PREDICTION OF PILE DRIVABILITY FROM CPT AND WEAP ANALYSIS By Jiawei Wang B. Sc., Tong Ji University, China M. Eng., Tong Ji University, China A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE STUDIES CIVIL ENGINEERING We accept this thesis as conforming to the requi±ed standard THE UNIVERSITY OF BRITISH COLUMBIA September 1992 0 Jiawei Wang, 1992 In presenting this thesis in partial fulfilment of the requirements for an advanced degree 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. (Signature) Department of 4/v/ /‘Je-/ The University of British Columbia Vancouver, Canada Date DE-6 (2/88) / Abstract Pile drivability is a difficult problem because of the complex dynamic pile-soil behaviour. The current procedure in predicting blow count during pile driving uses the Smith’s one-dimensional wave equation model with input appropriate soil resistances during pile driving. There is no general consensus to date on one approach for estimating the driving resistances in all types of soils. The cone penetration test (C PT) is a useful tool for detailed profiling of soil conditions at a site and has been found by many researchers to provide a reliable estimate of long term pile capacity as determined from a static loading test. An attempt has been made in this thesis to use the CPT directly to estimate pile driving resistance for use in pile drivability analysis. Several approaches were undertaken to estimate the driving resistances from the CPT, and the predicted blow counts from the wave equation analysis were compared to the field measured blow counts. Pile and soil data from three sites: UBC Pile Research Site, Tilbury Island Site and Evanston Campus of Northwestern University (ENCU) Site were analyzed. The piles included steel pipe piles of both closed and open ended as well as H pile. An empirical correlation approach is proposed which uses CPT cone bearing directly to estimate the driving toe resistances. (qc) The shaft resistances during driving, however, was estimated in a conventional way from static long term resistance calculated from correlations with CPT q,, data but was then multiplied by a set of empirical de termined reduction factors. The application of the proposed method to a steel pipe pile at another site location (not included in the above data base) is illustrated. Reasonable 11 agreement is obtained between calculated and measured blow counts. Although the data base in this study is limited, the proposed method appears promis ing. More research is needed to check the applicability of this method to different soil condition and other pile types. 111 Table of Contents Abstract ii List of Tables viii List of Figures x Acknowledgement xiv 1 Introduction 1 2 In Situ Test and Research Sites 5 2.1 Outline 5 2.2 The Cone Penetration Test 5 2.3 Research Sites 6 3 4 2.3.1 UBC Pile Research Site 6 2.3.2 Tilbury Island Site 9 2.3.3 ECNU ( Evanston Campus of Northwestern University ) Site 9 Pile Installation 14 3.1 Outline 14 3.2 UBC Piles 14 3.3 Tilbury Piles 16 3.4 ECNU Piles (Finno et al.,1989) 17 Application of CPT in Pile Capacity Prediction iv 20 5 4.1 Introduction 4.2 Methods of Predicting Pile Capacity from CPT 20 4.3 LCPC CPT method (Bustamante and Gianeselli, 1982) 24 4.4 Pile Capacity Predicted from LCPC CPT Method 27 • 20 Wave Equation Analysis of Piles 33 5.1 Introduction 33 5.2 GRLWEAP Program 33 5.2.1 5.3 Background of GRLWEAP Program Preliminary Analysis of the GRLWEAP Program 33 42 5.3.1 Outline 42 5.3.2 Basic Pile and Soil Conditions 43 5.3.3 Analyses and Results 43 5.3.4 6 . Conclusion Drivability Analysis - 48 Blow Count Prediction 50 6.1 Introduction 50 6.2 Analysis Parameters Selection 54 6.3 Selection of Prediction Methods Based on Pile Capacity 56 6.4 Blow Count Prediction Based on Pile Shaft Resistance 61 6.4.1 Introduction 61 6.4.2 Determination of Reduction Factor 63 6.4.3 Results 66 6.4.4 Influence Factors 75 6.4.5 6.5 Dynamic Effects during Driving Conclusion 81 87 V 7 Statistical Analysis and Criteria for Blow Count Prediction 7.1 Introduction 89 7.2 Linear Regression Analysis 90 7.3 Confidence Interval Analysis 91 7.4 Criteria Study of Predicted Blow Counts Based on Statistical Analysis 94 7.5 8 7.4.1 Outline 94 7.4.2 Steel Pipe Pile 94 7.4.3 II Piles 99 Conclusion 101 Application 103 8.1 Outline 103 8.2 Predicting Steps 103 8.3 Results 104 8.4 Comparison between Predicted and Measured Blow Counts 104 8.5 9 89 Conclusion 108 Summary and Conclusion 109 Bibliography 111 Appendices 115 A Pile Driving Records 115 B Definition of Confident Interval 124 C Predicted Blow Count with Different Confidence 127 vi D Input and Output Data Sample of Application vii 132 List of Tables 3.1 Summary of UBC Piles (after Davies, 1987) 15 3.2 Summary of Driving and Testing Details of UBC Piles (after Davies, 1987) 15 3.3 Summary of Tilbury Piles 17 3.4 Summary of Driving Details of Tilbury Piles 17 3.5 Summary of ECNU Piles (after Finno, 1989 18 3.6 Summary of Measured Capacities of ECNU Piles (after Finno et al. 1989) 18 4.1 Pile Capacity Prediction Methods Evaluated (after Robertson et al., 1987) 23 4.2 Bearing Capacity Factor k (from Bustamante and Gianeselli, 1982) 28 4.3 Friction Coefficient, a (from Bustamante and Gianeselli, 1982) 29 5.1 Parameters of UBC Pile 3 and Driving Data 43 6.1 Proposed Quake and Damping Values Used in Study 56 6.2 Predicted Blow Counts Based on Total Pile Capacity Method 59 6.3 Record of Soil Plug and Calculated 8 R / R ‘72 6.4 Blow Counts Comparison For UBC pile 4 and 5 80 7.1 Regression Analysis Results 91 8.1 Predicted and Measured Blow Counts at the Same Hammer Blow Rate C.2 Predicted Blow Counts Based on Confident Interval for UBC Pile 5 . . . . . . 107 128 C.2 Predicted Blow Counts Based on Confident Interval for UBC Pile 5 Con tinuous 129 C.3 Predicted Blow Counts Based on Confident Interval for UBC Pile 6 . . 130 C.3 Predicted Blow Counts Based on Confident Interval for UBC Pile 6 Con tinuous 131 C.4 Predicted Blow Counts Based on Confident Interval for Tilbury H Pile ix . 131 List of Figures 1.1 The Basic Procedure of Blow Count Prediction of Driven Pile 2 2.1 Cone Penetrometer Used at UBC ‘7 2.2 Simplified Soil Classification Chart for the CPT (after Robertson and Campanella, 1986) 8 2.3 Location of UBC Pile Research Site and Tilbury Island Site 10 2.4 CPT results of UBC Pile Research Site 11 2.5 CPT Results of Tilbury Island Site 12 2.6 CPT Results of ECNU Site 13 3.1 Load-Displacement Results of Load Testing for UBC Piles (after Robert son et al. 1987) 4.1 16 de Beer Scale Effect Diagram for CPT Pile Predictions (Adapted from Nottingham, 1975 21 4.2 Pile Capacity Distribution (after Bustamante and Gianeselli, 1982) 4.3 LCPC CPT Method to Determine Equivalent Cone Resistance at Pile Tip, 25 . (after Bustamante and Gianeselli, 1982) 4.4 25 Pile Capacity Predicted from LCPC CPT Method on TJBC Pile 1, 2, 3, 4 and5 31 4.5 Pile Capacity Predicted from LCPC CPT Method on ECNU Pipe Pile 31 4.6 Pile Capacity Predicted from LCPC CPT Method on ECNU 14x73 H Piles 32 5.1 Schematic Representation of Driving System for Wave Equation Model x . 34 5.2 Hammer Driving System model for ECH hammer (adapted from GRL WEAP Menu) 5.3 37 Pile and Soil Model used in GRLWEAP Program (adapted from GRL WEAP Menu) 37 5.4 Definition of Soil Quake 40 5.5 Definition of Soil Viscous Damping 40 5.6 Block Diagram of Predictor Corrector Analysis for GRLWEAP Program (adapted from GRLWEAP Menu) 41 5.7 Effect of Friction Percentage 44 5.8 Effect of Friction Percentage with Different Values of Quake 44 5.9 Effect of Hammer Efficiency 46 5.10 Effect of Hammer Efficiency with 46 5.11 Effect of Smith’s Damping 47 5.12 Effect of Skin Quake 47 5.13 Effect of Toe Quake 49 5.14 Bearing Graph of Pile 3 at Depth of 55 feet 49 6.1 Increase of Load Capacity with Time (after Soderberg, 1962) 6.2 Strength Loss by Remoulding for Normally Consolidated clays. 52 (after Fenske and Hirsch, 1986) 52 6.3 Proposed Model for 55 6.4 Shaft Resistance Distribution of IJBC Pile 5 at a Depth of 102 Feet 6.5 Predicted Blow Counts for TJBC Pile 5 Based on Total Pile Capacity Method 60 6.6 qea . . 57 and . 81 Determined from Analysis of Blow Counts Prediction Based R on End Bearing Method 62 Xl 6.7 Shaft Resistance to Total Pile Capacity vs Depth with CPT q, UBC Pile Research Site Pile 1 to 5 6.8 64 Calculation Steps of Establishing Empirical Correlation between Rsr/Ruj and q 6 for Specified Depth 67 Rsr/Rut vs qea for UBC Pile 3 and 5 68 6.10 Rsr/Rut vs ea for UBC Pile 6 68 6.9 6.11 Rsr/Rut vs q for UBC piles 70 6.12 Rsr/Rut vs qea for Tilbury piles 70 6.13 Rgr/Rut vs ea for UBC and Tilbury piles 71 6.14 Rsr/Rut vs ea for Tilbury H12x53 pile 73 6.15 8 R / R vs qea for ECNU H piles 74 6.16 Rsr/Rut 74 VS ea for H piles 6.17 Influency of Soil Profile Change to the 3 R r /Rut 77 6.18 Depth Influence to Rsr/Rut 79 6.19 Displacements around Driving Pile in Sand (after Robinsky and Morrison, 1964) 82 6.20 Working Principle of a Liquid Injection Open End Diesel Hammer (adapted from GRLWEAP Menu) 84 6.21 Influence of Diesel Hammer Blow Rate to the Pile Blow Counts with Con stant 85 6.22 Bearing Graph of Tilbury Pile 2 at Depth of 56 feet 86 6.23 Influence of Diesel Hammer Rate with Constant Energy to the Pile Blow Counts 86 7.1 Probability of Student Distribution 91 7.2 Bands of Different Confident Interval for UBC Pile 5 92 xi 7.3 Bands of Different Confident Interval for UBC Pile 6 92 7.4 Bands of Different Confident Interval for Tilbury Pile 2 93 7.5 Bands of Different Confident Interval for Tilbury H12x53 Pile 93 7.6 Blow Counts Predicted from Regression Line, Lower and Upper bounds with Different Confident Interval for UBC Pile 5 7.7 95 Blow Counts Predicted from Regression Line, Lower and Upper bounds with Different Confident Interval for UBC Pile 6 7.8 96 Blow Counts Predicted from Regression Line, Lower and Upper bounds with Different Confident Interval for Tilbury H Pile 100 7.9 Proposed 3 R r /Rut 8.1 Procedure for Blow Count Prediction 105 8.2 Predicted and Measured Blow Counts for Tilbury Pile 3 106 VS. ea for Predicting Blow Counts of Drven Piles xiii . . 102 Acknowledgement The author wishes to express his gratitude to his research advisor , Professor R. G. Cam panella, for his support, encouragement, patient and valuable suggestions throughout this research and thesis preparation. The author is very thankful to Alex Sy for suggesting the research topic and providing data. His advice and suggestions during our discussions proved invaluable. The author also expresses his sincere gratitude to Professors Liam Finn and Y. P. Vaid for reading this thesis and suggesting improvements. The financial support in the form of a Research Assistantship from the Natural Sci ence and Engineering Research Council of Canada, which made this study possible, is gratefully acknowledged. The author is deeply indebted to Mr. Matt Kokan for reviewing and making im provements to the thesis. His suggestions and friendly helps was invaluable. Thank you Matt. Appreciation is extended to my colleagues, G. X. Wu and Y. C. Shi, for their valuable discussions. A big thank you also to my parents and my sister, S. N. Wang, for their continued encouragement, understanding and support throughout my studies in UBC. I wish to thank my dear wife Z. Q. Yang, whose love, friendship and understanding I treasure. This thesis is dedicated to them. xiv Chapter 1 Introduction In design of driven pile foundations for axial loadings, two types of analyses are usually conducted. Given the design load and soil conditions, the pile type, pile size and length are determined from a static analysis. Then a dynamic analysis is carried out to select an optimum pile driving hammer system and to determine whether the pile can be installed to the design depth. If the pile has to penetrate a dense or hard layer, it may be necessary to predict how long the driving will continue be through the layer and to evaluate whether the driving stresses will exceed the structural strength of the pile. Where the pile tip is driving into in a very dense stratum, a practical refusal criterion or final