '  C  TC  Jo  dr  Pu Li ( >- > 6M  Mi  y  e  dr  rTci  (0 -y)T Ei  j  Sci  "  C  e >Ei  E  . e(BEi-y)r &i  ^  Jo eVii-vVsn  frTa Jo  {<>s,-v)Ts  f  e  j  Tci  Ct  r  _|_  MiT  8 e  Sci  6  ^ a  Ta  [ C, Jo  dr [ C. L Jo ' Tci  L  P , Li  TCi  e *i * [ " C . JoI  •+  e 'i  T  E  f I. e^- >dr Jo t - Si_ e ^c. f'^^L e^-^dr Jo io T i «  Ci  e^i-^dr  Eie^-^ dr  Ts  T s  x  e^-^dr  L  +  e  r  S  M  e  d  ±Ci  e  r  (fli,.-i/>r  e  <  T C t  Jo  +(1 - /)e( -^ "  c  r  c  Tc  +  6  C  (7.6)  Sta  Chapter 7. Validations and Applications  Figure 7.23: The Project Network for the Second Example  192  193  Chapter 7. Vahdations and Apphcations  where WPC{ is the discounted i  work package cost, Ci , CM;, and (7E are the unit {  ;  rates for labour, materials and equipment, Li and Ei are the labour and equipment usage profiles, PL, is the labour productivity rate, Ic and Si are the indirect and sub {  contractor costs assumed as uniform constant dollar profiles,  ^M QEI, 8i and 6s ;)  t  t  are inflation rates for labour, materials, equipment, indirect cost and sub contractor cost, and Ts  and Ta are work package start time and duration for the i work th  Ci  package respectively. T is the time at which the repayment of interim financing is p  due for all work packages (assumed as the end of the construction phase), / is the equity fraction, and r and y are interest and discount rates respectively. The time r is measured from the start of the i  th  work package.  The function for revenue streams (NRSi) is as follows.  =  NRSi  I* "**" iRveW-**) 5  - Mae "'*} e~*dt 8  where NRSi is the discounted i revenue stream, Roi and Moi are the constant dollar th  cashflowfor the i  gross revenue and operation and maintenance cost assumed as  th  uniform profiles, Ts and TR; are start time and duration of the revenue stream, and Ri  , 8Mi and y are inflation and discount rates respectively. The expected value, standard deviation, skewness and kurtosis for individual primary variables in the functions for work package durations, costs, and revenue streams are given in Appendix F. A construction loan for 75% (/ = 0.25) of the current dollar expenditure on construction is assumed. The statistics for the interest rate on the construction loan are E[r] = 7.537%, tr = 0.852%, V / ^ = 0.2 and 8 = 2.5. A r  2  minimum attractive rate of return of 9% is used for the analysis and vahdation. This section demonstrates third and fourth validations of the analytical method. The third vahdation assumes all the primary variables to be uncorrelated. The fourth validation treats hnear correlations between the primary variables. The example is extended to a third level where correlations at all levels of the project economic  Chapter 7. Validations and Apphcations  194  Table 7.19: Statistics for Project Duration from Third Vahdation - Ex #2 Simulation  #  E\PD) mths  500 1000 1500 2000 2500 3000 3500 4000  30.73 30.85 30.89 30.92 30.94 30.98 30.98 31.01  Analytical Method 0~PD  mths  4.24 4.40 4.37 4.42 4.46 4.50 4.55 4.62  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[PD] mths  29.44 29.44 29.44 29.44 29.78 31.84 31.84 31.94 32.04 32.42 32.42  fa  mths  4.69 4.69 4.69 4.69 4.53 4.06 4.06 3.98 3.90 3.69 3.69  0.34 0.34 0.34 0.34 0.4 0.3 0.3 0.3 0.4 0.4 0.4  2.77 2.77 2.77 2.77 2.9 2.9 2.9 2.8 3.1 3.2 3.2  structure are treated.  7.6.1  T h i r d Validation  A simulation for complete time and economic risk quantification was done for the third vahdation of the analytical method. For this simulation all of the variables were assumed to be uncorrelated. Tables 7.19, 7.20, 7.21 and 7.22 contain results from the simulation at 500 iteration intervals and statistics from the analytical method at different transitional correlation values for project duration, discounted project cost, discounted project revenue and project net present value. Tables 7.23 contains the expected value and standard deviation for project internal rate of return from the simulation and the analytical method at different transitional correlation values.  Chapter 7. Validations and Apphcations  195  Table 7.20: Statistics for Discounted Project Cost from Third Validation - Ex #2 Simulation  # 500 1000 1500 2000 2500 3000 3500 4000  E[DPC]  Analytical Method °~DPC  %  $  47747712 47549456 47539104 47484848 47574352 47586560 47592672 47623920  7272635 7255953 7097718 7290801 7313699 7287798 7249368 7317997  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[DPC]  ft  °~DPC  $  $  47656668 47656668 47656668 47656668 47642261 47548355 47548355 47544054 47535878 47519155 47519155  6800455 6800455 6800455 6800455 6798415 6784741 6784741 6784131 6783307 6780948 6780948  0.188 0.188 0.188 0.188 0.188 0.188 0.188 0.188 0.188 0.188 0.188  2.724 2.724 2.724 2.724 2.724 2.724 2.724 2.724 2.724 2.724 2.724  Table 7.21: Statistics for Discounted Project Revenue from Third Validation - Ex #2 Simulation  # 500 1000 1500 2000 2500 3000 3500 4000  E[DPR)  Analytical Method °~DPR  $  $  69621328 69777088 69942976 70071568 70116800 70091024 69932000 69941072  13437693 13114197 13445692 13397565 13345129 13409836 13602733 13626623  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[DPR]  ft  °~DPR  $  $  70266290 70266290 70266290 70266290 70183320 69672971 69672971 69648247 69624938 69529211 69529211  13744103 13744103 13744103 13744103 13726601 13624327 13624327 13618945 13613874 13593995 13593995  -0.431 -0.431 -0.431 -0.431 -0.431 -0.429 -0.429 -0.429 -0.429 -0.428 -0.428  4.718 4.718 4.718 4.718 4.717. 4.706 4.706 4.706 4.706 4.705 4.705  Chapter 7. Validations and Apphcations  Table 7.22: Statistics for Project NPV from Third Vahdation - Ex #2 Simulation  # 500 1000 1500 2000 2500 3000 3500 4000  E[NPV)  Analytical Method cr  N  P  V  %  %  21872384 22226560 22411568 22592032 22546816 22507776 22338016 22312432  15545242 15178882 15381492 15317318 15415091 15496218 15561380 15657844  E[NPV)  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  82  <J p N  V  %  $  22609623 22609623 22609623 22609623 22541059 22124616 22124616 22104194 22089060 22010056 22010056  15334489 15334489 15334489 15334489 15317899 15220217 15220217 15215127 15210220 15191377 15191377  -0.327 -0.327 -0.327 -0.327 -0.327 -0.324 -0.324 -0.324 -0.324 -0.324 -0.324  4.098 4.098 4.098 4.098 4.097 4.085 4.085 4.084 4.084 4.082 4.082  Table 7.23: Statistics for Project IRR from Third Vahdation - Ex #2 Simulation  #  500 1000 1500 2000 2500 3000 3500 4000  Analytical Method  E[IRR]  °~IRR  16.13 16.26 16.29 16.35 16.32 16.30 16.23 16.23  5.07 4.94 5.00 4.99 5.02 5.03 5.04 5.07  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[IRR]  16.305 16.305 16.305 16.305 16.268 16.061 16.061 16.052 16.046 16.014 16.014  °~IRR  5.061 5.061 5.061 5.061 5.044 4.955 4.955 4.953 4.952 4.944 4.944  Chapter 7. Validations and Apphcations  197  Figure (7.24) illustrates the cumulative distribution functions for upper and lower bounds for project duration approximated from the analytical method and that generated from the simulation of 4,000 iterations. Figure (7.25) depicts, in addition to those in figure (7.24), the cumulative distribution functions for project duration at different transitional correlation values. Figures (7.26), (7.27), (7.28) and (7.29) illustrate the cumulative distribution functions for upper and lower bounds approximated from the analytical method and those generated from the simulation for discounted project cost, revenue, net present value and internal rate of return. The third validation also demonstrates that cumulative distribution functions and the estimates for expected values for time and economic variables generated from the simulation are within the upper and lower bounds predicted by the analytical method. Thereby, validating the analytical method. The bounds for the derived economic variables are extremely tight. These bounds are the sensitivity of the derived economic variables with respect to the start times of the work packages. Table 7.24 contains the comparison of the execution time for the simulation and the analytical method. The computational economy of the analytical method is again highlighted. For this example the analytical method is about fifty times faster when compared to 1000 iterations from the Monte Carlo simulation.  7.6.2  Fourth Validation  The fourth validation of the analytical method was also a complete time and economic risk quantification. The only change from the previous section is that the primary variables in the functions for work package durations and costs are considered to be correlated. The positive definite correlation matrices for work package duration and cost functions were obtained using the process described in sections 4.2.2. Since the function given by equation (7.5) is used to evaluate the work package  Chapter 7. Vahdations and Apphcations  198  Table 7.24: Comparison of CPU times from Third Vahdation - Ex #2 Simulation # CPU Sec. 500 861 1721 1000 1500 2577 2000 3427 2500 4275 5121 3000 3500 5969 4000 6819  Analytical Method CPU Sec. P 0.0 28.64 0.1 28.60 0.2 28.67 0.3 28.57 0.4 27.97 0.5 2805 0.6 28.11 0.7 28.28 0.8 28.38 0.9 28.23 1.0 28.10 ;  durations, an identical positive definite correlation matrix was used for all the work package durations. This simplification is also convenient when the correlations between the derived variables are approximated using the identified common (shared) primary variables. The positive definite correlation matrix for work package durations and the positive definite correlation matrix for work package costs used for all the work packages in this apphcation are given in Appendix F. Even though the function for work package costs given by equation (7.6) has seventeen variables, the positive definite correlation matrix is only 14x14. The reason is because three variables - work package duration, start time, and project duration are always pre-defined in the decomposed function for work package cost. Their moments are evaluated from the modified PNET algorithm. Therefore, the correlation matrix is only of the variables that are ehcited as input primary variables. The computer program 'ELICIT' ensures that there is no confusion during the ehcitation process by identifying the pre-defined variables as thefirstthree variables of the decomposed function (see equation 6.7).  199  Chapter 7. Validations and Applications  Duration (months)  Figure 7.24: CDFs for Project Duration - Third Validation - Ex #2  10  20  30  40  Duration (months)  Figure 7.25: CDFs for Project Duration - Third Validation - Ex #2  50  Chapter 7. Validations and Applications  200)  Simulation (n=4000)  ¥ o O  _  0 8  All Paths Longest Path  /  /  0.6  V  to  o O t3 0.4 CD  '£?  ri  2 a. 0.2 i  30  20  i  80  70  40 50 60 Cost (discounted $*'\0 ) e  Figure 7.26: CDFs for Discounted Project Cost - Third Validation - Ex #2  v C  Simulation (n=4000) 0.8 _  All Paths  rr o  Longest Path 0.6  3 C % O  /  -  /  i  li fi li  rr 0.4 h-  ts o  '2  0.2  QL  ri o  i  10  20  11  30  —  i  1  1  ••  i  40 50 60 70 80 90 Revenue (discounted $*10 )  i  100 110 120  6  Figure 7.27: CDFs for Discounted Project Revenue - Third Validation - Ex #2;  Chapter 7. Validations and Applications  -40  -20  201  0 20 40 Net Present Value ($ *10 )  60  80  6  Figure 7.28: CDFs for Project Net Present Value - Third Validation - Ex #2  Discount Rate (%) Figure 7.29: CDFs for Project Internal Rate of Return - Third Validation - Ex #2  Chapter 7. Validations and Apphcations  202  Table 7.25: Statistics for Project Duration from Fourth Validation - Ex #2 Simulation  # 1000 2000 3000 4000 5000 6000  E[PD] mths  29.34 29.41 29.44 29.48 29.51 29.47  Analytical Method mths  3.05 3.00 3.04 3.17 3.22 3.20  P  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[PD] mths  29.31 29.31 29.31 29.31 29.57 31.65 31.65 31.77 31.83 32.21 32.21  ft  mths  4.59 4.59 4.59 4.59 4.46 4.03 4.03 3.94 3.88 3.68 3.68  0.37 0.37 0.37 0.37 0.4 0.3 0.3 0.4 0.4 0.4 0.4  2.81 2.81 2.81 2.81 2.9 2.9 2.9 3.1 3.0 3.1 3.1  Tables 7.25 contains results from the simulation at 1000 iteration intervals and statistics from the analytical method at different transitional correlation values for project duration. Figure (7.30) illustrates the cumulative distribution functions for upper and lower bounds for project duration and that generated from the simulation of 6,000 iterations. Figure (7.31) depicts the cumulative distribution functions for project duration at different transitional correlation values. The cumulative distribution function from the simulation is comparatively tight with the upper part outside of the bounds predicted from the analytical method. When the standard deviations from Table 7.19 and 7.25 are compared, the analytical method shows a small reduction while the simulation shows a significant dampening which can be attributed to the approach used to treat correlations. The approach used does not distinguish between positive and negative correlations. This dampening caused the distribution to be outside the bounds. However, a study of the individual work package durations show that there should not be a significant reduction in the variance for project duration.  