set (displacement per blow) is determined for the given hammer system to be used during construction. The dynamic pile analysis is conducted in practice using a one-dimensional wave equation program such as GRLWEAP (Goble, 1987). The problems outlined above require an estimation of net pile displacement or set, given a hammer-pile-soil model. Set is the inverse of blow count, i.e. number of blows for a given displacement increment, typically onefoot. The prediction of set or blow count is commonly referred to as a pile drivability problem. Blow counts are often predicted throughout the penetrated soil profile, i.e. as a function of depth. Given the hammer blow rate (blows per minute), the time required to drive a pile can be estimated from the predicted total number of blows. Pile drivability consideration is important in offshore and onland piling for construc tion planning, scheduling and cost estimating purposes. The prediction of blow count, 1 Chapter 1. Introduction 2 Long Term Pile Resistance Reduction Factors Hammer Model K Pile Model Soil Model / Resistance During Pile DrMng WEAP Program Predicted Blow Counts Figure 1.1: The Basic Procedure of Blow Count Prediction of Driven Pile however, is a difficult task because it requires a knowledge of the resistances acting on the pile during driving. The driving resistances depend on the complex dynamic pile-soil interaction characteristics. Studies (Soderberg, 1962 and Hereema, 1980) have show that the driving resistance, particularly along the pile shaft, is less than the resistances act ing on the pile from a static loading test conducted oa the pile after construction pore pressure have dissipated. In this study, the pile resistance from a static loading test are referred to as static long term resistance. The current practice in pile drivability prediction involves two basic steps. First, the long term pile shaft and toe resistances are evaluated from in-situ tests and these are multiplied by some reduction factors based on soil type and/or pile length to arrive at the estimated driving resistances. In the second step, the estimated driving resistance is used in a wave equation analysis of the hammer-pile-soil system to predict the blow counts as a function of pile penetration. This basic procedure is illustrated in Figure 1.1. Qhapter 1. Introduction 3 There is no general consensus on the reduction factors applied to the static long term pile resistance. Some results of studies (Aurora, 1980; Tang et al, 1988) have shown that the static resistance during driving changed with the undrained shear strength of the clay. In their approach (Tang et al, 1988) a factor of 0.5 was used to multiply undrained shear strength to estimate shaft resistance during driving in clay. The toe resistance in clay and both shaft and toe resistances in sand use the static resistances determined from pile load testing. Chow et al (1988) determined both shaft and toe resistances using remoulded undrained shear strength. They used a reduction factor of 0.33 to determine shaft resistance during driving, but a factor of 9 times the remoulded undrained shear strength was used to determine toe resistance during driving. Heerema (1980) presented a “friction fatigue” theory for describing pile driving behaviour in clay. In his study, the gradual decrease in skin friction during pile driving was considered to be caused not only by clay remoulding but also by the decrease in horizontal stress in the soil around the pile. In their research, using the WEAP86 program, Alm et al (1989) considered the blow count as a non-linear function of input energy and static resistance during driving at a given depth. They used statistical analyses to estimate static resistance during driving from input energy and pile capacity. However, comparison of similar hammers demonstrates that all hammers, unfortunately, do not provide a consistent amount of energy into the pile (Rausche et al, 1985). It is therefore difficult to predict drivability accurately if the effects of input energy and static pile resistance during driving are not considered separately. The cone penetration test (CPT) provides a repeatable and reliable means of charac terizing soil conditions (Campanella and Robertson, 1986). Because the CPT is a model of a displacement pile, it has been correlated to results from static pile loading tests (Bustamante and Gianesalli, 1982 and Burland, 1983, etc.). In fact, the LCPC method has been found to provide pile capacities in good agreement with static loading tests at Chapter 1. Introduction 4 several sites in North America (Davies, 1987, Campanella et al, 1989 and Finno, 1989). The purpose of this study is to explore the possibility of using the CPT in a more direct approach to pile drivability analysis. Data from three sites: UBC Pile Research Site, Tilbury Island Site and Evanston Campus of Northwestern University (ENCU) Site were used. The pile types include steel pipe piles, both closed and open ended, and H piles. A method is finally proposed which uses the CPT cone bearing (qc) values directly to estimate the static toe resistance during driving and uses a reduced shaft resistance calculated from the LCPC CPT pile capacity prediction method. It is shown that the detailed profiling of the CPT provides a promising method of evaluating the driving resistance for use in a wave equation analysis to predict blow counts. Chapter 2 In Situ Test and Research Sites 2.1 Outline The study in this thesis was based on the data obtained from three pile research sites. The research sites include: UBC Pile Research Site Tilbury Island Site ECNU ( Evanston Campus of Northwestern University, USA ) site For each site, the blow count data were analyzed in conjunction with cone penetration test (CPT) data to establish a method which can predict blow counts of different types of driven piles accurately. The CPT tests were done near or at the location of pile test sections. Data from different sites with different soil types were used in the analysis to consider the effects of soil type on the analysis. 2.2 The Cone Penetration Test The cone penetration test (CPT) is a quasi-.static penetration test. The CPT allows for near continuous delineation of stratigraphy. The small end area and low pushing rate, make the CPT a very good tool for modelling pile performance. The CPT has the following advantages: a) continuous logging b) rapid procedure 5 Chapter 2. In Situ Test and Research Sites 6 c) good repeatability d) easy standardization The CPT can be used to rapidly assess soil variability at a site. The major disad vantage of the CPT is that it can not be used in some soil conditions such as gravels or heavily cemented soils. Hence, the forthcoming analysis will be restricted to soils that are well suited to penetration testing The electric cone is illustrated in Figure 2.1. It has a tip having base area of 10 cm 2 and an apex angle of 600. The friction sleeve located immediately behind the cone tip has a standard area of 150 cm . The cone is pushed into the soil at a constant rate of 2 2 cm/sec and has the ability to sample on five different channels at 2.5 cm intervals, measuring the cone bearing (q), sleeve friction (f ) 5 , pore pressure (U), temperature (T) and inclination (I). Robertson and Campanella (1986) provide a comprehensive review of equipment, testing procedure and data interpretation. Soil classification from CPT data is based on cone bearing and friction ratio as shown in Figure 2.2 (Robertson and Campanella, 1986). 2.3 Research Sites 2.3.1 UBC Pile Research Site UBC Pile Research Site is located on the eastern end of Lulu Island, within the post glacial Fraser River delta, as shown in Figure 2.3. The surficial geology of this part of Lulu Island is typical of a former marine environment no longer dominated by tidal action (Blunden, 1975). The CPT results in Figure 2.4 show deposits of organic silty clays that has been laid down in a swamp or marsh environment extend to a depth of about 50 feet. Below this upper layer, a medium dense sand deposit, locally silty, prevails to roughly 90 feet in Chapter 2. In Situ Test and Research Sites 7 fItting to lock 14 conductor cabla iDlicid to cabi• kisida tuba wfr•s a•ismem•t•r sansor ,— strain gages for. friction load cell ,, Quad rInQ iQuil and ar.a friction $i..vi (150cm’ ar.&) QIQSS lot con• 0•il porous amall *5.88mm 0.0. Figure 2.1: Cone Penetrometer Used at UBC Chapter 2. In Situ Test and Research Sites 1000— Al 11 J\ 10/ IO 8 Il I 12 I J 3 w Z 0 C-) 2 I— —••—————————— O I 4 2 3 5 FRICTION RATIO ac/N Zona 1) 2) 3) 4) 5) 5) 7) 8) - - - 10) 11) 12) (a) 2 1 1 1.5 2 2.5 3 4 5 6 1 2 6 (%) 7 8 Soil B.hovlour Typo •onsltivo fina gralnod organic motorlal clay silty clay to clay clays>, silt to silty clay sandy silt to claysy silt silty sand to sandy silt sand to silty sand sand gravelly sand to sand vsry stiff fine grairied C.) sand to claysy sand Ca) overconsolidated or camantad Figure 2.2: Simplified Soil Classification Chart for the CPT (after Robertson and Cam panda, 1986) Chapter 2. In Situ Test and Research Sites 9 depth. A normally consolidated clayey silt containing sand layers underlies the above sequence to a depth of 700 2.3.2 - 800 feet depth (Davies, 1987). Tilbury Island Site Tilbury Island Site is located in the middle of the Fraser River delta, as shown in Fig ure 2.3. The CPT results are shown in Figure 2.5. There is a filled sand layer which is medium dense to dense up to 6.5 feet in depth. Below this layer is a silt deposit containing thin clay layers to 14 feet in depth. Next, a fine to medium grained sand layer with interbedded silt layers exists up to 25 feet depth. Below 25 feet are sands which appear to increase in density with depth. Above 41 feet depth, the sand is fine grained and loose to medium dense. Below 41 feet depth, this sand layer changes to fine to medium grained sand with some coarse grained sand, the density of this sand layer changes from medium dense to dense. 2.3.3 ECNU ( Evanston Campus of Northwestern University ) Site The data of this test site is from the ASCE Foundation Engineering Congress (Finno, 1989), to evaluate capacity and load transfer characteristics of piles. The test site is located on the lakefihl on the Evanston Campus of Northwest University, Evanston, IL, USA. The CPT results are shown in Figure 2.6. With increasing depth from the ground surface, the soils consist of 23 feet of fine grained sand, 45 feet of soft to medium clay, 12 feet of stiff clay and 10 feet of hard silt. Beneath the silt Niagaran dolomite bedrock is encountered. The water levels are static within the clay deposits (Finno, 1989). Chapter 2. In Situ Test and Research Sites 10 VANCOUVER ,—;:: ThburSil TA DE [wand Site. ‘: : : • a U., SCA.t: 0I a 3 4 Figure 2.3: Location of UBC Pile Research Site and Tilbury Island Site 0 a) 0 .4-, -C 4- a) a) .4-, IN TESTING Rf () norma fly consolidated cloyey SILT w/ thin SAND layers minor silty SAND lenses medium dense SAND soft or anic 9 silty CLAY INTERPRETED PROFILE Cqmments: NEAR CASING Page No: Figure 2.4: CPT results of UBC Pile Research Site Fs (bars) FRICTION RATIO Cone Used: UBC8 STD TP Qc (bars) 100 50 SITU Dote: 850813 MD AS DW SLEEVE FRICTION CPT PRI ON Site Loc: CPT UBC CONE BEARING ANNA PLT Site Location: I o o CD o 0 0 Depth (feet) 0) 0 0’ 0 - o C_.j 0 1’.) 0 — 0 0 z VI CD U) CD C-) 0 C > -o r9 0 C 0 C z m C) Cl) z C) 0 —I U CD 0 C CD CD C 11111111111 II.. C) liii I I ii iii ii I p -c U) CD I I,, ‘1 C), UC —‘ CI) 0 C -I CD P1 0 —I C— C-, —I C CD z 0 0 CD - C C) - P1 -0 -‘ z crj - . z C) C C-) 0 z -I P1 -U P1 -l P1 ID 3 3 CD C CD CD z 0 Li) z I,, > C) > cr 0 ‘1 I z C, P1 pU-s s uj g .radvq • 0 V 4-, £ N 80 70 - 60 50: 0 SLEEVE FRICTION • Rf 5 10 (%) Silty CLAY SAND Fine Snad SAND INTERPRETED PROFILE Comments: NEAR CASING 1 TESTING Page No: SITU FRICTION RATIO Figure 2.6: CPT Results of ECNU Site Qc (bars) 100 200300 400 0— iiIiiiljiiiIij CONE BEARING LAKE FILL Cone Used: ISMES—MK7—20 TESTING Date: 870325 09:44:08 LOAD ON Site Loc: PILE CPT ASCE Site Location: NWU CAMPUS ti) Chapter 3 Pile Installation 3.1 Outline The test piles installed at the three research sites included H pile and steel pipe piles with closed and opened ends. The hammer types used in installation included drop hammer, diesel hammer and steam hammer. During pile installation, blows per foot were recorded. Load tests were carried out on UBC piles and ECNIJ piles. The results of the load testing are presented in tables in order to make comparison between predicted and mea sured pile capacity. The details of both the piles used and the load testing programs are from Davies (1987) for UBC piles and Finno (1989) for ECNU piles. 3.2 UBC Piles Six piles were driven at UBC Pile Research Site during Aug. 15 to 19 1985. All of these piles were steel pipe piles. The details of these piles are given in Table 3.1. A summary of the driving and load testing is shown in Table 3.2. The complete driving record can be found in Appendix A (Davies, 1987). All piles were driven with a steel drop hammer using a metal helmet and plywood cushion. Pile 1 was driven with a 4400 lb hammer and the others were driven with a 6200 lb hammer. Pile 1,2,3,5, and 6 were driven closed-ended with the base-plate flush with the diameter of the piles. Pile 4 was driven open-ended. Soil plug monitoring was 14 Chapter 3. Pile Installation 15 Table 3.1: Summary of TJBC Piles (after Davies, 1987) Pile No. 1 2 3 4 5 6 Outside Diameter (in) 12.75 12.75 12.75 12.75 12.75 24.00 Wall Thickness (in) 0.375 0.375 0.375 0.375 0.500 0.500 Cross Section area ) 2 (in 18.56 18.56 18.56 18.56 19.24 36.91 Pile Length (feet) 50.0 50.0 60.0 90.2 106.0 118.3 Open/Closed Ended C C C 0 C C Table 3.2: Summary of Driving and Testing Details of UBC Piles (after Davies, 1987) Pile No. 1 2 3 4 5 6 Embedded Length (in) 47.0 45.0 55.0 76.0 102.0 103.0 Hammer Weight (ib) 4400 6200 6200 6200 6200 6200 Drop Height (feet) 4 3 4 5 6 to 7 10 max - - - - - - Driving Date(s) 19 16 16 16 15 14 15 Aug. Aug. Aug. Aug. Aug. Aug. Aug. 85 85 85 85 85 85 85 Testing Date(s) Capacity 9 Nov. 85 1 Mar. 86 9 Nov. 85 1 Mar. 86 22 Sep. 85 NOT TESTED (kips) 38.0 50.0 137.0 270.0 241.0 performed on pile 4 during driving. After final driving, the top of the soil plug was 26.47 feet below ground surface, thus the total length of soil plug was 49.53 feet. The load testing summary of the UBC piles is given in Table 3.2. The Quick Load Test Method of axial loading ( similar to ASTM 43-81 Section 5.6) was used. Davisson’s method (1973) of interpreting axial pile load test data was used in determining pile capacities of each pile. Experience has shown that piles driven in the Fraser delta reach their ultimate capacity after 4 to 5 weeks. Thus all capacities given in Table 3.2 are judged to be at their ultimate. Figure 3.1 presents a summary of the load-displacement test results. Based on this data, pile 1, 2, and 5 are interpreted as predominantly shaft Chapter 3. Pile Installation 16 AXIAL LOAD(kN) 00 400 600 800 5 z I0 15 20 25 30 -J 35 40 “4 Figure 3.1: Load-Displacement Results of Load Testing for UBC Piles (after Robertson et al. 1987) resistance piles, whereas pile 3 and 4 had significantly larger contribution to their total capacity from end bearing (Robertson et al, 1987). 3.3 Tilbury Piles Four piles were driven in Tilbury Island Site during March 4th to 10th 1991. Among these piles, test pile 1, 2 and 3 were steel pipe pile, while pile HP was a steel H pile. For each pipe pile, the end was closed with a flush base plate. The base plate diameter was slightly larger than the diameter of the pile. The details of these piles are given in Table 3.3. A summary of driving data is given in Table 3.4. A complete driving record can be found in Appendix A. For the first 15 and 24 feet, respectively, piles 1 and 2 were driven by a drop hammer with a ram weight of 12000 lb. For the first 20 feet, pile 3 was driven by a drop hammer Chapter 3. Pile Installation 17 Table 3.3: Summary of Tilbury Piles Pile No. 1 2 3 HP Outside Diameter (in) 20.00 20.00 12.75 12x53 Wall Thickness (in) 0.500 0.375 0.375 End-Plate Diameter (in) 20.75 20.75 12.00 End-Plate Thickness (in) 1.375 1.375 1.500 Cross Section Area ) 2 (in 30.63 23.12 18.56 15.50 Pile Length (feet) 82.0 62.0 82.0 100.0 Embedded Length (feet) 80.0 60.0 80.0 97.0 Table 3.4: Summary of Driving Details of Tilbury Piles Pile Embedded Drop Hammer Driving Depth Weight Drop Height Depth Date(s) No. (feet) (lb) (feet) (feet) 1 80.0 12000 4 15.0 9/10 Mar. 91 2 60.0 12000 4 24.0 9/10 Mar. 91 3 80.0 8000 4 24.0 9/10 Mar. 91 HP 97.0 8000 4 10 Mar. 91 97.0 D30-13 hammer was used after driving depth of drop hammer with a ram weight of 8000 lb. After this, they were driven by a D30-13 diesel hammer, with aluminum plus conbest cushion. The area of the hammer cushion was 238.5 in 2 and 415.5 in 2 for up to 16 in. and to 24 in. diameter piies respectively. The HP pile was driven by a drop hammer with a ram weight of 8000 lb. The blow rates of the diesel hammer were recorded and presented with pile driving records in Appendix I. 3.4 ECNU Piles (Finno et al.,1989) The piles included one pipe pile, one 14 x 73 H pile and nine lOx 42 H piles. The pipe pile was closed-ended, having a slightly oversized base plate. All test piles were embedded 50 feet. The details are given in Table 3.5 (Finno, 1989). Chapter 3. Pile Installation 18 Table 3.5: Summary of ECNU Piles (after Finno, 1989 Pile Types Pipe HP1 HP2 Outside Diameter (in) 18.00 14x73 10x42 Wall Thickness (in) 0.375 End-Plate Diameter (in) 19.00 End-Plate Thickness (in) 0.750 Cross Section Area ) 2 (in 20.76 21.40 12.40 Pile Length (feet) 53.0 52.0 60.0 Embedded Length (feet) 50.0 50.0 50.0 Table 3.6: Summary of IVleasured Capacities of ECNU Piles (after Finno et al. 1989) Pile frpe HP jpe Measured 2 weeks 5 180 140 Capacity (kips) weeks 43 weeks 194 220 160 233 All piles were driven with a Vulcan 06 hammer. The driving system consisted of the Vulcan 06 hammer, a 5 in. cushion of alternating plates of 1/2 in. thick aluminum (4 plates) and 1 in. thick micarta (3 plates) and a 1000 lb steel helmet. The cushion plates were 11-1/4 in. outside diameter with a 3 in. diameter hole in their centres. A 2-1/2 in. thick striker plate topped off the aluminum-micarta cushion. The driving records of the piles are given in Appendix A. Note that a 12 in. diameter hole was preaugered to a depth of 23 feet at the location of the closed-ended pipe pile to assist in penetration. The load testing was performed according to Standard Loading Procedures method described in ASTM D-1143-81. Loads were applied until pile failure. Table 3.6 presents the summary of measured capacity for each test with different elapsed time after pile driving (Finno et al., 1989). The ultimate (long term) capacity was not reached in 5 weeks, which was typical of the Fraser delta piles in sand, but took much longer as shown by the increase in capacity from 5 to 43 weeks. Thus longer time to ultimate Chapter 3. Pile Installation 19 strength is likely due to the firm local sands which took longer time for pore pressure dissipation. Chapter 4 Application of CPT in Pile Capacity Prediction 4.1 Introduction The CPT is gaining acceptability as a tool for geotechnical investigation and design. It is particularly relevant for predicting the capacity of pile foundation. A summary of different methods of pile capacity prediction is represented by Davies (1987). In this study the pile capacity estimated from the CPT is adopted as a basic parameter to determine the ultimate resistance of a pile. Current methods of predicting pile capacity from CPT are based on measured CPT parameters as well as empirical factors. These method include: direct methods that use the CPT data without evaluating any intermediate values; and indirect methods that required intermediate correlations, such as coefficients of earth pressure, friction angle, etc.. 4.2 Methods of Predicting Pile Capacity from CPT In his earlier research, de Beer demonstrated that a scaling factor must be used to obtain the pile capacity using q,, from CPT. As shown in Figure 4.1, when a probe of zero diameter penetrate into soil layer, the penetration resistance will follow the idealized curve .4BGD. That means the device would feel the entire effect of a lower soil layer immediately upon penetration. If a large diameter pile is pushed into the layer, the point resistance would not equal that of zero diameter probe until the pile reached a greater depth, at point E. This depth is often termed the critical depth (Dc). De Beer showed 20 Chapter 4. Application of OPT in Pile Capacity Prediction 21 PENETRATION RESISTANCE A U IDEAL CURVE (ZERO DIAMETER) C ACTUAL CURVE x I 0 U C P R CURVE D Figure 4.1: de Beer Scale Effect Diagram for CPT Pile Predictions (Adapted from Not tingham, 1975 Chapter 4. Application of CPT in Pile Capacity Prediction 22 that it is reasonable to assume that the pile resistance curve between point B and E varies linearly; thus, the pile resistance at any intermediate depth could be determined if the idealized penetration resistance curve and D were known. Although there is no probe of zero diameter, the diameter of the cone is sufficiently small that it can be assumed to approximate this condition, following curve ABC’D. Meyerhof, de Beer, and others have shown that D is a function of foundation size and soil stiffness. Therefore, it is more logical to express critical depth as a ratio (D/B) in which B is the foundation diameter. When the thickness of high stiffness soil layers is less than D for a large diameter pile, the full penetration resistance may be mobilized on the cone but may not be realized for the pile before the influence of another layer is felt. There are twelve prediction methods based on CPT data. A summary of these meth ods was made by Robertson (1986) and is shown in Table 4.1. Based on the study of Davies (1987), the direct and indirect methods both provide reasonable predictions of the measured pile capacity for smaller piles. For large piles however it was shown that while direct methods predict the pile capacities quite satisfactorily, indirect methods predicted pile capacities that were significantly in error and non-conservative when compared to the measured results for the large pile. Since indirect methods rely on correlations between the CPT data and intermediate parameters, they can give erroneous results in compli cated soil conditions. Based on his research, Davies suggested that the method of LCPC CPT provided the best prediction for pile capacity. The LCPC CPT method does not require the CPT sleeve friction value other than to define soil type. This is a desirable feature since cone bearing is generally obtained with more accuracy and confidence than the sleeve friction, (Bustamante et al, 1982). In the following analysis, the LCPC CPT method was selected to predict pile capacity. Chapter 4. Application of OPT in Pile Capacity Prediction 23 Table 4.1: Pile Capacity Prediction Methods Evaluated (after Robertson et al., 1987) Direct Methods 1. Schmertmann and Nottingham CPT 2. de Ruiter and Beringe CPT 3. Zhou et al CPT 4. Van Mierlo and Koppejan CPT 5. Laboratorie Central des Ponts et Chaussees CPT (LCPC) Indirect Methods 6. API RP2A 7. Dennis and Olson 8. Vijayvergiya and Focht 9. Burland 10. Janbu 11. Myerhof Conventional 12. Flaate and Selnes References Schmertmann (1978) Notes Proven CPT Method de Ruiter and Beringen (1979) Zhou et al (1982) European (Fugro) Van Mierlo and Koppejan (1952) LCPC Bustamante and Gianesalli (1982) - American Pet. Inst. (1980) Dennis and Olson (1983 a and b) Vijayvergia and Focht (1972) Burland (1983) Janbu (1976) Myerhof (1976 ) Flaate and Selnes (1977) Chinese Railway Experience Original Dutch French Method Offshore Modified API “\“ Method ‘NB” Method NIT Original Bearing Theory NGI Chapter 4. Application of CPT in Pile Capacity Prediction 4.3 24 LCPC CPT method (Bustamante and Gianeselli, 1982) This method is based upon the interpretation of a series of 198 full-scale static loading (or extraction) tests. The test data analyzed included 96 deep foundations on 48 sites, containing soils made up of such