Chapter 7. Validations and Apphcations  203  Table 7.26 contains the expected values, standard deviations and the differences when the primary variables are uncorrelated and correlated for individual work packages. The difference of all the expected values are negligible. Except for work packages # 6, 7, 8 and 10 the difference in the standard deviations are small. When these work packages are studied in the context of the paths in the project network (see next section) and their contributions to path variances, except for the third longest path which has work packages 6 and 10, none of the others have more than one of the above four. In addition, their contributions to path variances are small. Hence, none of the paths can have a significant reduction in variance from the uncorrelated case to the correlated case. Table 7.26: Statistics for Project Variables WP# 02 03 04 05 06 07 08 09 10 11 12 13 14  Expected Value Uncor Corr 7.715 7.681 4.971 4.952 6.949 6.918 3.363 3.356 3.44 3.363 1.732 1.687 6.634 6.442 5.812 5.795 2.748 2.686 4.571 4.55 6.979 6.959 6.797 6.794 6.337 6.349  (months) Differ -0.44% -0.38% -0.45% -0.21% -0.22% -0.26% -0.29% -0.29% -0.23% -0.46% -0.29% -0.05% 0.18%  Standard Deviation (months) Uncor Corr Differ 2.717 2.625 - 3.38% 1.311 1.267 - 3.35% 2.451 2.368 - 3.38% 1.046 1.047 0.1 % 1.118 0.90 -19.38% 0.667 0.56 -16.04% 2.251 1.702 -24.39% 2.039 1.999 - 1.96% 0.899 0.725 -19.35% 1.185 1.114 - 6.0 % 2.453 2.405 - 1.95% 2.399 2.442 1.79% 2.372 2.442 2.95%  Tables 7.27, 7.28 and 7.29 contain results from the simulation at 1000 iteration intervals and statistics from the analytical method at different transitional correlation values for discounted project cost, discounted project revenue and project net present value. Tables 7.30 contains the expected value and standard deviation for project  Chapter 7. Validations and Apphcations  204  Table 7.27: Statistics for Discounted Project Cost from Fourth Validation-Ex #2 Simulation  # 1000 2000 3000 4000 5000 6000  E[DPC)  Analytical Method &DPC  %  %  46114784 46077568 46144304 46200736 46197600 46228352  5724537 5668257 5705700 5750056 5783185 5786760  P 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  E[DPC)  ft  0~DPC  %  $  46808547 46808547 46808547 46808547 46797312 46705300 46705300 46700307 46693962 46678295 46678295  6422711 6422711 6422711 6422711 6421195 6408483 6408483 6407809 6407225 6405120 6405120  0.214 0.214 0.214 0.214 0.214 0.214 0.214 0.214 0.214 0.214 0.214  2.742 2.742 2.742 2.742 2.742 2.742 2.742 2.742 2.742 2.742 2.742  internal rate of return from the simulation and the analytical method at different transitional correlation values. Figures (7.32), (7.33), (7.34) and (7.35) illustrate the cumulative distribution functions for upper and lower bounds approximated from the analytical method and those generated from the simulation for discounted project cost, revenue, net present value and internal rate of return. The estimates and cumulative distribution functions for time and economic variables are reasonably close to the predicted envelope of bounds.  It must be noted  that the bounds are again extremely tight. In addition to start time being one of the seventeen variables in the work package cost functions, the project network given by figure (7.23) is also small, with few interrelationships between work packages. The combination of these two factors increase the tightness of the bounds. Table 7.31 contains the comparison of the execution time for the simulation and the analytical method. The computational economy of the analytical method is again highlighted. The execution times for Monte Carlo simulation from third and fourth validations are similar. The reason for the similarity is because the same computer  Chapter 7. Validations and Apphcations  205  Table 7.28: Statistics for Discounted Project Revenue from Fourth Validation-Ex #2 Simulation  #  Analytical Method  E[DPR]  °~DPR  %  1000 2000 3000 4000 5000 6000  70145200 70439824 70465632 70314480 70220368 70297504  P  %  13176377 13459856 13466430 13685384 13688517 13691024  E[DPR] %  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  fa  0~DPR %  70299596 70299596 70299596 70299596 70233575 69720113 69720113 69690857 69674338 69582818 69582818  13749690 13749690 13749690 13749690 13735779 13633218 13633218 13626900 13623299 13604284 13604284  -0.431 -0.431 -0.431 -0.431 -0.431 -0.429 -0.429 -0.429 -0.429 -0.428 -0.428  4.720 4.720 4.720 4.720 4.719 4.708 4.708 4.708 4.708 4.707 4.707  Table 7.29: Statistics for Project NPV from Fourth Vahdation - Ex #2 Simulation  #  E[NPV] $  1000 2000 3000 4000 5000 6000  24029376 24368384 24323088 24106912 24010976 24053856  Analytical Method °~NPV $  14524955 14635079 14750314 14934437 14976197 14972542  P  E[NPV) $  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  23419049 23419049 23419049 23419049 23436263 23014812 23014812 22990550 22980376 22904523 22904523  0~NPV $  15175809 15175809 15175809 15175809 15162564 15064305 15064305 15058301 15054793 15036692 15036692  P2  -0.337 -0.337 -0.337 -0.337 -0.337 -0.334 -0.334 -0.334 -0.334 -0.334 -0.334  4.151 4.151 4.151 4.151 4.150 4.137 4.137 4.137 4.137 4.135 4.135  Chapter 7. Validations and Apphcations  206  Table 7.30: Statistics for Project I R R from Fourth Validation - E x #2 Simulation  #  E[IRR]  1000 2000 3000 4000 5000 6000  16.90 16.97 16.94 16.86 16.83 16.83  Analytical Method VlRR  4.69 4.73 4.75 4.80 4.82 4.81  P  E[IRR]  0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0  16.654 16.654 16.654 16.654 16.628 16.427 16.427 16.413 16.408 16.366 16.366  °~IRR  5.026 5.026 5.026 5.026 5.014 4.912 4.912 4.902 4.898 4.869 4.869  Table 7.31: Comparison of C P U times from Fourth Validation - E x #2 Simulation # C P U Sec. 1000 1702 2000 3395 3000 5085 4000 6775 5000 8465 6000 10168  Analytical Method C P U Sec. P 0.0 29.00 0.1 29.03 0.2 29.12 0.3 29.08 0.4 29.11 0.5 28.41 0.6 28.40 0.7 28.61 0.8 28.72 0.9 28.71 1.0 28.83  Chapter 7. Validations and Apphcations  207  program was used for both simulations. T h e only difference in the input was the use of identity matrices as the correlation matrices for the third validation and positive definite correlation matrices for the fourth.  One could argue that if the random  number modification algorithm was not included in the computer program for the third validation, the simulation could have been more efficient.  7.6.3  Correlations at A l l Levels of the Project  T h e analytical method can approximate the linear correlations between derived variables using the linear correlations (shared)  between primary variables when the  variables are identified (see section 4.3).  common  T h e common variables are de-  fined as those variables of the same type having the same first four moments in the functional forms for two or more derived variables. Hence, correlation between work package durations, costs and revenue streams can be treated in the evaluation of the first four moments of project duration, cost and revenue. As demonstrated in this section, the contribution to the moments of the derived variables from these correlations can be significant. It is not possible to duplicate this treatment for the Monte Carlo simulation from the available information.  T h e simulation assumes that only the linear~correlations  between the primary variables in the functions are available (see figure 7.36). However, if it is possible to obtain the correlation matrix for the complete system,  then  simulation can treat all the correlations. For project cost in this example, one would have to elicit a (182 — i)x(182 — I) positive definite correlation matrix, where I is the total number of common variables in the functions for derived variables minus the number of sets of common variables. Even though linear correlation coefficients between all the work package durations are approximated, only the positive definite correlation matrices for the individual  Chapter 7. Validations and Applications  Duration (months) Figure 7.30: CDFs for Project Duration - Fourth Validation - Ex #2  Duration (months) Figure 7.31: CDFs for Project Duration - Fourth Validation - Ex #2  208  209  Chapter 7. Validations and Applications  Simulation (n=6000) All Paths  0-8  U  8 V t>  o O  Longest Path 0.6  ts o  cr  0.4  CL  2  Q-  0.2  40  30  50 Cost (discounted $*10 )  60  70  6  Figure 7.32: CDFs for Discounted Project Cost - Fourth Validation - Ex #2 1  Simulation (n-6000) CD  S  0.8 _  All Paths  •V •7  ff  CD  rr rr  II  Longest Path  S> 0.6 c  CD > CD  t3  0.4  CD  '2  CL  ri 0.2  2  Q.  10  l  l  20  30  \  ^  1  l  i  i  i  40 50 60 70 80 90 Revenue (discounted $*10 )  i  100  i  110 120  6  Figure 7.33: CDFs for Discounted Project Revenue - Fourth Validation - Ex #2  210  Chapter 7. Validations and Applications  Figure 7.34: C D F s for Project Net Present Value - F o u r t h V a l i d a t i o n - E x #2  0  5  10  15  20  25  30  Discount Rate (%) Figure 7.35: C D F s for Project Internal Rate of R e t u r n - Fourth V a l i d a t i o n - E x #2  Chapter 7. Validations and Applications  Correlation between work packages approximated by the analytical method utilizing the common (shared) variables between the functions for durations and costs.  I  I i  The correlation information required for the Monte Carlo simulation ] The correlation information available for the Monte Carlo simulation  Figure 7.36: Correlation Matrix for the Complete System  211  Chapter 7. Validations and Apphcations  212  paths to a work package are necessary to evaluate the first four moments of its start time. For example, there are six paths to complete the project illustrated by figure (7.23). The positive definite correlation matrices for the six paths approximated by the analytical method are as follows. (The paths are ordered in decreasing mean path durations). Path #1 - Work Packages # 2, # 3, # 6, # 12, # 14. 1.0  0.0  0.0  0.3  0.0  0.0  1.0  0.53  0.0  0.59  0.0  0.53  1.0  0.0  0.45  0.3  0.0  0.0  1.0  -0.19  .0.0  0.59  0.45  -0.19  1.0  Rpath#l =  Path #2 - Work Packages # 2, # 4, # 7, # 14. 1.0  0.3  0.0  0.0"  0.3  1.0  0.0  0.79  0.0  0.0  1.0  0.42  .0.0  0.79  0.42  R Path#2  1.0.  Path #3 - Work Packages # 2, # 3, # 6, # 10, # 13.  Rpath#3 =  •i.o  0.0  0.0  0.0  0.0"  0.0  1.0  0.53  0.53  0.0  0.0  0.53  1.0  0.36  0.0  0.0  0.53  0.36  1.0  0.0  .0.0  0.0  0.0  0.0  1.0.  Path #4 - Work Packages # 2, # 3, # 5, # 9, # 13.  R-Path#4  ~  1.0  0.0  0.0  0.92  o.o-  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.92  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0.  Chapter 7.  Vahdations  and  213  Apphcations  Path #5 - Work Packages # 2, # 3, # 6, # 11.  R-Path#5  =  1.0  0.0  0.0  0.0  0.0  1.0  0.53  0.0  0.0  0.53  1.0  0.0  LO.O  0.0  0.0  l.OJ  Path #6 - Work Packages # 2, # 4, # 8. "1.0  0.3  0.0  0.3  1.0  0.0  .0.0  0.0  1.0  R-Path#6 =  The correlation matrix for the work package costs is however a 13x13 matrix. This is because all the work package costs are summed to evaluate the project cost. The positive definite correlation matrix for work package costs is as follows.  "1.0  .29  .24  .26  .25  .23  .23  .25  .25  .29  .24  .23  .23-  .29  1.0  .25  .30  .28  .26  .27  .29  .29  .35  .29  .27  .35  .24  .25  1.0  .26  .25  .23  .23  .25  .25  .29  .25  .23  .23  .26  .30  .26  1.0  .27  .24  .24  .26  .27  .47  .26  .24  .24  .25  .28  .25  .27  1.0  .15  .24  .25  .16  .30  .25  .24  .23  .23  .26  .23  .24  .15  1.0  .21  .23  .14  .28  .23  .22  .21  .23  .27  .23  .24  .24  .21  1.0  .23  .24  .28  .22  .22  .21  .25  .29  .25  .26  .25  .23  .23  1.0  .25  .30  .25  .23  .23  .25  .29  .25  .27  .16  .14  .24  .25  1.0  .31  .25  .24  .23  .29  .35  .29  .47  .30  .28  .28  .30  .31  1.0  .29  .28  .28  .24  .29  .25  .26  .25  .23  .22  .25  .25  .29  1.0  .23  .20  .23  .27  .23  .24  .24  .22  .22  .23  .24  .28  .23  1.0  .22  .23  .35  .23  .24  .23  .21  .21  .23  .23  .28  .20  .22  1.0.  