materials as clay, silt, sand, gravel or weathered rock, mud, peat, weathered chalk, and marl (Bustamante and Gianeselli, 1982). There were 31 driven piles with diameter ranging from 30 to 64 cm and lengths from 6 to 45 m. Driven piles included H piles, closed ended pipe piles and concrete piles. All the piles tested were loaded axially. Efforts were made to defined the real geometry of the shaft and the properties of soil around the shaft of pile. The LCPC CPT method is based upon the work of Begemann (1963) and Van der Ween (1957) for point resistance calculation and Dinesh Mohan (1963) for skin friction calculation. The calculated limit load QL of a deep foundation is the sum of two terms, as shown in Equation 4.1. (4.1) QL=Q+Q In equation 4.1 Q is the limit resistance under the pile tip. Q is the limit skin friction on the shaft of embedded length of the pile. They are calculated as follows: { (4.2) m Q 1 Q: f sj Ij where: qca is the equivalent cone resistance at the level of the pile tip (in kN/m ) 2 k is the penetrometer bearing capacity factor f. is the limit unit skin friction over the thickness of the layer i A is the area of pile tip is the area of pile shaft over the thickness of the layer i Chapter 4. Application of CPT in Pile Capacity Prediction 25 Figure 4.2: Pile Capacity Distribution (after Bustamante and Gianeselli, 1982) a1D 2 e 0.7 ca Figure 4.3: LCPC CPT Method to Determine Equivalent Cone Resistance at Pile Tip, (after Bustamante and Gianeselli, 1982) Chapter 4. Application of CPT in Pile Capacity Prediction 26 i is the number of soil layers The unit end bearing is calculated using an equivalent cone resistance at the pile end, as shown in Figure 4.3. In practice, the equivalent cone resistance q is calculated in several steps. Firstly, the curve of the cone resistance q, is smoothened so as to remove the local irregularities of the data. To be conservative, the smoothened curve is made to pass closer to the valleys than to the peaks. Then using the smoothened curve, q’ is calculated which is the mean of the smoothened resistance between the values —a to +a where a is 1.5 times the diameter of pile. Finally, the equivalent cone resistance ca is calculated after clipping the smoothened curve. This peak clipping is carried out so as to eliminate only the values higher than 1.3 q’ under the pile tip, whereas the values higher than 1.3 qca’ and lower than 0.7 q,’ are eliminated above the pile tip (Bustamante and Gianeselli, 1982). The value of k depends on the nature of the soil, the value of q and also, on the pile placement techniques. For driven piles, the value of k, is without reserve to closed ended pipe piles. For open ended pipe piles and H piles, the value of k must be reduced unless it can be demonstrated, either with reference to similar cases or preferably as a result of full-scale loading test, that a soil plug occurs under the pile point, capable of taking up the equivalent forces of a point whose section would be determined by the circumscribed perimeter. For each layer i, the limit unit skin friction f, is calculated by dividing the cone resistance q corresponding to the given level by a coefficient a as shown in Equation 4.3 which accounts for the nature of the soil, the pile type and the placement method. In selecting the values of a, it is not necessary to account for the diameter of the pile or more precisely for the radius of curvature of the foundation. Chapter 4. Application of OPT in Pile Capacity Prediction f_E The values of bearing capacity factors k and 27 (4.3) f, are given in Table 4.2 and Table 4.3 (Bustamante and gianeselli, 1982). 4.4 Pile Capacity Predicted from LCPC CPT Method The comparison between predicted pile capacities using the LCPC CPT method and the results of load testing are shown in Figure 4.4 on UBC Pile 1, 2, 3, 4, and 5, Figure 4.5 on ECNU pipe pile, and Figure 4.6 on ECNU pile 1114 x73, respectively. In these figures, the curves of friction and total pile capacities are shown. The difference between the curves for total and frictional capacity is the end bearing capacity. Pile capacity is a function of elapsed time since driving (Soderberg, 1962). Normally, the load testing is performed after sufficient elapsed time. According to the study of Davies (1987) the load testings of UBC piles were performed based on the elapsed time which pore pressure fully dissipated. The elapsed times after driving were 38 days for pile 5, 84 days for pile 1 and 3 and 210 days for pile 2 and 4, respectivelly. The pile capacity determined from load testing was considered to be a long term pile capacity. In general. predicted pile capacities show very good agreement with measured pile capacities. From the results of the ECNU load testing given in Table 3.6, the predicted pile capacity agree best with the pile capacity measured after a long elapsed time. This demonstrates that the pile capacity determined from LCPC CPT method represents a long term pile capacity. Because of the small end area of the H piles, the determined pile capacities of H piles show a very low end bearing capacity. The frictional component comprises the bulk of total pile capacity. Thus H piles are characteristically friction piles. Chapter 4. Application of OPT in Pile Capacity Prediction Table 4.2: Bearing Capacity Factor 28 k (from Bustamante and Gianeselli, 1982) Nature of Soil q (MPa) Soft clay and mud Moderately compact clay Silt and loose sand Compact to stiff clay and compact silt Soft chalk Moderately compact sand and gravel Weathered to fragmented chalk Compact to very compact sand and gravel Group I: Plain bored piles Mud bored piles Micro piles (grouted under low pressure) Cased bored piles Piers Barrettes < 1 1 to 5 < 5 > 5 5 5 to 12 < 5 < 12 Factors k Group I Group II 0.40 0.50 0.35 0.45 0.40 0.50 0.45 0.55 0.20 0.30 0.40 0.50 0.20 0.40 0.30 0.40 Group II: Cast screwed piles driven precast piles Prestressed tubular piles Driven cast piles Jacked mental piles Micropiles (small diameter piles grouted under high pressure with diameter < 250 mm) Driven grouted piles (low pressure grounting) Driving mental piles Driving rammed piles Jacked concrete piles High pressure grouted piles of large diameter Chapter 4. Application of CPT in Pile Capacity Prediction 29 Table 4.3: Friction Coefficient, a (from Bustamante and Gianeselli, 1982) Coefficients, a Nature of Soil (MPa) < 1 Soft clay and mud I to 5 Moderately compact clay Silt and 5 loose sand Compact to > 5 stiff clay and compact silt Soft chalk 5 Moderately 5 to 12 compact sand and gravel Weathered to > 5 fragmented chalk > 12 Compact to very compact sand and gravel IA Plain bored piles mud bored piles Hollow auger bored Micropiles (grouted Cast screwed piles Piers Barettes I A 30 B 30 II AW 30 30 40 80 40 80 0.035 (0.08) 0.035 (0.08) 0.035 (0.08) 0.035 0.08 60 150 60 120 0.035 0.035 0.035 0.035 0.08 60 120 60 120 0.035 (0.08) 0.035 (0.08) 0.035 (0.08) 0.035 0.08 0.2 100 100 120 200 100 100 120 200 0.035 0.08 (0.12) 0.035 0.035 (0.08) 0.035 0.08 (0.12) 0.035 0.08 0.08 0.125 0.2 60 80 60 80 0.12 (0.15) 0.08 (0.12) 0.12 (0.15) 0.12 0.15 0.2 150 300 150 200 0.12 (0.15) 0.08 (0.12) 0.12 (0.15) 0.12 0.15 0.2 - TB hA - Driven precast piles Prestressed tubular piles Jacked concrete piles - lilA - JIB - Driven metal piles Jaked metal piles piles under low pressure) Cased bored piles Driven cast piles - Maximum Limit of f,, (MPa) Category III II B A B B A A 0.015 0.015 0.015 0.015 0.035 Driven grouted piles Driven_rammed_piles - - IIIB - High pressure grouted piles with diameter> 250 mm Micro piles grounted under high pressure Note: Max. limit unit skin friction, f: bracket values apply to careful execution and minimum disturbance of soil due to construction. 0.1 Chapter 4. Application of CPT in Pile Capacity Prediction 30 Similarly for both open and closed ended pipe piles (UBC Pile 1 to 5), when the pile tip is embedded in a soft soil layer (lower q,), the end bearing is a small component of the total capacity, and the frictional resistance comprises the bulk of the total pile capacity. From Figure 4.4 (pile 1 and pile 2), Figure 4.5 and Figure 4.6, the predicted pile capacities agree well with measured pile capacities. These results suggest that not only the total pile capacity but also frictional resistance can be predicted well by using LCPC CPT method. Since frictional resistance and end bearing are estimated separately in the LCPC CPT method, both frictional resistance and end bearing can be estimated separately in predicting long term pile capacity Chapter 4. Application of CPT in Pile Capacity Prediction 31 Predicted Pile Capacity (kips) 100 0 I 0 I—I I I I 400 300 200 i i I I I I I II 20 LEGEND - shaft total 40 — Pile 1 Pile 2 Pile 3 -c 60- 0 Pile 4 80 100 — - Pile 5 120 Figure 4.4: Pile Capacity Predicted from LCPC CPT Method on UBC Pile 1. 2, 3, 4 and 5 0 Ii) — — - 1 - I I I I I LI shaft total LEGEND Measured (after 43 weeks) Predicted Pile Capacity (kips) 250 500 750 I I I i i i l__i__ I I 1000 Figure 4.5: Pile Capacity Predicted from LCPC CPT Method on ECN[ Pipe Pile 20 100— 80 - 60- 40 20 0 0 Figure 4.6: Pile Capacity Predicted from LCPC (PT 60 Method on ECNU 11x73 H Piles 0 ID -c Predicted Pile Capacity (kips) 0 I—. CD C.) 0 CD 0 0 Chapter 5 Wave Equation Analysis of Piles 5.1 Introduction Since the research of Isascs (1931), it has been recognized that the behaviour of driven piles does not follow the simple Newtonian impact as assumed by many simplified piledriving formulas. Hence a computational tool for the analysis of pile driving, known as the Wave Equation, was developed based on a one-dimensional wave equation. In 1950, Smith developed a solution to the wave equation that could be used to solve extremely complex pile-driving problems. The solution was based on a discrete element idealization of an actual hammer-pile-soil system using a high-speed digital computer. In a paper published in 1960, he dealt exclusively with the application of wave theory to the investigation of dynamic behaviour of pile during driving. Figure 5.1 shows a schematic representation of the wave equation model. Many Wave Equation programs have been developed. One of the most widely used today is a program called WEAP, developed by Goble and Rausche. Since the original program in 1976, WEAP was updated to WEAP87 and then to GRLWEAP which is the program used in the subsequent analysis. 5.2 5.2.1 GRLWEAP Program Background of GRLWEAP Program The pile driving process provides information regarding the soil resistance. The greater the permanent set, S, of a pile under a hammer blow with energy Ek, the less the total 33 Chapter 5. Wave Equation Analysis of Piles 34 I I Transducers attached here Pile Soil Figure 5.1: Schematic Representation of Driving System for Wave Equation Model Chapter 5. Wave Equation Analysis of Piles 35 driving resistance R, which opposes the pile penetration. The energy formula describing the driving process can be expressed as follows: edehEr — 1 E — 1 E = RS (5.1) where: ed is coefficient less than 1 to consider energy loss in driving system eh is hammer efficiency; E,. is rated energy given by manufacturer; 1 is energy lost in pile; E E 3 1 is energy lost in soil; R is total drying resistance, and S is permanent set of a pile under one blow. Assuming Er is known, the values ed, eh, 1 and 5 E E 1 can be estimated, the following can be done: a) Compute the set s using predicted value of R before the pile is driven. The blow count, B, is then merely the inverse of s. b) During pile driving, B may be observed and R computed. This process is known as a dynamic pile test. c) A bearing graph can be constructed with the ultimate soil capacity plotted versus blow count for corresponding depth. This is an analysis of drivability. The wave equation approach differs from the energy formula in that the parameters ed, , and 3 1 E E 1 are computed. They are computed by modelling the driving system, pile, and soil behaviour. Only the hammer efficiency is estimated. Chapter 5. Wave Equation Analysis of Piles 36 Hammer Model The following hammer types can be selected in the program: Diesel hammer with liquid injection. Diesel hammer with atomized injection. External combustion hammers (air/steam/hydraulic). The ram is the most important hammer component. A single mass segment is often used in analysis. For slender rams, often encountered in diesel and modern hydraulic units, more than one ram segment may be necessary for simulation. As a rule, ram segments should not be shorter than 2.5 feet or unnecessary computational efforts will result. Driving System Model The driving system consists of striker plate, hammer cushion, helmet and, for concrete piles, pile cushion. The spring for the pile cushion is modeled in series with the first pile spring. For external combustion hammers, the hammer cushion spring acts in series with the ram spring, as shown in Figure 5.2. The weight of devices like the striker plate, cushion, pile adaptors etc. should be included in the mass between hammer and pile top. Pile Model The pile model consists of springs, masses and dashpots, as shown in Figure 5.3. The pile is divided into N segments whose lengths are given by = aL (5.2) L is the total pile length and a is a multiplier which describes the length of the segment i with respect to the overall length of the pile. Chapter 5. Wave Equation Analysis of Piles 37 Figure 5.2: Hammer Driving System model for ECH hammer (adapted from GRLWEAP Menu) DIVISiONS —. — SKIN FRICTION PILE TOE RESISTANCE Figure 5.3: Pile and Soil Model used in GRLWEAP Program (adapted from GRLWEAP Menu) Chapter 5. Wave Equation Analysis of Piles 38 Therefore: j a. = 1.0, j = 1,2,... ,N. (5.3) i=1 The weight of segment i is then: W, (5.4) = W is the average specific weight and A is the average cross sectional area of the pile element, both averaged over the distance i. Similarly the segment stiffness are = EA (55) E is the average elastic modulus over the element length. Viscous damping is assumed with parameters: 1 EA (5.6) = Cdp top and is a non-dimensionalized input quantity and EA/c is the impedance of the pile Cdp is assumed equal for all elements. Soil model The soil model basically consists of a spring and dashpot, as shown in Figure 5.3. The quake and viscous damping are defined as shown in Figures 5.4 and 5.5. The elastic spring yields at a pile segment displacement equal to q, (quake). Beyond the quake, there no further increase in static resistance, R , with increasing displacement, u. Thus, 3 f R 8 2 = for u, < qj 3 R = for u, > qi is the ultimate static resistance during driving at segment i. (o.7) at each segment is determined from the total ultimate static resistance during driving, which is Chapter 5. Wave Equation Analysis of Piles 39 divided into two parts, friction and end bearing. The percentage of friction is determined by the option, IFERCS , in the input menu of the program. For unloading, i.e. when the pile segment has an upward velocity, a spring rate that is equal to that used in the loading path is used. The damping models used in this study is according to Smith (Goble et al, 1987) which is defined by Equation 5.8. Rd (5.8) — where Rd is a dynamic resistance at segment i; sj is the Smith damping factor at segment i; T is the velocity of pile segment i , and 3 is the static resistance at segment i. R Smith’s damping factor has units of time/length. Numerical Procedure and Integration The time increment is chosen as follow: min(t) (5.9) min(tcrj) stands for the minimum critical time of all segments, i, and p is a number greater than 1.0. The analysis steps are shown in Figure 5.6. Analysis Stop Criteria According to the stop criteria given by GRLWEAP program, the analysis is run until the specified elapsed time, tmax, has been covered. The limitation of tma is 499 ms. For a drop hammer, if the user does not specified a time, the analysis will cover an elapsed Chapter 5. Wave Equation Analysis of Piles R 40 Rut = Ultimate Resistance q = Quake u = Displacement Rut q U Figure 5.4: Definition of Soil Quake Rd Rd = Dynamic Resistance V = Velocity J = Damping V Figure 5.5: Definition of Soil Viscous Damping Chapter 5. Wave Equation Analysis of Piles f 41 tNTIAL.IZE I PREDICT VELOCITIES AND ACCELERATIONS COMPUTE FORCE. F; ACTING ON ELEMENT I COMPUTE ACCELERATION AND INTEGRATE RECOMPUTE FORCE. F . 1 jr COMPARE VELOCITY CURRENT VS PREVIOUS OK OR OF ITERATIONS EXCEEDED NO YESy . T ( COMPUTE ALL. RESISTANCE FORCES INCREMENT “P [ END ANALYSIS I Figure 5.6: Block Diagram of Predictor Corrector Analysis for GRLWEAP Program (adapted from GRLWEAP Menu) Chapter 5. Wave Equation Analysis of Piles 42 time of at least twice the pile length divided by wave speed or 20 ms. For diesel hammer, if the user does not specified a time, the analysis will cover an elapsed time of 2L/c+Sms or 50 ms, whichever is longer. Non Residual Blow Count Computation The difference between the maximum toe displacement, Umt, and the toe quake, qt, is used as a prediction of the final net set of the pile. An average quake used in program is computed as follows: N+1 1 qav = J D 11 z1 (5.10) and qj are the individual ultimate static resistance and quake, respectively, and is the total ultimate staic capacity. N is the number of pile segments. The N + 1 resistance is the end bearing. The predicted permanent pile set is then computed as follows: .9 = Umt — q (5.11) and the blow count is calculated as follows: = 5.3 5.3.1 (5.12) Preliminary Analysis of the GRLWEAP Program Outline When using the GRLWEAP program for pile driving analysis, the hammer, pile and soil parameters are selected according to the driving equipment used, the type of pile and soil type. Some parameters are suggested by the program based on experience and correlations with past field tests. In practice, it is recommended that the influence of the different parameters be examined so that the sensitivity of the parameters can be Chapter 5. Wave Equation Analysis of Piles 43 Table 5.1: Parameters of UBC Pile 3 and Driving Data Length Pile Driving Data Outside Diameter (feet) (in) 60.00 12.75 Hammer weight drop (kips) (feet) 6.20 4.00 Wall Ended Embedded Thickness Depth (in) (feet) 0.375 closed 55.00 Hammer Cushion area E modulus thickness ) 2 (in (ksi) (in) 144.00 100.0 2.25 determined. This will allow a reasonable range of parameters to be selected for analysis. In order to select the parameters in the analysis, a preliminary analysis was carried out. 5.3.2 Basic Pile and Soil Conditions UBC pile 3 is a steel pipe pile, the details of this pile are summarized in Table 5.1 with driving data. The ultimate capacity of UBC pile 3 determined from the static axial load test is 133 kips. The soil condition are given in Figure 2.4. 5.3.3 Analyses and Results The influences of various parameters are examined and the results of the analysis are shown in Figures 5.7 to Figure 5.13. The results are discussed below in terms of the parameters considered. A. Effect of Friction (IPERCS) From the results shown in Figure 5.7 and Figure 5.8, the effect of IPERCS is small. For the same ultimate total resistance, the blow count generally increased with increasing IPERCS, (i.e. with increasing skin friction and decreasing tip resistance). The blow count is only increased 3.3% from 25% IPERCS to 75% IPERCS for an ultimate Chapter 5. Wave Equation Analysis of Piles 44 250 200 (1) 150 ci D o 1QQ Jskin/Jtoe 0.20/0.15 = 25% ocn IPERECS = 5D = 00000 IPERECS 50H IPERECS 0 I I I I I I j I I 10 0 I I I I I I I I I Ii I I 20 30 Blow Counts (b/foot) = 75 1I I I I I 40 50 Figure 5.7: Effect of Friction Percentage 250 U) 1 50 - - 10000000 DD /7 50- IPERCS IPERCS IPERCS IPERCS Solid Line Qs/Js Dashed Line Qt/Jt 0 I 0 liii 1111111 10 III I I II I 11111 20 30 Blow Counts (b/foot) = III = = = = 25% 75% 25% 75% 0.1 0/0.20 025/0.10 liii 40 I I 50 Figure 5.8: Effect of Friction Percentage with Different Values of Quake Chapter 5. Wave Equation Analysis of Piles 45 capacity equal to 133.0 kips. Figure 5.8 also shows that the influence of IFERCS is small for constant Quake/J for both skin and tip. B. Effect of Hammer Efficiency The effect of hammer efficiency is very important in the analysis, as shown in the Fig ures 5.9 and 5.10. Figure 5.9 shows that the blow count increases about 42% when the hammer efficiency decreases from 75% to 50% at an ultimate resistance of 133 kips. The change in blow count versus the hammer efficiency is not proportional for different ultimate capacities, as shown in Figure 5.10. When the ultimate resistance is low, lower energy is needed to drive the pile to the desired depth. In this case increasing hammer efficiency does not effect the blow count significantly. When the ultimate resistance is high, the energy needed to drive the pile to the desired depth increases. The higher the ultimate resistance, the more sensitive is the blow count to hammer efficiency. From the above results, it is clear that hammer efficiency has an important influence on the analysis. In practice, it is a difficult to get accurate hammer efficiency value, unless it is measured in the field during pile driving. C. Effect of Smith’s Damping Smith’s damping values have some influence on the results. The effect depends on the combination of 3 ,J 4 j j,. and Jt as shown in Figure 5.11. For the same ultimate capacity, the blow count increased with increasing damping values. The largest difference occurs if both J . and J 3 0 are increased at the same time. According to the definition of the damping model shown in Equation 5.8, the dynamic soil resistance is proportional to the damping factor. The dynamic resistance will increase with increased soil damping factor. Similarly the total soil resistance during pile driving increases with damping. The blow count will increase with increasing soil resistance for a given hammer energy. On the Wave Equation Anaiysis of Piles Chapter 5. 46 250 = 200 (‘) - 1 50 = 100- oHammerEfficency= 25% DCDCD Hammer Efficiency = 50% Hammer Efficiency = 75 50H 0- i I 0 I I I 10 I I I I I I I I I I I I I I I I 20 30 Blow Counts (b/foot) I I I I I I 40 I 50 Figure 5.9: Effect of Hammer Efficiency 80 — - 7fl L) > U C - U uJ - - - - 60- ci) E E 0 5Q - 00000 Rut = Rut = Rut = Rut = DDDDO 4o I 0 I I I I I I 20 I I I I I I I I I I 40 Blow Counts (b/foot) 1_i I 60 Figure 5.10: Effect of Hammer Efficiency with kps kips kips kips 50 100 150 200 I I I I I I I 80 Chapter 5. Wave Equation Analysis of Piles 47 250 200 (1) 1 50 cs - - iooI Jskin/Jtoe Jskin/Jtoe Jskin/Jtoe **** Jskin/Jtoe Jskin/Jtoe 00000 CCCCD j 50 0 I 11111 II 1111111111 III = = = = 0.10/0.15 0.15/0.15 0.20/0.15 0.15/0.10 0.20/0.10 liii II I II 50 40 30 20 Blow Counts (b/foot) 10 0 11111 = Figure 5.11: Effect of Smith’s Damping 250 j 200 U, - 1 50 0 D 00000 Dacco 50 ocj.o’ 0• I 0 I I I I Qskin/Qtoe Qskin/Qtoe Qskin/Qtoe Qskin/Qtoe = = = 0.15/0.106 0.18/0.106 0.18/0.250 0.18/0.330 I I 20 40 Blow Counts (b/foot) Figure 5.12: Effect of Skin Quake 60 I I I 80 Chapter 5. Wave Equation Analysis of Piles 48 other hand, the results are nearly the same when one parameter is increased and the other decreased by the same amount, ie. curve 0.15/0.10 is about the same as 0.10/0.15, and the curve 0.20/0.10 is about the same as 0.15/0.15, where the ratio is Jskin/Jtoe. Based on these results the ratio of J k/Jt€ is not as important as the overall value of 3 the damping JsIcjn. + Jtoe. Damping is generally selected based on experience, however this points out the need for good in situ damping values to confirm the assumption that are being made D. Effect of Quake The effect of the quake parameters is similar to Smith’s damping parameters, although quake may have a greater influence. When soil quake increases, the soil stiffness decreases, and consequently the maximum amount that the hammer can drive the pile is reduced (Authier and Fellenius, 1980). Figure 5.12 shows that the blow count increases about 30% for values of q,kin/qtoe increasing from 0.10/0.106 to 0.18/0.33. Keeping the q 3 constant, the blow count increased about 10% with the qtoe increased from 0.106 to 0.25 at ultimate capacity equal 133 kips, as shown in Figure 5.13. The results show that the quake values have an important influence on the final result of analysis using GRLWEAP program. 