Chapter 7. Validations and Apphcations  214  Tables 7.32 contains the expected values and standard deviations approximated by the analytical method for project duration, total dollar project cost, project net present value and internal rate of return for the transitional correlations p = 0, p = 0.5 and p — 1.0. The revenue streams were assumed to be uncorrelated because of the difficulty in identifying common variables. The correlation matrices given above were used in the evaluation of moments for project duration and costs. Table 7.32: Statistics for Project Variables Project Variable Duration Cost (Tot$) NPV IRR  = 0 P =  E[PV] 29.31 56955300 23494481 17.251  CTpv  5.43 13650279 17758202 6.743  P - 0.5 E[PV] (Tpy 32.57 4.75 57850640 13869375 22838442 17612327 16.923 6.616  P = 1.0 E[PV] <TPV 33.06 4.46 57984776 13902068 22738804 17589760 6.573 16.860  Table 7.33 and 7.34 contain comparisons of the expected values and standard deviations approximated by the analytical method at the transitional correlations p = 0, p = 0.4 and p = 1.0 for project duration and current dollar project costs when all the variables are uncorrelated (third vahdation case), primary variables in the functions for work package durations and costs are correlated (fourth vahdation case) and when the primary variables, work package durations and costs are correlated. c  The p = 0.4 is used because the current dollar project cost at p > 0.5 is same as the upper bound (p = 1) estimate. There is only a marginal difference in the statistics for the project duration and the project cost from the first and second cases. Even though the expected value for project duration is slightly larger when p — 0.4 and p = 1 for the third case, the expected values for project cost are similar for all three situations. Since project cost is the hnear addition of work package costs, there is no effect from the correlation between work package costs. Hence, the identical expected values for project cost  Chapter 7. Validations and Apphcations  215  Table 7.33: Comparison of the Statistics for Project Duration Type of the Correlations Uncorrelated Primary only All Variables  P= E[PD] 29.44 29.31 29.31  0  p = 0.4 E[PD] °~PD 29.78 4.53 4.69 29.57 4.46 4.59 32.22 4.82 5.43  p = 1.0 E[PD] &PD 32.42 3.69 32.21 3.68 33.06 4.46  Table 7.34: Comparison of the Statistics for Current Dollar Project Cost Type of the Correlations Uncorrelated Primary only All Variables  p= 0 E[PC] 54129602 53485125 53485125  o~pc  7584872 7285335 12776812  p = 0.4 E[PC] vpc 54145288 7588666 53500807 7289218 53500807 12781905  0.5 < p < 1.0 E[PC] <TPC 54158049 7589427 53513534 7289989 53513534 12784827  from the second and third cases. However, there is nearly 18% and 75% increases in the standard deviations for project duration and cost due to the correlations between work package durations and costs. The correlations between work package costs are relatively small. This clearly illustrates that these correlations can be significant. Figure (7.37) depicts the cumulative distribution functions for project duration at different transitional correlation values approximated from the analytical method. Figure (7.38), (7.39) and (7.40) depict, the cumulative distribution functions for upper and lower bounds approximated from the analytical method for total dollar project cost, project net present value and internal rate of return. The hnear correlations between the primary variables in the functions for work package durations and costs, and the hnear correlations between work package durations and work package costs respectively are treated.  Chapter 7. Validations and Applications  c o  t3 2  Longest Path P =0.3,0.4  0.8 h  3 Q  V c o  1  P =0.5 0.6 h  P =0.6 All Paths  3 Q  0.4 h t5 CD '2 CL n  2  0.2  CL 15  20  25  40  30 35 Duration (months)  45  Figure 7.37: CDFs for Project Duration  All Paths ^ o O  0.8 |—  1/5 o o  0.6  v  Longest Path  CD  If  0.4  CL  p  0.2  20  40  60 Cost (total $ * 1 0 )  80  6  Figure 7.38: CDFs for Total Dollar Project Cost  100  Chapter 7. Validations and Applications  All Paths ^  0.8  Longest Path  Q_ Z  >  Q_ Z  t5 o o  0.6  —  //  0.4  //  //  //  //  //  //  //  //  M r .4  /  °-  0.2  >  -  y • —  (40)  (20)  y i  i  i  i  0 20 40 Net Present Value ($*10 )  60  80  6  Figure 7.39: CDFs for Project Net Present Value  All Paths 0.8 -  ,'S  Longest Path  0.6  0.4  0.2  .4?  10  15  20  25  30  Discount Rate (%) Figure 7.40: CDFs for Project Internal Rate of Return  35  Chapter  7.6.4  7.  Validations  and  Apphcations  218  Discussion  The precedence network for the engineering project used as the example in this section was small (see figure 7.23), with few interrelationships between work packages. This feature had both advantages and disadvantages. The advantages were, because of its size it was possible to elaborate the work package durations and costs to detailed functions, thereby demonstrating the full potential of the analytical method. Also, it was possible to illustrate the treatment of correlations at all levels of the project economic structure. For example, if the precedence network was highly interrelated with a large number of paths to complete the project as in the first example, it would not have been feasible to illustrate the positive definite correlation matrices for work package durations on individual paths and for work package costs. The disadvantage is that the elaboration of work package cost functions and the few interrelationships between work packages, combined to approximate extremely tight bounds for economic variables. Thereby, hampering the validation process. There were 210 random primary variables at the input level. The comparisons of execution times for the simulation and the analytical method highhghted the computational economy of the analytical method. The treatment of correlations between work package durations and between work package costs clearly demonstrated their significance.  7.7  Sensitivity Analysis and Contingency  This section will briefly discuss the different ways in which the analytical method can perform sensitivity analysis and use one of them to outline a method to distribute the contingency allocated to a derived variable to its primary variables. Current dollar project cost is used as the example for the derived variable.  Chapter  7.  7.7.1  Validations  and  Apphcations  219  Sensitivity Analysis  The concept of sensitivity analysis is simple. If a change in a primary variable has little effect on the derived variable, then the estimate for the derived variable is not hkely to depend to any great extent on the accuracy of the estimate for that primary variable. On the other hand, if a change in a primary variable produces a large change in the estimate for the derived variable, then the uncertainty surrounding that primary variable may well be a significant consideration when evaluating the derived variable. The sensitivity of a primary variable is measured by the total sensitivity coefficient for that variable. For a functional relationship given by, Y — <7(X), the sensitivity of the derived variable with respect to the primary variables is given by (Russell, 1985),  f  AY  where —y- and  " ?  S, %  (7.8)  AX1  are the percent changes in Y and Xi respectively, and Si is  the total sensitivity coefficient of X,-. For the sensitivity plot, Si is the gradient of the sensitivity hne relating percent change of Xi to percent change in Y. The total sensitivity coefficient Si is defined as (Russell, 1985), b l  -  dXi Y  ( 7  '  9 )  BY where 4£^- is the sensitivity coefficient of Y with respect to Xi. Since moment analysis is based on the truncated Taylor series expansion of £f(X), the partial derivatives with respect to primary variables should be evaluated. However, the analytical method transforms the primary variables X to Z and #(X) to G(Z) prior to using the Taylor series expansion. Even though the sensitivity coefficients  Chapter 7. Validations and Apphcations  220  T£^- are not evaluated by the analytical method it still evaluates |^f> sensitivity coefficients with respect to the transformed variables. Hence, the analytical method has an in-built sensitivity analysis process, whereby the sensitivity coefficients either increase or decrease the contribution of each term, depending on the importance of each transformed variable to the derived variable. Nevertheless, the sensitivity plot of -y- versus  * can be developed by obtain-  ing a range of outputs at different percent changes of X{. This can be a rather long process. However, since the analytical method is efficient and computationally economical, if desired it can be developed. Similar sensitivity analysis can be performed on the subjective estimates for primary variables. If the analyst requires a sensitivity analysis on the subjective estimates, again a sensitivity plot can be developed from a range of outputs at percent changes of subjective estimates. Since the objectives of this thesis do not require the validation of the input primary variables, such a study is not presented. The third sensitivity analysis performed by the analytical method is on the transitional correlation (p) specified by the analyst. The bounds for time and economic variables recognize the high degree of uncertainty associated with the decisions that have to be made during the feasibility analysis. By the definition of risk analysis, the quantification of risk for a specified transitional correlation should encompass the uncertainty of the assumed scenarios. However, the bounds add further reliability to the quantification because they are the true analytical bounds for those assumed scenarios. The fourth way in which the analytical method can perform sensitivity analysis is the basis for the method to distribute the contingency allocated to a derived variable to its primary variables. The derived variables at the project performance level are all linear additive and the sensitivity coefficients with respect to primary variables are  Chapter 7. Vahdations and Apphcations  221  e q u a l t o one. W h e n t h e u n c e r t a i n t y i n t h e d e r i v e d v a r i a b l e is d u e t o t h e u n c e r t a i n t y of t h e p r i m a r y variables alone (no effects d u e t o c o r r e l a t i o n ) , t h e v a r i a n c e o f Y is t h e s u m m a t i o n o f t h e variances o f t h e p r i m a r y variables.  T h e allocated contingency can  t h e n b e d i s t r i b u t e d t o the p r i m a r y variables o n t h e basis o f t h e i n d i v i d u a l percentage c o n t r i b u t i o n s t o t h e t o t a l u n c e r t a i n t y o f t h e d e r i v e d variable.  7.7.2  Distribution of Contingency  T h e contingency  is generally defined as t h e a m o u n t i n c l u d e d i n a n estimate  t h e overruns d u e t o unforeseen items a n d events i n t h e d e n n e d project this a l l o c a t i o n is d o n e for d e r i v e d variables s u c h as project agement  is i m p o r t a n t .  T h e abihty  scope.  Since,  d u r a t i o n o r cost, its m a n -  to distribute the contingency  p r o v i d e a l o g i c a l basis t o m a n a g e i t . T h e objective  t o cover  t o w o r k packages  o f this section is t o d e m o n s t r a t e  a n a n a l y t i c a l m e t h o d t o d i s t r i b u t e t h e contingency. Inyang  (1983)  C = X where Xc estimate  (C)  derived the contingency  C  as,  - E  is t h e target cost a n d EB is t h e base estimate cost t o t h e e x p e c t e d  a s s u m e t h a t t h e project  cost Xc  (1982),  cost.  H e preferred t h e base  b e c a u s e i t was necessary t o  cost was n o r m a l l y d i s t r i b u t e d t o derive t h e c o n t i n g e n c y a n d  because t h e base estimate t h e target  value used b y Y e o  (7.10)  B  cost is always smaller t h a n t h e e x p e c t e d  was n o t r e l a t e d  to any probabihty  o f success  value.  However,  (or failure).  As  h i g h l i g h t e d i n t h e i n t r o d u c t i o n , i n s t i t u t i o n s s u c h as t h e W o r l d B a n k n o w r e c o m m e n d the use o f p r o b a b i l i t i e s o f success (or failure) for p e r f o r m a n c e T h i s thesis derives t h e c o n t i n g e n c y  variables.  (C) as,  C = Xp - E[PC)  (7.11)  Chapter 7. Validations and Apphcations  222  where Xp is the cost estimate to achieve a desired probability of success and E[PC] is the expected value of project cost. Consider the current dollar project cost at the transitional correlation of p = 0.5 for the second example. Table 7.35 contains the values for Xp, C and the percentage of the contingency to the expected value {^c] 0.9 and Pr.[PC]  x  1 0  °)  for  Pr.[PC] < 0.75, Pr.[PC) <  < 0.95 for two cases. The first case has considered only the cor-  relation between primary variables (the correlation treatment that the Monte Carlo simulation can duplicate) and the second has treated the correlation between primary variables, work package durations and work package costs. The expected values for project cost from both cases are identical (see Table 7.34). However, the variance for the second case has increased by about 200%. This is reflected in the values for Xp and C, where to achieve the same probability of success the contingencies have to be increased by about 80%. For example, if the contingency was set at a 90% probability of success using the results from the first case, in reality the project cost has only about a 75% probability of success. This example, again highlights the significance of the correlation between work package costs. When the contingencies are compared as percentages of the expected value, the insufficiency of the traditional allocations of 10% to 15% is clearly demonstrated. Table 7.35: Xp, C and £ E  c 1  for Different Probabilities of Success  Correlations Scenario  Primary Variables Only  Pr.[PC] < 0.75 Pr.[PC] < 0.9 Pr.{PC) < 0.95  58470505 4956971 9.26 63243785 9730251 18.2 66037795 12524261 23.4  X  P  C  %  Primary k, Derived Variables X  P  C  62088867 8575333 70581087 17067553 75632830 22119296  %  16.0 31.9 41.3  The main advantage of this definition is that the contingency distributed to individual work packages can be used to predict their probabilities of success (or failure).  Chapter 7. Vahdations and Apphcations  223  This provides the bench mark to manage the contingency. Since the analytical method evaluates the expected value of project cost as the summation of all the expected values of work package costs, equation (7.11) can be re-written as,  X  where E[WPCcDi]  p  = f2(E[WPC }  + CON )  CDi  is the expected value of the i  th  the contingency distributed to the i  th  (7.12)  {  work package cost and CONi is  work package on the basis of its percentage  contribution to the variance of project cost. Then, E[WPCcDi]  + CONi, is the  amount available for the cost of defined scope and unforeseen items and events of the i  th  work package. The probabihty of success of this amount can be measured  from cumulative distribution function for the i  th  work package cost. Not only does it  provide a bench mark to manage the contingency but also allows the project manager to transfer contingency between work packages on a logical basis. Consider the second case in Table 7.35. The derived variable is the current dollar project cost while the primary variables are work package costs. The expected values, standard deviations, coefficients of variations, skewness, kurtosis and the percentage contributions to the variance of the project cost from individual work package costs are given in Table 7.36. The percentage contributions to variance of project cost is evaluated from the following function.  % Contribution from WPC Di C  =  n  ^  £ i=l  where pz(WPCcDi)  is the variance of the i  th  W  P  C  c  D  i  (7.13)  ^  p (WPC Di) 2  C  work package cost.  Figure (7.41) depicts the cumulative distribution function for the current dollar project cost at p = 0.5, and the Xp values given in Table 7.35. The values for the  Chapter 7. Validations and Apphcations  224  Table 7.36: Statistics for Current Dollar Work Package Cost - Ex #2 WP# 02 03 04 05 06 07 08 09 10 11 12 13 14  E[WPC]  3894677 6137617 7877801 1723771 917295 2146194 3667912 4080786 1728653 2370725 8220980 2397196 8349926  O~WPC  1575760 2237672 3215341 701822 353412 959195 1486228 1677239 697393 852850 3370799 1037134 3886408  C.O.V % 40.46 36.45 40.81 40.71 38.52 44.69 40.52 41.10 40.34 35.97 41.00 43.26 46.54  2  3  0.381 0.818 0.387 0.503 1.317 1.001 1.572 0.406 1.256 0.344 0.404 0.859 0.578  2.174 2.802 2.179 2.304 4.082 3.204 4.967 2.198 3.892 2.142 2.196 2.885 2.401  % Cont. 4.67 9.42 19.45 0.93 0.25 1.73 4.15 5.29 0.92 1.37 21.38 2.02 28.42  contingencies distributed on the basis of percentage contributions, the total amount available for the defined scope and unforeseen items, and the probabilities of success for individual work packages based on those total amounts for the three scenarios of Pr.[PC)  < 0.75, Pr.[PC]  < 0.9 and Pr.[PC]  < 0.95 are given in Table 7.37.  Figure (7.42) depicts the values for work package # 4 from Table 7.37. This work package was selected because it is an early work package in the network that has a high contribution to variance of project cost. A typical example of where things could go wrong. The values in Table 7.37 show that allocating contingency on the probabihty of success (or failure) of a global criterion such as project cost may not necessarily reflect the true situation because none of the work packages costs achieved the probabihty of success desired for the project cost. Analytically this can be reasoned that risks decrease when they are aggregated. In terms of practical situations, the importance of distributing the contingency becomes apparent. The contingency was distributed on the assumption that those work package cost  Chapter 7. Validations and Apphcations  225  Table 7.37: Distributed Contingency and Probability of Success Pr.[PC] < 0.75  Pr.[PC) < 0.9  Pr.[PC] < 0.95  Total Total Total < Contn. < Contn. < # Contn. 02 400468 4295145 .61 797055 4691732 .68 1032971 4927648 .72 03 807796 6945413 .67 1607763 7745380 .76 2083637 8221254 .80 04 1667902 9545703 .68 3319639 11197440 .81 4302203 12180004 .87 05 79752 1803523 .58 158728 1882499 .61 205710 1929481 .63 06 21440 938735 .62 42672 959967 .64 55300 972595 .65 07 148353 2294547 .63 295269 2441463 .67 382664 2528858 .69 08 355876 4023788 .68 708303 4376215 .75 17951 4585863 .77 09 453635 4534421 .61 902873 4983659 .69 1170110 5250896 .74 10 78893 1807546 .63 157021 1885674 .66 203497 1932150 .68 11 117482 2488207 .57 233825 2604550 .61 303034 2673759 .63 12 1833406 10054386 .69 3649043 11870023 .82 4729105 12950085 .89 13 173221 2570417 .62 344764 2741960 .66 446810 2844006 .69 14 2437109 10787035 .73 4850598 13200524 .85 6286304 14636230 .91  variances which contribute most to the variance of project cost cause most of the uncertainty in the project cost. Therefore, the distribution ensured that the work packages with higher contributions had the greater probability of success and vice versa. Having got the initial bench marks to reflect the reasoning for the distribution, it is now possible to transfer some of the contingency from work package costs that have a greater probability of success to those which have a greater probability of failure. Unlike project cost, the distribution of contingency for project duration is not straightforward because the project duration is not a summation of all the work package durations. However, the modified PNET algorithm does permit a basis for an approach. Since the variances of all the paths to complete the project are evaluated when the transitional correlation p = 1, a sensitivity analysis similar to that adopted for the project cost can be utilized.  Chapter 7. Validations and Applications  226  Cost (current $*10 ) 8  Figure 7.41: CDF for Current Dollar Project Cost  17.5 Cost (current $*10 ) 6  Figure 7.42: CDF for Current Dollar Cost for Work Package #4  Chapter 7. Validations and Apphcations  Consider the j  th  227  path of the precedence network. The variance for the j  path  when the correlations between work package durations are not considered is given by,  (7.14)  i=l where  p-i{WPDij)  is the variance of the i  th  work package duration on the j  th  path.  The contingency allocated for project duration can then be distributed to the work package durations on the j  th  the variance of j  th  path based on the individual percent contributions to  path duration. Then similar to work package costs it is possible to  measure the probability of success of individual work package durations for defined scope and unforeseen items and events. However, since a work package can be on more than one path it can have a number of distributed contingency durations. In such situations the lowest distributed contingency should be assumed as the duration for unforeseen items and events. The measured probabilities of success will then be the lowest for every work package duration in the network. Again, providing a bench mark to manage the contingency allocated for project duration.  7.8  Summary  This chapter described the validations and the applications of the analytical method developed in the previous chapter. The validations of the analytical method was performed by using Monte Carlo simulations. The simulations were used because at present, simulation based models are considered to be the "state-of-the-art" for quantification of time and economic-risks in large engineering projects.  Chapter 7. Vahdations and Apphcations  228  The second section of this chapter derived the random number modification process used to treat correlations between variables in Monte Carlo simulation. The number of iterations for an "acceptable" simulation should be based on the standard error for the expected value and standard deviation, and the error band for the cumulative distribution function generated from the simulation. The modified PNET algorithm was validated by solving the two examples presented by Ang et al., (1975). The comparison of the results showed that modified PNET identifies the individual paths correctly, evaluates the expected value (mean) and standard deviation for path durations accurately, and selects the representative paths correctly. The ordering of paths may differ because in addition to ordering the paths in decreasing mean durations, the modified PNET orders the paths in decreasing standard deviations when mean path durations are equal. This ensures the selection of the path with the highest variance as the representative path from the paths having the same mean duration. The project duration of a parallel network was used as the first limiting case to validate the Monte Carlo simulation process. When simulations are used to validate an analytical approach it is essential that the simulation process is first validated. A single dominant path of a highly interrelated network was used as the second limiting case. The simulations for both cases behaved as predicted thereby validating the Monte Carlo simulation process. The first example for validating the analytical method was an actual feasibility study of a mineral project. This example had 164 random primary variables at the input level and four simulations were performed. Two were for the case when the coefficients of variations for work package durations were low (first validation), while the others were for the case when the coefficients of variations for work package durations were approximately 40% (second validation).  Chapter 7. Validations and Applications  229  In both instances, the cumulative distribution functions and the estimates for expected values for derived time and economic variables generated from simulations were within the upper and lower bounds predicted by the analytical method. Thereby, validating the analytical method. The computational economy of the analytical method was highlighted from the comparisons of execution times. A brief comparison with deterministic results and when p = 0.5 were done for the first and second validations respectively. The second example was an hypothetical engineering project developed to demonstrate the full potential of the analytical method. The project network was small (thirteen work packages) with few interrelationships between work packages. Elaborate functions were used for work package durations, costs and revenue streams. There were 210 random primary variables at the input level. Two complete simulations for time and economic risk quantifications were done. The first assumed that all the primary variables were uncorrelated (third vahdation) while the second assumed that the primary variables in the functions for work package durations and costs were correlated (fourth vahdation). The bounds for derived economic variables predicted by the analytical method were extremely tight, because the work package start time was one of the seventeen variables in the function for work package cost and because of the few interrelationships between work packages. The simulations demonstrated the validity of the analytical method by generating estimates and cumulative distribution functions similar to those from the analytical method. The third section demonstrated the treatment of hnear correlations between work package durations and between work package costs when evaluating the moments for project duration and costs. The analytical method approximates these correlations using the hnear correlations between the primary variables when the common  Chapter 7. Validations and Apphcations  230  (shared) variables are identified. The positive definite correlation matrices developed by the analytical method were given. The Monte Carlo simulation does not have the capability to duplicate this treatment. A comparison of the statistics and the contingencies that should be allocated to achieve a desired probability of success highlighted the significance of the effect of these correlations. The standard deviations for project duration and current dollar cost increased nearly 18% and 75% respectively. The final section described the four ways in which the analytical method can and/or do perform sensitivity analyses, and used the fourth approach to develop an analytical basis to distribute the contingency allocated at a desired probability of success. The distribution of contingencies allocated for Pr.