5.3.4 Conclusion Based on the preliminary analysis for UBC Pile 3, it can be concluded that hammer efficiency is the most important factor in the analysis, followed by quake, and Smith’s damping values. The influence of IPERCS is very small, and the result of the analysis will not change very much for different proportions of skin friction to end bearing. Chapter 5. Wave Equation Analysis of Piles 49 250 200 U) 1 50 - H D 50H 0 Qskin/Qtoe Qskin/Qtoe Qskin/Qtoe Qskin/Qtoe 00000 00000 I — I I I I I I I I I I I I 10 0 I I I I I I I I I I I = = = I I 0.10/0.106 0.10/0.150 0.10/0.200 0.10/0.250 = I I I I I I I I I 50 40 30 20 Blow Counts (b/foot) Figure 5.13: Effect of Toe Quake 500 400 U) 300 0 0 200 1 00 IPERCS=75% 0 — 0 I 50 I j 100 I I I j I 250 200 1 50 Blow Counts (b/foot) I I I I 300 11111 350 Figure 5.14: Bearing Graph of Pile 3 at Depth of 55 feet II 400 Chapter 6 Drivability Analysis 6.1 - Blow Count Prediction Introduction The purpose of the drivability analysis presented herein is to attempt to predict the blow counts of a driven pile. This will be done using the program GRLWEAP and the pile capacity evaluated from CPT data. For a given hammer-pile-soil system, the blow count varied with penetration resis tance during pile driving. The total driving resistance consists of both static and dynamic soil resistance. According to the definitions in the GRLWEAP program, the static and dynamic soil resistance are determined from the theory of one-dimensional wave equation propagation in the pile and depends on soil parameters such as quake, damping, and ulti mate static resistance during driving, The penetration resistance can be calculated from Equation 6.1. Rt=R + 8 Rd (6.1) where: R is the total driving resistance 3 is a static resistance component of total driving resistance acting on pile shaft and R tip Rd is a dynamic resistance comppnent of total driving resistance acting on pile shaft and tip 50 Chapter 6. Drivabiity Analysis - Blow Count Prediction 51 The R 8 increases linearly with pile penetration displacement before the pile penetra tion displacement reaches the value of quake. When the pile penetration displacement reaches and exceeds the quake, the R 3 keeps a constant value of is ultimate static resistance during driving as defined in Equation 5.7, which has to be estimated in drivability analysis. The Rd is defined in Equation 5.8, in which the Rd is a composite function of static resistance, pile velocity and damping factor. The pile capacity determined from the LCPC CPT method (Bustamante and Gi aneselli, 1982) was chosen to evaluate which is used as an input parameter in the GRLWEAP program. As discussed in Chapter 4, the ultimate pile capacity determined from LCPC CPT method represents the long term pile capacity. For many soils, partic ularly cohesive soil, the ultimate pile capacity several days after pile installation can be significantly greater than the ultimate static resistance during driving and immediately after driving (Fenske and Hirsch, 1986). Figure 6.1 shows how the pile capacity increases with time for piles in cohesive soils (Soderberg, 1962). The maximum load capacity of a pile was the ultimate (or long term) capacity when the pore pressure fully dissipared. Data presented by Soderberg (1962) showed that the increase in ultimate load capacity of a pile depended on the excess pore pressure developed around the pile, and the rate of dissipation of these pore pressure. In prediction long term pile capacity, both shaft re sistance and end bearing components increase with time because of soil set up processes, such as pore pressure dissipation and aging, around the pile shaft and under the pile tip. Furthermore, shear failure takes place at the surface between soil and pile shaft dur ing large displacement encountered in continuous pile diving. The elapsed time between blows is very short compared to the soil set up processes described above. Therefore, the soil strength during pile driving will be less than the in-situ soil strength before pile driv ing. Typical in-situ and remoulded soil shear strength profiles for normally consolidated clays are shown in Figure 6.2 after Fenske and Hirsch, (1986). Chapter 6. Drivability Analysis - Blow Count Prediction 52 0 Seed and Reese 20 C U I G) 30 C. 40 0 50 Housel Burton Quay E 60 E x 70 80 90 - .5 1 10 1 00 Time. Hours 1000 Figure 6.1: Increase of Load Capacity with Time (after Soderberg, 1962) Shear Strength ol Soil. Xips/F1 2 3 U 0 0 0 -c --0 C, C Figure 6.2: Strength Loss by Remoulding for Normally Consolidated clays. (after Fenske and Hirsch, 1986) Chapter 6. Drivability Analysis We can estimate - Blow Count Prediction 53 during pile driving from long term pile capacity by introducing reduction factors, less than 1,0, into the components of pile capacity. Estimating the reduction factors for different soils requires some assumptions to be made that reflect the effects of driving. The distribution of soil resistance between pile shaft and pile tip is determined by the type of soil in which the pile is embedded. For example, the pile shaft resistance can be much larger than the end bearing if the pile tip is embedded in a soft soil layer as in the case of a friction pile. The pile is considered as an end bearing pile when the pile tip is embedded in hard soil layer. In this study, piles are divided into friction and end bearing piles according to pile resistance distribution. In both cases, the values of CPT q, are used to evaluate the soil conditions. Three methods were used to predict blow count of driven piles. These methods are based on total pile capacity, end bearing component, and shaft resistance component, respectively. By using different analysis methods, the writer expects to find the best em pirical correlation between and CPT q,. Based on a preliminary study, the method based on shaft resistance component appears to give most reasonable results. The lat ter method is done in three steps. The first step is to determine reduction factors for calculating shaft resistance during driving, Rsr, from long term shaft resistance. This is done based on experience and in-situ test results. The second step is to determine through simulated modelling calculation using the GRLWEAP program. The simulated modelling calculated blow counts to match the measured blow counts using different val ues of The third step establishes empirical correlations for different types of piles by using R 5 determined in the first step, determined from CPT q. qea determined in second step and ea which is known as the equivalent cone bearing. It estimates the soil influence range corresponding to the depth of the blow counts recorded during pile driving. From the empirical correlations developed, can be evaluated from long term Chapter 6. Drivabthty Analysis - Blow Count Prediction 54 pile capacity and CPT q. The details of the result and their limitations as well as factors that affected the results are given in following sections. 6.2 Analysis Parameters Selection The LCPC CPT method is used as the basic method to evaluate pile capacity from CPT. An equivalent cone bearing, qea, for a given depth is proposed as shown in Figure 6.3. The UBC Cone Interpretation program CPTINT is used to give an average value of q for each foot in depth using data files that have q measured every inch or 2 inches. Then, is calculated by taking the average values of q over the selected depth range. The range includes 1.5 D above the pile tip and one foot plus 1.5 D below the pile tip, where D is the diameter of pile. The additional one foot below the pile tip, that is different from the LCPC CPT method, is used since we need to know the penetration resistance in one foot penetration intervals. In this way, the penetration resistance at the pile tip can be considered by taking average values of q corresponding to the depth of blow counts recorded during pile driving. The equivalent cone bearing will be used as an in situ measured parameter in the empirical method proposed. The remaining parameters used to determine pile capacity from CPT are the same as those proposed in the LCPC CPT method. In the GRLWEAP analysis, program parameters, such as quake, damping, and ham mer efficiency, etc., were selected from the GRLWEAP menu. Based on the study on the sensitive of wave equation by Ramey (1977) the skin damping is third of the toe damping. For stratified sand and clay with pile tip in sand a reasonable skin damping is 0.067 to 0.10. Therefore, the damping for clay and sand use the values recommended by GRLWEAP, however a skin damping value of 0.10 s/ft for silt was used in this study. Ta ble 6.1 shows the values of quake and damping for different types of soils as recommended Chapter 6. Drivability Analysis D ft - a) - 55 Blow Count Prediction D = Pile Diameter a=1.5D b=1.Ofoot qea a) Figure 6.3: Proposed Model for qea in GRLWEAP (1987). In Table 6.1, d is the effective toe diameter for a displacement pile. For open cross sections, the full pile width or diameter is only applicable if the soil forms a plug in the pile. There were three types of hammer in the study: drop hammer, diesel hammer and stream hammer: The hammer efficiency was selected according to the values suggested by the GRLWEAP menu, 0.67 for drop hammer and 0.72 for diesel - hammer. The energy of a diesel hammer changes with different combustion chamber pressure (GRLWEAP, 1987). The blow rate of a diesel hammer effects the energy trans mitted to the pile. The lower the hammer blow rate, the higher the energy delivered into the pile. An adjustment method is proposed when using diesel hammers to normalize the predicted blow count in terms of blow rate. This is necessary so that predicted and measured blw count can be compared at a similar hammer efficiency. — Each pile was divided into 5 foot segments for the analysis. An adjustment was made Chapter 6. Drivability Analysis - Blow Count Prediction 56 Table 6.1: Proposed Quake and Damping Values Used in Study Soil Type Clay Silt Sand Quake (in) Toe Skin 0.10 cl/120 0.10 d/120 0.10 d/120 Damping (s/ft) Skin Toe 0.20 0.15 0.10 0.15 0.05 0.15 for each segment so that it could be located in only one type of soil. A relative dimensionless magnitude distribution of skin friction was selected, which is defined by option ITYS = —1 in GRLWEAP. With this option, the shaft resistance is accumulated with increasing penetrating depth. Figure 6.4 shows a an example of the skin friction distribution on UBC pile 5 which was installed to a depth of 102 feet. 6.3 Selection of Prediction Methods Based on Pile Capacity In order to establish a method to predict blow counts of driven piles based on the CPT, correlations between and CPT q were evaluated in this study. Three methods were carried out based on total pile capacity, end bearing component, and shaft resistance component. The analysis based on total pile capacity multiplies a reduction factor. less than 1.0, to the pile capacity determined from LCPC CPT method to get RutS Rut is then input into the GRLWEAP program, which computes blow counts. The ratio of shaft resistance to total pile capacity before reduction was taken as IPERCS. It is assumed that IPERCS is constant at a given depth. The reduction factor, defined as the ratio of Rut to total pile capacity, was determined by making the blow counts calculated equal to the blow counts measured during driving. The calculated blow counts were determined by GRLWEAP simulated modelling calculations. A reduction factor of 0.70 was determined for all soil types based on the results of total pile capacity analysis on UBC pile 5. Figure 6.5 Chapter 6. Drivability Analysis - 57 Blow Count Prediction Friction Resistance (kips) 6 8 10 12 14 0 10 20 30 40 .JLI G) ‘I— tTh L)L/ -c — D 70 80 90 100 110 Figure 6.4: Shaft Resistance Distribution of UBC Pile 5 at a Depth of 102 Feet Chapter 6. Drivability Analysis - Blow Count Prediction 58 shows comparison of predicted blow counts to measured blow counts of UBC pile 5. Two hammer drop heights, 