[PC] < 0.75, Pr.[PC] < 0.9 and Pr.[PC] < 0.95 of current dollar project cost showed that none of the work packages achieved the same probability of success. This distribution is biased towards the work packages costs with variances that contribute most to project variance, giving them the greatest probability of success. Overall, this chapter demonstrated the validity and the computational economy of the analytical method in the quantification of time and economic variables in large engineering projects.  Chapter 8 Conclusions and Recommendations  8.1  Conclusions  The primary objectives of this thesis were to develop an analytical method for economic risk quantification during feasibihty analysis for large engineering projects and to computerize the method to explore its behavior, to validate it and to test its practicahty in the measurement of uncertainty of decision variables. The secondary objective was to lay the foundation for obtaining the input data necessary to make the analytical method a practical tool for the construction industry. The main conclusions from the developments of this thesis are as follows. 1. The analytical method is a comprehensive alternative to Monte Carlo simulation for the quantification of time and economic risks in large engineering projects. 2. The start times of work packages and revenue streams evaluated from the analysis of the precedence network provided the hnk to model the interaction of time, cost and revenue throughout the hfe cycle of a project. 3. The definition of the project economic structure and the freedom to use any type of functional form for work package durations, costs and revenue streams provided the freedom to model a project realistically to any level of detail using any number of variables. 231  Chapter 8. Conclusions and Recommendations  232  4. The risk measurement framework is suitable for systems where predetermined functional forms are available, data limitations exist and the decisions are not based on extreme probabilities. 5. The reliance on subjective probabilities to obtain data for the primary variables at the input level recognized the data limitations that exist during the feasibility analysis. The elicitation of accurate, calibrated and coherent subjective probabilities as the measurement of expert belief incorporated the theoretical requirements into the practical process. 6. It was concluded that when eliciting subjective estimates for duration, neither the holistic nor the decomposed estimation was the "better" approach. 7. The consideration of multiple paths of the project network provided a more realistic evaluation of the statistics for work package start time. In addition, the modified PNET algorithm provided the basis to evaluate the true analytical bounds for derived time and economic variables. 8. The uncertainty of the project performance and decision variables were quantified by consistently utilizing the moment analysis approach with the Pearson family of distributions. 9. The correlations between variables was identified as an important feature of this problem. The variable transformation approach developed to treat the correlations between primary variables and between derived variables was found to be accurate and robust. 10. The elicitation of positive definite correlation matrices for primary variables in the functional forms at the input level incorporated an important theoretical requirement into the practical application.  Chapter 8. Conclusions and Recommendations  233  11. The approximation and the treatment of correlations between work package durations, between work package costs and between revenue streams demonstrated the abihty of the analytical method to go beyond the capabilities of the Monte Carlo simulation process. 12. It was concluded that during the feasibility analysis work package concept can be utihzed as the approach to obtain intermediate milestone information to set realistic targets for performance. 13. The quantification of uncertainty of project time and economic variables provided the basis to answer such strategic questions as setting up of the contingency for a probabihty of success (or failure) and the reliability of the "go - no go" decision. The individual contributions to the overall uncertainty was used to distribute the contingency for project variables to work packages. It was found that the probabihty of success predicted at the project level was not achieved at the work package level. 14. When the starting points were identical, the results from the Monte Carlo simulations were within the upper and lower bounds predicted by the analytical method. From the validations it was concluded that the analytical method had theflexibilityto model and evaluate the derived time and economic variables of a project accurately and economically. The analytical method and the computer programs developed from this research achieved the objectives of this thesis. However, in terms of the total process of risk management for large engineering projects, these developments are only the beginning. Unless there is an efficient approach to quantify time and economic risks it is impossible to respond to the identified risks. On other hand, until the area of risk response is developed the quantifications are of httle use. The recommendations for  Chapter 8. Conclusions and Recommendations  234  future research briefly highlight the scope of the work that is necessary to make this development a practical tool for engineering construction.  8.2  Recommendations for Future Work  Recommendations for future work are identified under three sections, namely, the analytical method, computer programs and the risk management process.  8.2.1  Analytical Method  The analytical method developed in this thesis consisted of a number of major building blocks - project economic structure; risk measurement framework; elicitation of subjective probabilities and positive definite correlation matrices; treatment of correlations between variables; and the modified PNET algorithm. 1. Project Economic Structure : It is recommended that a suite of time, cost and revenue estimating relationships at the work package/revenue stream level be developed. This would significantly increase the ability of the analytical method to model large engineering projects realistically. The publications by Tanchoco et al., (1981) and Buck, (1989) are useful starting points. 2. Risk Measurement Framework : At present the approximations for the first four moments of the derived variable consider terms only up to the fourth order. However, since all the primary variables are approximated to Pearson type distributions, it is possible to obtain the higher order moments from the recurrence property of the Pearson family. It is recommended that with practical experience in the elicitation of subjective probabilities terms up to the eighth order be included. This would  Chapter 8. Conclusions and Recommendations  235  ensure more accurate approximations for the first four moments of the derived variables at the work package/revenue stream level because all of the necessary terms are included. 3. Elicitation of Subjective Probabilities : The development in this thesis was the foundation for obtaining input data. The next stage should concentrate on building up experience from field apphcations, refining and validating the ehcitation approach. To this end, a complete automation and pre-testing of the process is recommended. With experience from field apphcations, the process can be refined based on the performance of analysts and experts. Also, the development of calibration curves for vahdation is recommended. The pubhcations by Budescu and Wallsten, (1987), Phillips, (1987), Wallsten and Budescu, (1983), Murphy and Winkler, (1984), and Wright and Ayton, (1987) are useful starting points. 4. Elicitation of Correlation Matrices : A more consistent approach to ehcit the correlation coefficients between variables is necessary. It is recommended that effort should be devoted towards developing questions that would better capture the expert's knowledge about correlated variables. The pubhcations by Inyang (1983), Hull (1977), Kadane et al., (1980) and Keefer and Bodily, (1983) are good starting points. The routine in the interactive computer program "ELICIT" that ensures the positive definiteness of the correlation matrix should be further refined with a better user interface. 5. Correlations between Variables : The variable transformation approach described in this thesis was found to be both accurate and robust in the treatment of correlations between variables. More studies to test  Chapter 8. Conclusions and Recommendations  236  and to further understand the transformation is recommended. The approximation and the treatment of correlations between derived variables require further studies to understand the benefits from the transformation. 6. Modified P N E T Algorithm : At present the modified algorithm treats only finish  to start — 0 relationships. The extension of the modified  PNET algorithm to treat overlapping relationships will increase the versatility of the modelling capability. This extension however, requires the development of an algorithm to treat the correlations between work package durations in overlapping relationships. It is strongly recommended that future efforts be devoted to developing such an algorithm. Ideally, a single value for the transitional correlation p that can be used for all economic risk analyses of engineering projects should be recommended. However, at present it is not possible to make this recommendation. Efforts should be devoted to deriving such a value. The study of the behavior of the modified PNET algorithm for highly interrelated networks versus those with few relationships (hnear networks in pipeline or highway projects) is the logical starting point.  8.2.2  Computer Programs  One of the primary objectives of this thesis was to computerize the analytical method to explore its behavior, to validate it and to test its practicahty in the measurement of uncertainty. The two computer programs developed by this research facihtated the achievement of this objective. While the computer programs were not meant to be software development, they are a useful starting point for a software package. At present, both programs, "ELICIT" and "TIERA", lack sophistication especially in the area of user friendliness. It is recommended that efforts be devoted to  Chapter 8. Conclusions and Recommendations  237  achieving a higher degree sophistication, for this development to become a practical tool for decision makers in engineering construction.  8.2.3  Risk Management Process  In the introduction, the process of risk identification, risk quantification and risk response was identified as the most suitable approach for risk management in engineering construction. This thesis presented a computationally economical approach that can be used develop the basis for decision makers to respond to the identified risks. Until the area of risk response is developed the quantifications of derived time and economic risks are of little use. The next stage of this research should concentrate on developing strategies to respond to the quantified risks. It is strongly recommended that efforts be devoted towards this end. 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Then the function becomes Y = G(Z) where Z is the vector of transformed uncorrelated variables.  A.2  Expected Value  From equation (2.20) the expected value of Y is,  E[Y)  * <7(X) +  Q^T  252  coviXttXj)  (A.2)  Appendix  A.  253  The First Four Moments  In the transformed system the variables are uncorrelated. When using the transformed system function G(Z) the expected value of Y is (equation 2.36),  *  E[Y]  A.3  +  G(Z)  \ t ^  (A.3)  l*(Zi)  Second Central Moment  From equation (2.21) the second central moment of Y is,  P2{Y)  =  E  1  n  8a  n  2  + 2 Et hE dXJXj 3 in n dg -Y Y  (AA)  9  ~  (Xi  X  i  )  (  X  j  2  h  2 ti  p (Y) 2  =  E  d  x  ^ ^ W i W ; E  E  E  X  i  "  i  X  n  n  + I E  Q^:  QXjdx  i  ^  k  E ^ T Q£§X;  " E E 1  {  cov(Xi,Xj) x  )  {  X  ~  j  X  j  )  ^ ^  X  n n  n  +  <  d  n  E  E  X  i  ~  j  {  V  k  n  n  n  k  ~  X  k  ^  (A.5)  dg dXidXj  -  X ) (X k  t  -  2  dX dX k  (Xi - Xi) (Xj t  x n  X  (^i  -  t  k  0  ^ ^  j  2  x (X  -j^ n n n n  X  k  d 9 flvflv. fl .flv. dXidXj dX dX 2  E  ~  i  (Xi - X ) cov(Xj,X )  d9  n  X  d9 2  dXidXj  d dX dX, 2  9  k  cov(X ,Xi) k  Xj)  Xt)  Appendix A. The First Four Moments  254  Neglecting the cross moment terms in equation (A.5) that cannot be defined due to the lack of moment information,  'dgj  E  M{Y)  dXi  t=i  ^  t  t  ^  ^  X  ,  )  dg d g  "  2  ^ 6Xi 8X?  dg dxf 2  1  p (Xi) 4  n  - - Y  2  (A.6)  8Xf  h n  dg  n  2  E E  - 2  d g1 2  1  2  M*)]  S  <9A?  E E i=i  55  T  2  l n  n  dX? ^  h  \d g] 2  i=i  E  {  X  i  (A.7)  )  2  p {X )  [dX?\  £  [coviXi.Xif  dXidXj  j = i + i  E i =  [coviX^Xjf  A  dg  {  -  \p, (Xi)f 2  2  dXidXj  [covtXitXjtf  Appendix A. The First Four Moments  255  In the transformed system the variables are uncorrelated. When using the transformed system function G(Z)  the second central moment of Y is (equation 2.37),  dG_  E  MY)  M  dZi  z  i )  t=i " dG d G 1^ 9Zi ^T2 M ^ i ) dzt 2  +  1  n  (A.8)  2  ' dG 2  p (z )  dzt  4  -  {  IMZi)}'  If all the correlated primary variables are normally distributed or if it is assumed that there are no non-linear correlations between the transformed variables,  dG_  E  M Y )  i=l "  1  M  dZi  dG  i )  dG 2  dG  n  z  2  (A.9)  1 2  M Z i )  ^ i=l L dZi  +E E i=i  A.4  dG 2  dZidZj  -  [MZi)Y  12 M  z  i )  M J) Z  j=i+i  Third Central Moment  From equation (2.21) the third central moment of Y is, M Y )  =  E  -  Xi)  (A.10)  Appendix A. The First Four Moments  256  Neglecting fifth and higher order terms in equation (A.10),  n  E  pi +  n n * T i *=i i dX  dX  i  dX  k  *k 9x ix  ^S S S £ ^  d k  ~  {Xi  l  x (X 3  f v f v  a  5  89  Xi)  -  {Xj  j  - X ) {Xt -  k  k  (x x)(x-  929  X  x cov(X ,X )] k  )  X) t  x) (A.ll)  l  Neglecting the cross moment terms in equation (A.ll) that cannot be denned due to the lack of moment information,  dg_ dXi  E  i=l  dg dXi  2  fe  &g_ dX? a  Mi  12  n  + -  (A.12)  <9  5  dg 2  5  ^  9X8X-  JUL* ,2  p (Z )  W  »  X  ^  X  dg dXi  E o  &g_ p (Zi) dX? A  "  fe  Pz{Y)  12  dg_ dXi  --E ^  t*(Xi)  fe  E  dg_ dXi  "  " Mi  dX  r  8g oxi  A  dg Mi  {  -  (A.13)  ^ (^)]  s  2  dg  .  2  ^dX,  f  ^  ,  ^  )  ]  Appendix A. The First Four Moments  257  In the transformed system the variables are uncorrelated. When using the transformed system function G(Z) the third central moment of Y is (equation 2.38),  I 3  E i=l  3" +  dG_  M i) z  L dZi  8G  dG  2  2  5 g dZi  dZf  p {Z ) - [p (Zi)f 4  {  (A.14)  2  If all the correlated primary variables are normally distributed or if it is assumed that there are no non-linear correlations between the transformed variables,  13  dG E dZi M i) i=l dG d G + - E dZi dzf p (Zi) „ " " dG dG d G  MY)  z  2  4  2  A.5  Fourth Central Moment  From equation (2.23) the fourth central moment of Y is,  MY)  =  E  - 2E E fe  S'g S^flA-i  (A.15)  - MZ;)f  cov(Xi,Xj)  ,„  N  Appendix A. The First Four Moments  258  Neglecting fifth and higher order terms in equation (A.16),  n  MY)  «  E  n  n  n  £ S£ £ m  «f7 ^ ^  x (X„ -  (  X  i  X ) (X, -  "*  , )( X i  A',)]  k  " **  f  (A.17)  Neglecting the cross moment terms in equation (A.17) that cannot be denned,  «  MY)  £  dg 8Xi  (A.18)  u (Xi) 4  In the transformed system the variables are uncorrelated. When using the transformed system function G(Z) the fourth central moment of Y is (equation 2.39),  «  MY)  dG dZi  £ i= l  M  Z  (A.19)  i )  If all the correlated primary variables are normally distributed or if it is assumed that there are no non-linear correlations between the transformed variables,  MY)  *  dG 1 4 M dZi  £  n  + 6 E  E n  t=i j=t+i  z  (A.20)  i )  dG' dZi  2  <9^  M i) z  M J) Z  Appendix A. The First Four Moments  A.6  259  Note : Higher Order Moments  The fifth and higher order terms are neglected in the approximations for the third and fourth central moments of a derived variable because moment information of the primary variables are not available beyond the fourth order (section  2.3.2).  However,  since all of the primary variables are approximated by Pearson type distributions, if required, it is possible to generate higher order central moments for the primary variables from the recurrence property of the Pearson family (Kendall and Stuart,  1969).  The recurrence relationship for fifth and higher order central moments for primary variables approximated by Pearson type distributions is (Pearson, 1963; Kendall and Stuart,  1969),  [o - (n + 1) c*i] -  p  (n + 2)fc  - nb  n  0  + 1  2  p^ n  ( A  "  2 1 )  where p (/* + Zu\) 3  a =  I0p p 4  h  4  — 18/4  —  2  12/i|  (A.22)  /*2 ( 4 / ^ 4 ~ 3/ig)  °°  -  6,  =  10p p 4  -  2  /*3 2  12M!  Zp\) 18p - Ylp\ 3  2  (2p p - 3/tg - 6pp I0p p — 18p — 12/zf, 2  4  4  2  {  '  y  " '  (A*4 +  10p p 4  18,*! -  2  (A.25)  Appendix B Investigation of R Q This appendix investigates the positive semi-definite correlation matrix R o discussed in section (4.2.1). The correlation matrix is as follows.  =  Ro  •1.0  0.5  0.5 "  0.5  1.0  .0.5  -0.5  -0.5  (B.l)  1.0 .  Since the correlation matrix is in the standardized space, E [Xi] = 0 and fi (Xi) 2  = 1.  The variances of the linear combinations of variables are, )i (X 2  + X ) = i (X )  l  2  p (X 2  x  \i (X 2  2  + *s)=  + p (X )  + 2Cov(X ,X )  + 1  (B.2)  /* (X ) + fi (X )  + 2Cov(X ,X )  + 1  (B.3)  + fj, {X ) + 2Cov(X ,X )  = 1  (B.4)  f 2  1  2  2  1  2  + X ) = u (X ) 3  2  2  2  1  3  2  2  1  3  2  3  3  Therefore, only the linear combination of variables 2 and 3 is valid. Hence, CovjX^X. Pl,2+3 =  ,  ^fi ( i)  + X,) =  MX  x  2  2  + X)  (B.5J  3  Since /z (X ) = 1 and u. (X + X ) = 1 2  1  2  2  pi,  2+3  3  = Cov(X ,X +X ) 1  260  2  3  (B.6)  Appendix B. Investigation of RQ  261  From definition,  Cov{X X u  2  + X)  = £ [ A \ ( X + X ) ] - £[A\] E[X + X ]  3  2  3  2  =  E[X X ] + E[X X ]  =  E[Xr]E[X ] + Cov{X X )  X  2  1  (B.7)  3  3  2  u  + £[AM£[X ] +  2  3  Cov{X X ) u  3  Therefore,  Cov(X ,X +X ) 1  2  = Cov(X ,X )  3  1  + Cov(X X )  2  u  3  (B.8)  From definition, Cov(X X ) u  =  2  Cov{X X ) u  3  =  p  1|2  P l < 3  Jn (Xi)  ^(X*)  = 0.5  (B.9)  y/^iXi)  p {X )  = 0.5  (B.10)  2  2  3  From equations (B.6), (B.8), (B.9) and (B.10),  ^  > 2 + 3  =  Cov(X X ) lt  2  + Cov{X X ) u  3  = 1  (B.ll)  Variable 1 is perfectly correlated with the linear combination of Variables 2 and 3.  Appendix  C  B o u n d s for a C o r r e l a t i o n Coefficient This appendix derives the bounds for a correlation coefficient to exist in a positive definite correlation matrix. The bounds are derived from the necessary condition on vector b (see equation matrix R  C.17) given by Kadane et al., (1980), for a nxn correlation  to be positive definite when R - i is positive definite. The proof for that  n  n  condition was derived by Dr. Ricardo 0. Foschi during the review period of this thesis. His contribution which is described in the first section is gratefully acknowledged.  C.l Let R  The Proof n  be a nxn correlation matrix partitioned as, R n - l  R n  where R - i n  b  T  is  =  (n — l)x(n — 1)  = [pin P2n  b  (C.l)  1 correlation matrix for  n  2,3,,  and  Pn-ln]-  Let R _ i be positive definite. For any vector of n scalars x n  R n - l  [  x  n - l  b  x _i" n  Z n J  x  > 0  (C.2)  n  where x £ _ = [x x x  x  z -i]-  2  n  Expanding equation (C.2), xj,!  R  n  _ i  x _i n  + 2 x xj_ b + x n  262  x  2 n  > 0  (C.3)  Appendix C. Bounds for a Correlation Coefficient  The roots for x  263  from equation (C.3) are,  n  X  =  n  -  X n - l  b  ±  ^(xj.i  b)  -  2  xT_  x  R  n  _  x  x _!  (C.4)  n  For imaginary roots  x  b b  n - i  T  R  x _i  -  xj_j  [ R „ _ !  -  b b ]  n  n  x„_x < 0  _ i  (C.5)  Rewriting equation (C.5),  xj_!  Therefore, for R  x _  T  n  > 0  x  (C.6)  to be positive definite ( R _ i — b b ) must be positive definite. T  n  n  Then, for any vector of n — 1 scalars y,  y  [ R  A  _  n  -  x  y > 0  b b\ x  Since R - i is symmetric R n - i is also symmetric. Choosing n  y  T  = b  ( R ^ ) *  T  =  b  (C.7) y = Rn-i  T ^  b  (  c  .  8  )  Substituting in equation (C.7), b  T  R - ^ [ R  n  _  -  x  R-l  b b ] T  x  b > 0  (C.9)  Expanding equation (C.9), [l - b  T  b] b  R - ^  T  R - ^  b > 0  (CIO)  Since R - i is positive definite, from Cholesky decomposition, n  R _ n  x  = L L  (C.ll)  T  Hence, R - ^  =  ( L - ^ L -  1  (C.12)  Appendix C. Bounds for a Correlation Coefficient  264  Therefore,  b Substituting  z = L  _  1  T  R-^ b = b  b and z b  Hence, R~^j  T  = b  T  T  T  (L" ) L- b 1  (L  R-^ b = z  T  )  - 1  T  m  (€.13)  1  equation (C.13)  z > 0  (C.14)  is also positive definite. From equation (C.10) l-b  K~\  T  b > 0  (C.15)  Therefore, the necessary condition on b when R - i is positive definite is n  b  C.2 R  n  T  R-\  b < 1.  (C.16)  The Bounds  is positive definite i f R - i is positive definite and, n  b and  T  R^  x  b < 1  b matrices i n equation (C.17), Si  B  T  (C.17)  : T :  Bi  < 1  r  s  2  B  2  where T is the correlation coefficient (pj ) for which bounds are required, n  Si is a (j — l ) x ( n — 1) matrix and S is a [n — 1 — j)x(n — 1) matrix, 2  B j and B  T  are lx(j — 1) and l x ( n — 1 — j) rcjw matrices, and  Sj is a l x ( n — 1) row matrix.  (C.18)  Appendix C. Bounds for a Correlation Coefficient  Multiplying b  by R ^ in equation (C.18),  T  i TSj  Ci  where  C  x  265  =  SI  and  C  i  C  < 1  2  (C.19)  = Bj S .  2  2  Since, Sj, Ci and C are lx(ra — 1) row matrices the quadratic equation (4.13) for 2  real bounds from equation (C.3) is,  s»  r  2  + [c  a j  +  c +£ s 2j  j{  t=l  u  Y,  +  n *]  s  B  r  n-1  j-1  + £  n-1  B  (C  lt  +  C) 2i  B  u  +  Y  i = j+l  (di  +  c  *)  B  2i  -  1 < 0  Appendix  D  The Computer Programs  D.l  General  One of the primary objectives of this research was to computerize the analytical method for economic risk analysis. The computer programs could then be used to explore its behavior, to verify it and to test its practicality in the measurement of uncertainty of performance and decision variables. This appendix describes the two computer programs, "ELICIT" and "TIERA", developed to achieve this objective. The two programs written in FORTRAN 77 can be executed together or separately.  D.2  ELICIT - Program to Obtain Input Data  ELICIT is an interactive program, to ensure that the subjective probabilities elicited for primary variables at the input level are coherent (section 3.7), their correlation matrices are positive definite (section 4.2.2), and to elicit the common (shared) variables in the functional forms. The flowchart for ELICIT is depicted in Figure (D.l). The objective of ELICIT is to obtain interactively all of the information necessary to set up the input files required to execute TIERA. ELICIT is developed in three sections - work package durations, work package costs and revenue streams. The program begins with work package durations and proceeds to the next module only if it is asked to. The output from the three sections are written to data files in Units  266  Appendix D. The Computer Programs  267  * # of Work Packages (Including Start & Finish) * Method of Duration Estimation NextWP * Common Variables In Functions  ' Subjective Estimates  * Positive Definite Correlation Matrix  # of Revenue Streams Yes  NextRS  ' Type of Functional Form  * C o m m o n Variables  ' # of Primary Variables  In Functional Forms  STOP * Positive Definite Correlation Matrix  Next V a r i a b l e !  