6 feet and 7 feet, were used in the modelling according to the field tests. The same reduction factor was used in the analysis of TJBC pile 6. Table 6.2 shows results from UBC pile 5 and 6. The results in Table 6.2 show that predicted and measured blow counts are close for UBC pile 5 and for UBC pile 6 at depths of 48 and 102 feet. However the predicted blow counts were larger than measured at the depths of 60, 80 and 90 feet for pile 6. Large differences are made in end bearing pile condition for UBC pile 6 because of overestimation of In order to fit the predicted and the measured blow counts better, a smaller reduction factor, less than 0.7, is required to estimate for UBC pile 6 where end bearing pile conditions are present. The predicted blow counts are very close to the measured ones in friction pile condition for both pile 5 and 6. Also, the results of Table 6.2 suggest that the reduction factor changes with different sizes of piles for end bearing conditions. Hence pile size must be considered in total pile capacity analysis. When the pile size increases, the pile capacity increases for both shaft resistance and end bearing. The shaft resistance changes because the pile shaft area increase. A study by Meyerhof (1982) showed that the ultimate unit skin friction of driven piles in sand of a given density is practically independent of the pile diameter. The results of this study also show that a good blow count prediction was made by using the determined from one reduction factor for friction piles of different sizes. However it was not clear how pile size affects the end bearing during pile driving. The results from LTBC piles 5 and 6 suggest that it is difficult to determine from long term total pile capacity using only one reduction factor for different size piles. However, it is possible that one reduction factor can be used for shaft resistance and another reduction factor for end bearing. The results also show that the end bearing and shaft resistance during pile driving are smaller than that of long term capacity. The Chapter 6. Drivability Analysis - Blow Count Prediction 59 Table 6.2: Predicted Blow Counts Based on Total Pile Capacity Method Soil Type Clay Sand Sand Sand Clay Depth (feet) 48 60 80 90 102 Pile 5 Predicted Measured 3.5 3.0 8.6 6.0 13.1 15.0 16.0 19.0 11.5 11.0 Pile 6 Predicted Measured 8.8 5.0 33.8 21.0 45.0 29.0 61.1 37.0 27.7 25.0 general agreement observed for UBC pile 5 suggests that using reduced pile capacity in the CRLWEAP program to predict blow counts produces reasonable results. The method based on end bearing uses the end bearing component of long term pile capacity to determine end resistance during driving. Again, a reduction factor, less than 1.0, is multiplied to the end bearing component of pile capacity to get the end resistance during driving for a given depth. Then, in the modelling calculation, end resistance is kept constant while is changed until the calculated blow counts fit the measured blow counts. In the calculation, the end resistance was kept constant and the IPERCS was allowed to vary with The final and IPERCS was then used to determine shaft resistance, Rsr. Figure 6.6 shows the results of and Rsr determined from end bearing analysis on UBC pile 5. A reduction factor of 0.7 was used to calculate end resistance during driving from long term end bearing. It is observed from Figure 6.6 that the . 87 reaches its maximum value of 180 kips at a depth of 84 feet, then, decreases R as low as 50 kips at a depth of 91 feet. In practice, accumulated friction resistance has to increase as pile penetrating depth increases. The results from Figure 6.6 suggest that the accumulated friction resistance decreases considerably with increasing depth. This apparent contradiction occurs because the end resistance is difficult to model using only one reduction factor as mentioned in total pile capacity method. The higher the end bearing, the lower the shaft resistance for a given R,. . If a higher shaft resistance 1 Chapter 6. Drivabitity Analysis - 60 Blow Count Prediction Blow Count per Foot io o I 20 - = - - I I I I ooco-o - •..-. 30 20 I I I I I I I Fie’d Record Hammer Drop Hammer Drop I = = I T feet 6 feet I -- I - - - oo: 120: Figure 6.5: Predicted Blow Counts for UBC Pile 5 Based on Total Pile Capacity Method Chapter 6. Drivability Analysis - Blow Count Prediction 61 is desired, a smaller end bearing is required. Therefore a smaller reduction factor (less than 0.7) must be used. Figure 6.6 demonstrates that when one reduction factor is used for all soil depths to obtain end resistance, it is not possible to keep the shaft resistance increasing with increasing depth. The analysis based on shaft resistance component of pile capacity is very similar to that of the end bearing, the only difference is that the long term shaft resistance is multiplied by a reduction factor to obtain the shaft resistance during driving. Based on the following result, this method is considered to be useful. The details of this method are given in following sections. 6.4 Blow Count Prediction Based on Pile Shaft Resistance 6.4.1 Introduction The blow count prediction based on shaft resistance component of pile capacity was carried out by establishing an empirical correlation between R .,. / 3 and C PT q. 81 R . is the shaft resistance during pile driving and is determined by introducing a reduction factor, less than 1.0, into the long term shaft resistance determined from LCPC CPT method. Since determination of is summation of static shaft and toe resistance during driving the reflects the static toe resistance during driving if Rsr known. As discussed previously, in the total pile capacity method, it is possible to use a single reduction factor to estimate for friction pile because the predicted blow counts matched well with the measured blow counts. When predicting pile capacity from the LCPC CPT method, the shaft resistance increases steadily with increasing depth. The ratio of shaft to total pile capacity at a given depth can be used to indicate static resistant condition of the pile. This ratio is larger for friction piles and smaller for end bearing piles as demonstrated in Figure 6.7. Figure 6.7 also shows that the change in the ratio Chapter 6. Drivability Analysis 50 - Rut & Rsr (kips) 200 150 100 I 0 62 Blow Count Prediction I I I I i i i i 250 300 I / 20 40 80 — Rsr Rut UBC Pile 5 — — - 120 Figure 6.6: R., and Rar Determined from Analysis of Blow Counts Prediction Based on End Bearing Method Chapter 6. Drivability Analysis - Blow Count Prediction 63 of shaft to total pile capacity is inversely proportional to the values of q. If the same inverse relationship exists between Rgr/Rut and q during pile driving, it is possible that a correlation between Rsr/Rut and CPT q can be found to determine assuming the following criteria are met: (1) for a particular soil a single reduction factor can be used to determine 3 R , . from long term shaft resistance; (2) Rsr remains constant for a given depth during pile driving; (3) Rsr increases with increasing depth. An empirical correlation between 3 R r /Ru and q, was established using GRLWEAP program stimulated modelling calculations. In the following discussion, data from differ ent sites and pile types are used to find empirical correlation between Rsr /R and q. The analysis was initiated by estimating the reduced shaft resistance during pile driving, Rsr, from long term pile capacity. Then, different values of were input into GRLWEAP program to calculate blow counts until the calculated blow counts equal those measured during pile installation for a specific depth. A final and ratio of Rgr/Rut was de termined for every one foot depth increment. Finally an empirical correlation between Rsr/Rut, (IPERCS), and equivalent cone bearing, qea, was established to determine 6.4.2 Determination of Reduction Factor The shaft resistance during driving, Rsr, was determined by introducing a reduction factor to the long term shaft resistance determined from the LCPC CPT method. For cohesive soils, the increase in pile capacity with time is significant, as shown in Figure 6.1 (Soderberg, 1962). The static frictional resistance during pile driving may be significantly less than the long term static shaft resistance after pile driving (Fenske and Hirsch, 1986). The effects of driven pile in clay are classified into four categories by de Mello (1969): (a) remoulding or partial structure alteration of the soil surrounding the pile; Chapter 6. Drivability Analysis - Blow Count Prediction 64 qc ave. (bars) Shaft/Total Pile Capacity o 40 80 120 160 I I I (%) 100 50 I I Soil Profile - ClAY 25— 50- I : SAND (. silty lenses 75 Ii) - I 100- ClAY 25- 150— - I Figure 6.7: Shaft Resistance to Total Pile Capacity vs Depth with CPT q, UBC Pile Research Site Pile 1 to 5 Chapter 6. Drivabiity Analysis - Blow Count Prediction 65 (b) alteration of the stress state in the soil in the vicinity of the pile; (c) dissipation of the excess pore pressures developed around the pile; and (d) long-term phenomena of strength-regain in the soil. For cohesionless soils, when a pile is driven, the soil is usually compacted by displace ment and vibration, resulting in permanent rearrangement and some crushing of soil particles. The frictional resistance depends on the stress history of soil, the shape and roughness of pile, and other factors (Meyerhof, 1982). It is usually assumed that in sand the static friction resistance during driving is equal to the static resistance after driving. Reduction factors of 0.5 for clay and 1.0 for sand, were used in a pile driveability study by Tang et al (1988). However in research on soil set up, Fellenius et al (1989) showed that the frictional resistance increases significantly up to twice of the frictional resistance during initial driving in restriking after 1 day when a pipe pile embedded in stratum of sandy clay and silty sand. In the following analysis, reduction factors, less than 1.0, were used for both clay and sand. The reduction factors were determined using friction pile conditions on TJBC pile 5 and 6 assuming the followings: (1) The pile tip was embedded in soft soil layers where the values of CPT q were smaller than 10 bars; (2) (IPERCS) remains constant at 90 % in the GRLWEAP simulated mod elling calculation. Based on the proceeding assumptions, increment and Rsr was calculated from was determined for each one foot depth using Rsr/Rut of 90%. The reduction factors were finally determined by comparing R r to long term shaft resistance. In this manner, 3 reduction factors of 0.5 for clay and 0.7 for sand were determined. Many factors, such as OCR, structural failure of soil, volume displacement of the soil and pile geometry will affect the shaft resistance of the pile (Meyerhof, 1982). The reduction factors chosen reflect the comprehensive influence of these factors. Chapter 6. Drivability Analysis 6.4.3 - Blow Count Prediction 66 Results The analysis procedure is presented in the flow chart shown in Figure 6.8. Each pile type was analyzed separately. For each pile, 5 R , . was first calculated from long term shaft resistance at given depth by using the reduction factors of 0.5 for clay and 0.7 for sand. The was determined by making the calculated blow counts match the measured blow counts. In the calculation procedure, the i.e. RSr/RUt, and between Rsr/Rut and IPERCS varied with Final IPERCS, were determined for each one foot depth interval. The correlation qea was established and a linear best fit was applied to the data to indicate the proposed correlation. Results on Closed-Ended Steel Pipe Pile This analysis is based on UBC pile 1, 2, 3, 5 and 6 as well as Tilbury pile 1 and 2. The details of sites, piles and pile installation procedures were given in Chapter 2 and 3. For the UBC piles, when the pile was driven above 50 feet or below 92 feet, the pile tip was embedded in cohesive soil where values of q, were lower than 20 bars. The calculated ratios of Rsr/Rut were 80 to 100 percent, indicating a lower end bearing, and confirming the friction pile behaviour. When the pile was driven from 50 to 90 feet, the calculated values of 3 R r /Rut varied from 70 to 45, indicating higher end bearing and showed end bearing pile behaviour. The Rsr/Rut versus qea is shown in Figure 6.9 for TJBC pile 3 and 5 and in Figure 6.10 for UBC pile 6. Since UBC pile 1 and 2 were only driven into depth of 47 feet and 45 feet, respectively, all calculated values of 8 R / R were in the range of 80 to 100. Clearly when the pile tip was embedded in hard soil the Rsr/Rut was lower, whereas, when pile tip embedded in soft soil the Rsr/Rut was higher. qea is adopted as parameter to give the relative strength of the soil, a correlation between Rsr/Rut and qea can be established to show the tendency of Rgr/Rut to change Chapter 6. Drivability Analysis - Blow Count Prediction 67 For each considered depth Shaft Resistance qea from LCPC CPT Method V Reduced Shaft Resistance Rsr 0.5 Clay, 0.7 Sand Assume Rut IPERCS = Rar/Rut Hammer, Pile & Soil V GRLWEAP Program .4 Parameters Cal. Blow Counts Parameters from WEAP Menu :_ - Recording Rut 1PERCS: Rsr/Rut Figure 6.8: Calculation Steps of Establishing Empirical Correlation between ea for Specified Depth and Chapter 6. Drivabiiity Analysis Blow Count Prediction - 68 110 1 00 90 - — e . 