Figure D.l: Flowchart for ELICIT  Appendix D. The Computer Programs  268  11, 12 and 13 which are the input files for TIERA. To ensure that the subjective percentile estimates for a primary variable are coherent ELICIT approximates them to a Pearson type distribution. The flowchart of this process is depicted in Figure (D.2). When the subjective estimates are coherent, ELICIT displays the expected value, standard deviation, skewness and kurtosis of the approximated Pearson distribution as verification and proceeds to the next variable. When the estimates are not coherent the analyst/expert are given an opportunity to re-estimate percentiles as suggested in section 3.7. To ensure that a correlation matrix is positive definite ELICIT follows the theoretical development described in section 4.2.2. When the vector of correlation coefficient values - b is elicited, the program checks for the condition given by equation (4.14). If the condition is satisfied the program accepts the b vector as valid correlation coefficients between the primary variables. When the condition is not satisfied the program informs the user that the theoretical requirement for a valid R has been violated, n  and requests the user to identify a previous variable in the ordered list whose correlation coefficient with the current variable that should be changed. Once the user has identified a variable, ELICIT calculates the bounds for that correlation coefficient (if they exist) from equation (4.15), thereby giving guidance for the user to conform to the theoretical requirement. Thirdly, the common (shared) variables between the functions for work package durations, the functions for work package costs and the functions for revenue streams are elicited. Utilizing this information, TIERA develops positive definite correlation matrices for work package durations, work package costs and revenue streams. These correlation matrices are then used in the evaluation of moments for path durations (hence project duration), project cost and project revenue respectively.  Appendix D. The Computer Programs  269  •\ * 5th Percentile Estimate (C)  * 25th Percentile Estimate  Yes  Approximate Expected Value a n d Standard Deviation {o-o  STOP  Q  gg)  Yes  | Obtain 2.5% and 97.5% Estimates Yes (Condition 1)  Subjective Estimates are Coherent  Display Expected Value, Standard Deviation, Skewness a n d Kurtosis  —r~  Next Variable  Figure D.2: Flowchart to Ensure Coherence of Subjective Estimates  Appendix D. The Computer Programs  D.3  270  T I E R A - Program for Risk Quantification  TIERA is the computer program of the analytical method developed in this thesis for time and economic risk quantification. It is developed in two modules. The main module follows Figure (6.3) and consists of all the analytical derivations described in chapter 6. Except for the reverse arrow in Figure (6.3), where thefirstfour moments for work package start times are evaluated from the modified PNET algorithm, all of the other arrows use the moments of the primary variables at the lower level to evaluate thefirstfour moments of the derived variables at the higher level. Theflowchartfor the modified PNET algorithm is depicted in Figure (D.3). When the transitional correlation, p = 0, the modified PNET algorithm defaults to the longest path approach because there is only one representative path. Then the process will always stop at the third decision node. Figure (D.4) depicts the flowchart for the process to trace all the paths to a work package (or milestone) from the start work package of the precedence network. The algorithm to trace all the paths to a work package was based on the "stack" concept. The second module for TIERA is an external subroutine consisting of functions for work package durations, work package costs and revenue streams that are specified by the analyst. At present, the analyst can specify five functions for work package durations, and ten each for work package costs and revenue streams. If more functions are needed for an analysis, the number can be increased with a small modification to the main program. The main program of TIERA should always be executed in combination with a compiled version of the external subroutine consisting of the functions. To execute TIERA the main program looks for data from Units 1, 10, 11, 12 and 13. The datafilecontaining the table of Pearson distributions should always be specified at Unit 1. At present the datafilecontains 2665 distributions. When the table  Appendix D. The Computer Programs  271  Read the Logical Relationships between Work Packages from Unit 10  I  Obtain all the Paths to a Work Package (see Figure D.4) Yes First Four Moments for Start Time of the W.P  STOP  Expected Value (EV) and Standard Deviation (SD) for all Path Durations Re-arrange Paths in the Decreasing Order of EV (Also Decreasing order of SD when EV are equal)  I  Calculate Correlation Coefficient for each pair of Paths Check Transitional Correlation P Select Representative Paths #of Yes First Four Moments for the Representative Paths, Representative Path Duration = 1^ 'No Evaluate the First Four Moments for STOP each Representative Path Obtain a Pearson Distribution for each Representative Path using its First Four Moments  \  Calculate t start Obtain Cumulative Distribution Function for Start Time by Evaluating p(t) for the range 0 £ p(t) £ 1 Evaluate First Four Moments for Start Time using Percentile Values from the developed Cumulative Distribution Function Figure D.3: Flowchart of the Modified PNET Algorithm  STOP  Appendix D. The Computer Programs  272  Put WP and its Predecessors on Stack and Temporary Counting Array of Predecessor #s.  STOP Decrease Successor's Predecessor #  Decrease Successor's Predecessor #  t  Yes  Take Start WP off Stack  I  Take Start WP off List  Take WP off Stack  I  Save List Add Start WP to List  Take WP off List  Store the WP on the List  I Store each Predecessor of the WP on the Stack  i  Set # of Predecessor WP's for each Predecessor WP on the Temporary Counting Array  Figure D.4: Flowchart to Trace all the Paths to a Work Package  Appendix D. The Computer Programs  273  developed by Amos and Daniel (1971) is included the file will contain approximately 12,100 distributions. The data file containing the logical relationships between work packages should be specified at Unit 10. At present only finish  to start = 0 re-  lationships between work packages are permitted. The data files for work package durations, work package costs and revenue streams should be specified at Units 11, 12 and 13 respectively. These data files are the output files from ELICIT. The output from TIERA is written to Unit 7. A typical output from TIERA is illustrated in Figure (D.5). The output is for the third case of the second example given in chapter 7 (i.e correlations between primary variables and between derived variables are treated). Units 5 and 6, the reading and writing units for FORTRAN are left free to permit the joint execution of ELICIT and TIERA. When the two programs are executed together the reading and writing for user - computer interaction are from Units 5 and 6 respectively.  Appendix D. The Computer Programs  Figure D.5: Typical Output from TIERA  274  g  OCT / 19B9  15.99  3. 10  0 .48  2.67  10  OCT / 1989  16.00  3.24  0 .71  2.86  11  OCT / 1989  16.00  3.24  0 .71  2.88  12  OCT / 1989  16.00  3.24  0 .71  2.88  13  MAY / 1990  23.00  4.24  0 .40  2.90  14  MAY / 1990  23.39  4.27  0 .50  3. 10  15  FEB / 1991  32.57  4.75  0 .40  2.90  PROJECT DURATION FORI A TRANSITIONAL CORRELATION OF 0.50 : THE TIME UNIT IS CALENDAR MONTH • *  • • EXPECTED VALUE • • •* STANDARD DEVIATION  FEB / 1991  32.57  4.75  SKEWNESS  • • • KURTOSIS  0.40  2.90  • • • WORK PACKAGE COSTS DISCOUNTED AT A RATE OF RETURN OF 0.090 • • •  f  P  • • • EXPECTED VALUE • * #  • ' • STANDARD DEVIATION • • •  SKEWNESS • * *  • • • KURTOSIS  1  0.  0.  0..000  0.000  2  3697832.  1523500.  0..398  2. 190  3  5578746.  2056956.  0..831  2.830  4  7125178.  2954487.  0. 401  2. 193  5  1524454.  625166.  0. 511  2.314  6  810978.  315346.  1. 324  4.105  7  1882968.  845204.  1. 007  3.218  8  3170606.  1307522.  1 .591  5.038  9  3505625.  1459957.  0. 419  2.211  10  1497887.  608433.  1. 261  3.909  11  2042131.  739623.  0. 343  2. 141  12  7037942.  2931777.  0. 420  2.211  13  1960845.  B65159.  0. 900  2.971  8824271.  3225952.  0.592  2.421  0.  0.  0.000  0.000  • • • • THE PROJECT COST DISCOUNTED AT A RATE OF RETURN OF O.OBO • • • • EXPECTED VALUE  STANDARD DEVIATION • • •  46659262.  11195712.  • • • SKEWNESS 0.26S  * • * NET REVENUE STREAMS DISCOUNTED AT A RATE OF RETURN OF 0.090 • * • EXPECTED VALUE  STANDARD DEVIATION • • •  KURTOSIS * • • 2.789  •••  SKEWNESS  KURTOSIS  32676410.  11681997.  -0.677  6.148  18432211.  4951469.  -0.230  2.260  18389084.  4885079.  0.2B2  2.604  • • • • THE PROJECT REVENUE DISCOUNTED AT A RATE OF RETURN OF 0.090 EXPECTED VALUE  STANDARD DEVIATION  69497704.  13595959.  SKEWNESS  22838442.  DEVIATION * • • . 17612327.  KURTOSIS * • *  -0.427  • • • • THE PROJECT NET PRESENT VALUE AT A DISCOUNT RATE OF 0.090 EXPECTED VALUE • S T A N D A R D  ••••  • • • SKEWNESS • • • -0.265  4.695  •••• • • • KURTOSIS • * * 3.567  Appendix D. The Computer Programs  277  Figure (D.5) contd.  O)  UJ 3 _l <  3  o  x  tt  in  o  CD i-l C— O Z o  > u ui  tu a a a:  O  Q  ~3  cr a. ui x  co in <o  <  z <  o a  Ul 3  cn  < >  cr o u.  tt  o  < > tt  O  o  o  cr u. o  < z cc  3 —i  CM  cn  Ul X  < > »«  o in ni  tt  o o  Appendix  E  The Correction Factor a T h e proof given in this appendix is an extension to that from Van Tetterode (1971). Let Q and P be two independent variables from (7(0,1). Then,  E[Q] = E[P] = \  ; crl = a% = 1  ; cov(Q,P)  = 0  Let R be a new random variable formed as,  R = P + a (Q - P)  (E.l)  T h e n from equation ( E . l ) ,  o\ = (1 - a )  4  2  +  a  2  £7  (E.2)  2  From the definition of covariance of R and Q,  cov(R,Q)  = cov(P + a(Q - P ) , Q) = cov(P,Q)  (E.3)  + cew(a((3-P) C?) J  (E.4)  Since cov(P,Q) = 0 = acov(Q,Q) cov(R,Q)  = a cr  - acov(P,Q)  (E.5) (E.6)  2  Let the correlation coefficient between R and Q be p. From the definition,  ,  =  C O v { R  278  >  Q )  (E.7)  Appendix E. The Correction Factor a  279  Substituting equation (E.2) and (E.6) in (E.7), a a? . (^(1 - a) a + a a  p =  2  Since a\  2  2  (E.8)  2  ) a  Q  = a% — — 12 9  Vl-2a  + 2a  2  Therefore, from equation (E.9), (2p - 1) a 2  -  2  2p a + p 2  2  = 0  (E.10)  The correction factor a as the solution of equation (E.10) is given by, p ± p.y/l 2  a = -  - p>  (E.ll)  2p - 1 2  Therefore, the random number correction is as follows (Van Tetterode, 1971). RNij  = RNj + cm {RN  -  RNj)  (E.12)  where RNi and RNj are the random numbers generated for variables A"; and Xj respectively, and RNij is the random number corrected for the linear correlation p^ between Xi and Xj relative to Xi. When = 0;  a^ = 0;  RNij  = RNj  Pij = +1;  ay = 1;  RNij  = RNi  Pij = —1;  a^  = 1;  RN^  = RNi  P i j  When p^ — 0 the corrected random number is given by the j  th  random number  demonstrating independence. When the correlation is either perfect positive or perfect negative the corrected random number is same as the i , ih  correlation. Therefore, for all values of p^, 0 < ctij < 1.  demonstrating perfect  Appendix  F  Input D a t a for N u m e r i c a l E x a m p l e s  This appendix contains the input data used for the numerical examples in chapter seven for the vahdation studies and apphcations of the analytical method for time and economic risk quantification.  Road Pavement Project Table F.l is Table 1 from Ang et al. (1975). It describes the various activities of the project involving the paving of 2.2 miles of roadway pavement and the construction of appurtenant drainage structures, excavation to grade, placement of macadam shoulders, erection of guardrails, and landscaping. The respective mean durations and corresponding standard deviations are also hsted.  Industrial Building Project Table F.2 is Table 3 from Ang et al. (1975). It describes the various activities of the project involving the construction of a single-story industrial building. The building is comprised of reinforced concrete piers, frost walls, structural steel columns, and a precast roof deck. The respective mean durations and corresponding standard deviations are also hsted.  280  endix F. Input Data for Numerical Examples  Table F.l: Activities and Estimated Durations (Pavement Project) E[D]  #  01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  Description of Activities Dummy Set-up batch plant Order and deliver paving mesh Dehver rebars for double barrel culvert Move in equipment Dehver rebars for small box culvert Build double barrel culvert Clear and grub from station 42 - station 100 Clear and grub from station 100 - station 158 Build box culvert at station 127 Build box culvert at station 138 Cure double barrel culvert Move dirt between station 42 - station 100 Start moving dirt between station 100 - station 158 Cure box culvert at station 127 Cure box culvert at station 138 Order and stockpile paving material Place subbase from station 42 - station 100 Finish moving dirt between station 100 - station 158 Pave from station 42 - station 100 Place subbase from station 100 - station 158 Cure pavement from station 42 - station 100 Pave from station 100 - station 158 Cure pavement from station 100 - station 158 Place shoulders from station 42 - station 100 Place shoulders from station 100 - station 158 Place guardrail and landscape Dummy  days 0 2 5 6 3 7 10 3 7 5 3 9 5 3 9 6 2 7 5 10 7 6 10 6 3 3 5 0  days 0 0.5 1.0 1.5 0.5 4.0 2.0 1.0 1.5 2.0 1.5 2.0 1.5 0.5 4.5 2.0 0.5 1.73 2.0 2.0 3.31 1.5 4.5 1.5 1.0 1.0 1.5 0  Appendix F. Input Data, for Numerical Examples  282  Table F.2: Activities and Estimated Durations (Industrial Building Project)  #  01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  E[D]  Description of Activities Mobilization Move in Initial layout Dummy Site rough grading Layout of piers Excavate piers Dummy Order and dehver rebars Form and rebars piers Pour piers Cure piers Strip piers Dummy Dummy Excavate frost walls Order and dehver structurl steel columns Erect structural steel columns Order and dehver precast roof deck Form and mesh frost walls Pour frost walls Cure frost walls Strip frost walls Backfill Grade and compact gravel for floor Rebarfloorand set screeds Pour andfinishfloor Dummy Excavate and grade parking Stone base for parking Dummy Set roof deck Hang siding and waterproof roof Hang doors Clean up Bituminous surface in parking  days 32 2 2 0 2 1 2 0 40 2 2 4 1 0 0 1 60 5 30 3 1 4 1 2 2 2 2 0 2 1 0 5 6 4 2 3  days 3.2 0.5 0.5 0 0.5 0.5 1.0 0 12.0 0.5 0.5 0.8 0.1 0 0 0.5 12.0 1.0 6.0 0.9 0.3 0.4 0.1 0.5 0.2 0.5 0.5 0 0.2 0.2 0 1.5 1.2 1.2 0.5 0.3  Appendix F. Input Data, for Numerical Examples  283  First Example The first example is an actual deterministic feasibility analysis conducted for a mineral project in South America.  Deterministic Estimates Table F.3 contains the description and deterministic estimates for duration cost of work packages. The work package durations were developed to correspond to the modified construction schedule. The work package costs were estimated such that the sum of the work package costs is equivalent to the constant dollar cost estimate of the deterministic feasibility analysis.  The Statistics The deterministic values are assumed as the median values of probability distributions for work package durations and costs. Table F.4 contains the expected value, standard deviation, skewness and kurtosis for work package durations and costs used in this example.  Revised Durations Table F.5 contains the statistics for the revised work package durations. The coefficients of variation for work package durations are approximately 4 0 % instead of the 3% to 13% used in the previous case.  Appendix F. Input Data for Numerical Examples  284  Table F.3: Deterministic Values for Work Package Durations and Costs WP#  Work Package Description  01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  Start Work Package Engineering &; Mobilization Construction of a temporary fuel tank Road & Rail for equipment transfer Camp expansion Roads for construction requirements Water supply scheme Mine auxiliary building Town-site - Phase 1 Power house construction Rainy season : Downtime Office, changehouse & lab for plant Road/rail/port transfer facilities Construction of process plant Taihngs Dam Town-site - Phase 2 Power plant - supply & distribution Roads for operational requirements - Phase 1 Construction of permanent fuel system Taihngs Pipeline - Phase 1 Plant shop & warehouse Pre-production Rainy season - Downtime Taihngs thickner - Phase 1 Town-site - Phase 3 Taihngs pipehne - Phase 2 Taihngs thickner - Phase 2 Equipment & installation of process plant Roads for operational requirements - Phase 2 Reclaim water system Start up Project mgmt., org. expenses, import tax Finish Work Package (Revenue Period) Total Base Estimate  Dura mths  -4  Cost $  -  2800000 200000 2520900 2620000 2400000 2501100 4233800 3552200 865800  3 3 3 8 11 11 8 5 3 2497200 10 4198000 10 8 4996300 3980000 8 8 4000000 6958300 13 3500000 9 9 743900 4 550000 13 1513600 21 33047700 3 440000 5 2000000 6 682500 5 4 346000 6 11853700 6 1475000 1356100 9 600000 3 33 18018000 180 36 124450100  Appendix F. Input Data for Numerical Examples  Table F.4: Statistics for Work Package Durations and Costs WP#  Duration (months) E[WPD)  01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  3.98 3.02 2.98 2.98 7.99 11.04 11.02 7.99 5.01 3.02 10.03 10.05 7.93 7.94 7.93 13.01 9.02 9.02 3.98 13.01 21.02 3.02 4.99 6.02 5.01 3.96 6.02 6.02 9.02 3.02 33.45 180.00  O'WPD  0.51 0.27 0.33 0.33 0.92 0.84 0.45 0.92 0.44 0.27 0.58 0.55 0.27 0.33 0.27 1.06 0.33 0.33 0.51 0.55 1.06 0.27 0.40 0.40 0.30 0.51 0.55 0.48 0.33 0.33 0.91 -  ft  0.4 0.7 -0.3 -0.3 0.2 0.1 0.5 0.2 0.1 0.7 0.2 0.3 0.4 0.3 0.4 0.0 0.3 0.3 0.4 0.0 0.1 0.7 0.0 0.2 0.4 0.0 0.1 0.2 0.3 0.3 0.7 -  Constant Dollar Cost ($ E[Co)  9.0 2836999 913186 9.0 203700 67051 3.5 2550166 912688 3.5 2649599 912707 2.1 2418499 881804 2.1 2537692 913156 5.5 4258293 1337784 2.1 3579135 1140382 2.6 860250 273656 9.0 2.3 2535235 913261 2.4 4198739 1276596 3.7 5034668 1581374 3.5 4042899 1370345 3.7 4073999 1341012 2.0 7029228 2220857 3.5 3536999 1095334 3.5 746157 237101 9.0 191632 555550 2.3 1517817 471158 2.1 33400048 10952316 9.0 2.8 445550 149120 2.8 2018499 638774 5.6 688975 219015 2.4 349330 118624 2.2 12074330 3992590 2.6 1502749 518038 3.5 1390842 458266 3.5 607400 194779 6.4 18751328 6156604 -  0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.0 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.2 0.2 0.3 0.1 0.5 -  02  2.1 2.2 2.1 2.1 2.1 2.1 2.0 2.0 2.0 2.1 2.0 2.0 2.0 2.0 1.9 2.0 1.9 2.0 2,1 2.0 2.0  2.2 1.9 2.0 2.0 2.1 2.3 1.9 2.6 -  Appendix F. Input Data for Numerical Examples  286  Table F.5: Statistics for Revised Work Package Durations Duration (months) WP# 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  E[WPD)  3.99 3.04 3.04 3.04 7.99 11.15 11.15 7.99 5.01 3.04 10.09 10.09 7.99 7.99 7.99 13.06 9.12 9.12 3.99 13.06 21.08 3.04 5.01 6.05 5.01 3.99 6.05 6.05 9.12 3.04 33.91  0~WPD  1.59 1.21 1.21 1.21 3.19 4.41 4.41 3.19 2.00 1.21 4.01 4.01 3.19 3.19 3.19 5.22 3.59 3.59 1.59 5.22 8.41 1.21 2.00 2.40 2.00 1.59 2.40 2.40 3.59 1.21 5.65  0.3 0.4 0.4 0.4 0.1 0.2 0.2 0.1 0.1 0.4 0.2 0.2 0.1 0.1 0.1 0.2 0.3 0.3 0.3 0.2 0.1 0.4 0.1 0.1 0.1 0.3 0.1 0.1 0.3 0.4 0.8  6.0 5.4 5.4 5.4 2.9 2.9 2.9 2.9 3.1 5.4 3.8 3.8 2.9 2.9 2.9 5.9 3.9 3.9 6.0 5.9 3.1 5.4 3.1 2.9 3.1 6.0 2.9 2.9 3.9 5.4 8.0  Appendix F. Input Data, for Numerical Examples  287  Revenue Streams The expected value, standard deviation, skewness and kurtosis for annual revenue and operating cost for the revenue streams are given in Table F.6. Table F.6: Statistics for Annual Revenue and Operating Costs RS#  Annual Revenue ($)  Annual Operating Cost ($)  E[R]  01 02 03 04 05 06 07 08  57771120 66244656 68975456 77449584 61242768 60687760 61242768 32325072  7925719 5804341 8889606 6990992 7014973 8011651 4596042 3656270  -0.2 -0.2 -0.3 0.0 -0.2 -0.3 -0.4 -0.2  fa  E[OkM]  2.5 2.2 2.1 2.1 2.1 2.1 2.7 2.6  18714624 18951120 21454432 21453872 21638864 19176192 13533594 10425628  2917630 3503251 4331357 4331492 4058441 3222784 1982918 2472484  VWi 0.5 0.1 0.7 0.7 0.9 0.4 0.2 0.7  82  2.9 2.1 2.5 2.5 2.8 2.5 2.5 3.5  Second Example The second example is a hypothetical engineering project of thirteen work packages and three revenue streams.  Work Package Duration The statistics for primary variables for work package duration model are given in Tables F.7, F.8 and F.9. The positive definite correlation matrix for primary variables that was used for all the work package durations is given by R w P D •  RWPD  1.00  -0.30  0.40  -0.30  1.00  -0.35  0.40  -0.35  1.00  Appendix F. Input Data for Numerical Examples  Table F.7: Statistics for Quantity Descriptor Q i WP# 02 03 04 05 06 07 08 09 10 11 12 13 14  E[Qi)  38397.3 60555.0 76850.0 16185.0 8092.5 20370.0 32429.2 38397.3 16160.8 21998.0 76850.0 20413.0 76850.0  <T  Qi  12186.1 8829.3 24440.5 3527.4 1373.3 5802.8 7030.8 12186.1 2820.8 2621.4 24440.5 5782.4 24440.5  0.5 0.9 0.5 0.8 0.4 0.8 0.8 0.5 0.3 0.2 0.5 0.7 0.5  02 3.3 9.0 3.2 7.8 3.6 9.0 7.8 3.3 2.6 2.4 3.2 8.5 3.2  E[Pu) 9.0 10.2 9.0 10.1 10.2 10.2 8.4 9.0 10.2 10.1 9.0 9.9 10.2  <M  1.25 2.23 1.25 2.28 2.23 2.23 1.28 1.25 2.23 2.28 1.25 2.22 2.23  0.0 0.8 0.0 0.1 0.8 0.8 0.1 0.0 0.8 0.1 0.0 0.9 0.8  02 5.6 8.0 5.6 2.2 8.0 8.0 8.8 5.6 8.0 2.2 5.6 9.0 8.0  3  Common * **  *  ** *  Table F.8: Statistics for Labour Productivity Rate Pj WP# 02 03 04 05 06 07 08 09 10 11 12 13 14  (ft )  Common *  **  * ** ***  **  (ft / 3  Ji  Appendix F. Input Data for Numerical Examples  289  Table F.9: Statistics for Labour Usage Li (m.d/year) WP# 02 03 04 05 06 07 08 09 10 11 12 13 14  E[Li]  6833.2 15185.0 15185.0 6074.0 3074.0 15370.0 7777.5 9055.5 7685.0 6074.0 15092.5 3850.8 15092.5  692.7 1539.5 1539.5 615.8 761.0 3805.3 2339.8 832.9 1902.6 615.8 1388.1 393.4 1388.1  0.4 0.4 0.4 0.4 1.0 1.0 1.1 0.4 1.0 0.4 0.4 0.4 0.4  02  2.4 2.3 2.3 2.4 7.2 7.2 5.7 4.3 7.2 2.4 4.3 2.3 4.3  Common * * **  ** ***  Work Package Cost The statistics for primary variables for work package cost model are given in Tables F.8, F.9, F.10, F.ll and F.12. The statistics for primary variables in Table F.12 are common for all the work package costs. Therefore, when the primary variables are assumed to be correlated, from the definition all of the work package costs are correlated. The positive definite correlation matrix for primary variables that was used for all the work package costs is given by R-yvpe-  Appendix F. Input Data, for Numerical Examples  290  Table F.10: Statistics for Equipment Usage E{ (e.d/year) WP# 02 03 04 05 06 07 08 09 10 11 12 13 14  E[Ei] 512.0 600.0 851.0 300.0 256.1 303.7 425.5 305.5 230.5 303.7 1063.8 461.0 300.0  °~Ei  126.8 60.8 136.6 30.4 63.4 30.8 68.3 31.8 57.1 30.8 170.7 114.2 30.4  VK 0.8 0.0 0.7 0.0 0.8 0.4 0.7 0.5 1.0 0.4 0.7 1.0 0.0  A  5.0 3.2 9.0 3.2 5.0 2.4 9.0 2.3 7.2 2.4 9.0 7.2 3.2  Common  * **  *  Table F . l l : Statistics for Subcontractor Cost 5,- ($) WP# 02 03 04 05 06 07 08 09 10 11 12 13 14  E[Si] 20370.0 10185.0 38425.0 10185.0 21966.5 32370.0 40462.5 20555.0 8092.5 6464.3 40462.5 20370.0 10185.0  5802.8 2901.4 12220.2 2901.4 2593.1 7054.8 6866.5 4918.8 1373.3 1128.3 6866.5 5802.8 2901.4  VK 0.8 0.8 0.5 0.8 0.3 0.8 0.4 0.6 0.4 0.3 0.4 0.8 0.8  02 9.0 9.0 3.2 9.0 2.6 7.8 3.6 3.4 3.6 2.6 3.6 9.0 9.0  Common * ** **  ***  * **  Appendix F. Input Data for Numerical Examples  291  Table F.12: Statistics for Common Primary Variables Primary Variable C {$lrn.d) Li  C (9/e.d) I .(%jyear) Ei  c  Oi* (%) e , (%) M  6 (%) Os. (%) Ei  »u (%)  r(%)  E[X] 141.85 76.85 301.85 161850.0 6.07 5.04 5.04 6.07 6.07 7.54  <rx 22.76 0.7 19.03 1.0 27.76 0.4 35274.18 0.8 0.64 1.0 0.58 0.8 0.58 0.8 0.64 1.0 0.64 1.0 0.85 0.2  9.0 7.2 4.3 7.8 7.2 9.0 9.0 7.2 7.2 2.5  02  1.0  -.56  0  0  .15  .65  0  0  0  .25  0  0  0  0"  -.56  1.0  0  0  .34  -.7  0  0  0  -.4  -.2  0  0  0  0  0  1.0  0  .20  0  0  -.56  0  0  0  -.2  0  0  0  0  0  1.0  .30  .15  0  .15  0  0  0  0  .7  0  .15  .34  .20  .30  1.0  .20  .15  .20  0  0  6  0  0  -.4  .65  -.7  0  .15  .20  1.0  0  0  0  .20  0  0  0  0  0  0  0  0  .15  0  1.0  0  0  0  .50  0  0  0  0  0  -.56  .15  .20  0  0  1.0  0  0  0  .30  0  0  0  0  0  0  0  0  0  0  1.0  .60  .30  .25  .3  .30  .25  -.4  0  0  0  .20  0  0  .60  1.0  .20  0  0  0  0  -.2  0  0  0  0  .50  0  .30  .20  1.0  0  0  0  0  0  -.2  0  0  0  0  .30  .25  0  0  1.0  0  0  0  0  0  .7  0  0  0  0  .30  0  0  0  1.  0  0  0  0  0  -.4  0  0  0  .30  0  0  0  0  1.0.  Appendix F. Input Data for Numerical Examples  292  Revenue Streams The expected value, standard deviation, skewness and kurtosis for annual revenue and operating cost for the'revenue streams are given in Table F.13. Table F.13: Statistics for Annual Revenue and Operating Costs RS#  Annual Revenue ($) E[R]  01 02 03  O-R  5907500 1763709 3453750 694077 3027749 441466  VK  Annual Operating Cost ($) K  -0.8 7.8 -0.3 3.5 0.9 9.0  E\OkM)  590750 176371 323670 70548 509249 145070  VK  -0.8 7.8 0.8 7.8 0.8 9.0  

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