0 - 00000 .. 80: ----. Pile 5 Pile 3 Point 1 : 70- ••.•• • . _60- 50 • - •• • •. 0 .‘ . 30 20100 — I I 0 I I 40 20 I I I I I I Figure 6:9: Rgr/Rut VS I I 100 80 qea (bars) 60 120 I I I 140 I I I 1 160 qea for UBC Pile 3 and 5 110 100 0 0 90- 8 0 80->° 0 0 70 0 - -S 60— 00 0 D 00 50- 0 U, 40 30 20 0 - - Point 2 Point 3 - - 100— 0 I_I 20 11111111 40 60 I 11111 80 100 qea (bars) 11111 120 140 Figure 6.10: /Ru 37 vs q for UBC Pile 6 R II 160 II 180 Chapter 6. Drivability Analysis with soil variation. - Blow Count Prediction 69 The results show values of R r/Rut decrease with increasing 8 qea When a linear best fit line is plotted through the data, the trend of Rgr/Rt versus q for UBC piles 5 and 6 agree well, as shown in Figure 6.11. At the range of ea from 50 to 110 bars, most data points are located below the line for pile 5. For pile 6 the line appears to give an average values. The calculated values of Rsr/Rut were unreasonable low above 10 feet for pile 5 and above 5 feet for pile 6 for the values of qea A threshold penetration depth must be reached to get reasonable results. This depth is defined as the effective beginning depth. Some values deviate significantly from the best fit lines. This is somewhat controlled by soil type, especially when the soil type changes either from soft to hard or from hard to soft. This point will be discussed in the following section. For the Tilbury piles, the values of Rsr /R were calculated for piles 1 and 2. A diesel hammer, D30-l3, was used for pile 1 below 24 feet and for pile 2 below 15 feet. Figure 6.12 shows the results of calculated values of Rsr/Rut corresponding to the values of qea at different depths. For a given value of and pile end bearing, R 3 /R increases with increasing depth because shaft resistance increases. Pile 1 was used to examine the depth influence. From the pile 1 data in Figure 6.12, the values of Rsr/Rut tend to be a constant value of about 50 percent. A non-linear tendency is clearer for the Tilbury piles than for the UBC piles. A comparison of UBC and Tilbury piles is given in Figure 6.13. All three linear best fit lines agree well. For UBC pile 5 and both Tilbury piles, most of the points are scattered beneath the best fit lines when the values of qea are smaller than 110 bars. When the values of q are larger than 125 bars, Rgr/Rut tends to be constant. The variation of Rsr/Rt with ea will be discussed later in the statistical analysis. Chapter 6. Drivability Analysis - Blow Count Prediction 70 11010Q-o 0. o— - I I 0 20 40 I 60 I I I I 100 80 ceo (bars) I I 120 I I 140 160 180 vs q 0 for UBC piles Figure 6.11: 1101 00 - 0- iii 0 ii 20 III 40 I 60 Figure 6.12: II I 80 100 qea (bars) vs 0 q III II 120 140 111111 for Tilbury piles 160 180 Chapter 6. Drivabiiity Analysis - Blow Count Prediction 71 110 100 90 80 — 70 60 50 40 30 20 10 0 qea (bars) Figure 6.13: vs qea for UBC and Tilbury piles Results on Open-Ended Steel Pipe Pile The analysis on open ended steel pipe pile was carried out oily for UBC pile 4. From the observation (Davies, 1987) soil plug was formed during pile driving. However in the GRLWEAP program, the pipe pile with soil plug is simply modelled as a nonuniform pile. Such that the properties of the pile change in the sçil plug reflect the combined pile and soil properties (Goble et al.,1987). According to this model, relative movement and friction between soil plug and pile interface are ignored. Field records of the soil plug are given in Table 6.4 (Davies, 1987). The results of calculated R,r/Rut for piles 4 and 5 are given in Table 6.3 to show the difference between open-ended and closed-ended piles. The field record shows that relative movement between soil plug and pile did take place during tile driving. This movement causes an increase in shaft resistance. The resistance of open-ended pile tends to increase because - Chapter 6. Drivability Analysis - Blow Count Prediction 72 Table 6.3: Record of Soil Plug and Calculated Rsr /R Data Embedded Depth (feet) 48 50 55 60 65 68 71 76 Length Without Plug (feet) 21,2 59.6 56.0 52.3 48.8 46.7 44.3 40.8 Pile 4 Measured Length Interval of Embedded Plug Depth (feet) (feet) 29.1 2 30.6 34.2 5 37.9 5 41.4 5 43.5 3 45.9 3 49.4 5 Calculated /R 3 R Pile 5 Calculated Rsr/Rut (feet) (%) (%) 1.5 3.6 3.7 3.5 2.1 2.4 3.5 78 65 56 73 85 62 73 70 76 54 51 67 50 61 61 48 Interval Plug of friction between the soil plug pile interface, however the resistance also tends to de crease because of smaller end bearing of the open-ended piles. The results indicate that the values of 3 R / R of open-ended pile are larger than for equal diameter closed-ended piles. Looking at depths below 60 feet, the open-ended pile 4 behaved like a friction pile, whereas the closed-ended pile 5 behaved like an end bearing pile. It can be con cluded that for open-ended piles, when relative movement exists between soil plug and pile, the resistance is mainly composed of shaft resistance, hence an open-ended pile will behave like a friction pile. Also Rsr/Rut is larger for an open-ended pile than that for a closed-ended pile. Results on H Piles The H piles included Tilbury H12x53 pile, ECNU H10x42 and ECNTJ H14x73 piles. Since the end bearing is very small due to small end areas, H piles always behave like friction pile as discussed in Chapter 4. Chapter 6. Drivability Analysis - Blow Count Prediction 73 110- 1 00 cnxrxD L00 - 00 O 800 0 9Q 0 0O - O0 0 80- ° 0 0 0 0 - —. 0 0 oo0g 0 0 7060- 30 20 - 10I 0 I 20 I 40 I I 60 I I I I I I I 100 80 qea (bars) Figure 6.14: /R 57 vs R 0 q I I I I 120 I 140 I I 160 I 180 for Tilbury H12x53 pile The calculated values of Rar/Rut were fiom 75 to 100 percent. Figure 6.14 shows the results of the Tilbury H pile. Figure 6.15 shows the results of the ECNTJ H piles. In these figures, numerous points are located in a band of 25 percent where the values of qea change irom 45 to 170 bars. The linear best fit line appears to give average values of calcu1atd values; The linear best fit lines agree well for these three H piles even though the points scattered in relative wider band,V as shown in Figure 6.16. V In this study, unreasonable results were made in determining end bearing for some depths. The end bearing, determined by calculated R, 4 minus the shaft resistance during - driving, was larger than the end bearing predicted from the CPT. In order t. explain this problem it is necessary to consider the determination of shaft resistance of the H piles. The shaft resistance of H piles was estimat&l using the outside rectangular area. During pile driving, a soil plug may be formed in the hollow sides of the H pile. This soil plug will affect the magnitude of the shaft resistance. Estimating the change in shaft Chapter 6. Drivability Analysis - Blow Count Prediction 74 4030 - - ••... H1Q*42 opooo H14*73 C I 20 40 I 60 I I 80 i I 100 120 140 qea (bars) Figure 6.15: R,r/R,.j vs ea ‘ I I 160 I 180 I 200 I 220 240 for ECNU H piles 110- DD 4030°-°-Q - - 20- 0— I’ 0 20 I 40 II 60 80 III II 100 12Q 140 160 ECNU H10*42 ECNU H14*73 H12*53 TIL ii I_ I I T 180 200 220 qea (bars) Figure 6.16: /R 87 R VS ea for H piles 2 Chapter 6. Drivability Analysis - Blow Count Prediction 75 resistance is more complex since it is difficult to model the types of soil plug formed in H piles. Based on the results, as shown in Figure 6.16, an average value of 90 for 8 R / R can be used to predict when q is smaller than 75 bars. The relationship given by the best fit line in Figure 6.16 can be used when qea is larger than 75 bars. This will be discussed further in the statistical analysis. 6.4.4 Influence Factors The empirical correlation between Rsr/Rut and qea is affected by many factors. The recorded blow count during pile driving is influenced by many variables within the hammer-pile-soil system. Firstly, soil conditions change from site to site. The values of CPT qc may be very different even for the same kind of soil. In this study, the CPT results are only used to indicate the relative strength of the soil. Secondly, the values of Rsr/Ru will change with increasing depth and pile size. Also the soil plug formed within open-ended piles must be considered when more complex soil resistance condition influence the values of Rsr/Rut. The final and perhaps most important factor is the ham mer efficiency. The energy of diesel hammer changes with different combustion chamber pressure and soil conditions. The recorded blow count is always related to a hammer blow rate. In order to make comparisons between predicted and measured blow count, adjustment of blow counts for hammer blow rate is proposed for diesel hammers. Soil Profile ea is an average of q,, values and therefore describes the soil resistance to end bearing type penetration. The soil profile of each site is described by CPT data. When the values of q are small, the soil is considered soft. In soft soils the pile end bearing is low, and the pile behaves like a friction pile. Hence the values of R r /R determined are large. When 3 the values of q are large, the soil layer is considered hard. In hard soils the pile end Chapter 6. Drivability Analysis - Blow Count Prediction 76 bearing is high, and the pile behaves like an end bearing pile. The values of then small. The values of qea normally provide reasonable predictions of RSD/RUt are However, in some instances values of 3 R / R deviate significantly from what is expected. This happens especially when soil layers changed from soft to hard or from hard to soft. In these situations the q is influenced by the values of q for different soils, as shown in Figure 6.17 in which the points 1, 2 and 3 correspond to the points in Figure 6.9 and 6.10. Because of the high values of q at a depth of 91 feet, a low value of Rsr/Rut is expected, however, a very high value of Rgr/Rut is calculated, as shown in Figure 6.9 point 1. The reason is that the value of ea is influenced by hard soil layer, but the pile tip is embedded in a soft soil layer. The contrary phenomena can be seen in Figure 6.10, where points 2 and 3 show low values of Rsr/Rut which normally correspond to high values of qea at depth of 52 and 53 feet. In fact the values of q are less than 80 bar. In this situation, the soil layers changed from soft to hard. The pile tip had embedded in hard soil layer, but the values of qea were influenced by the soft soil layer above the tip. In order to make better prediction, a comparison of blow count has to be made when the pile tip locates in both the upper and lower soil layers in the boundary zone where soil types change. Pile Embedded Depth and Pile Scale From the results, the ratio of 3 R r /Rut reflect the pile resistance conditions, i.e. friction pile or end bearing pile. However this ratio only make sense after a certain depth, defined as effective beginning depth. The values of Rsr/Rt are not reliable above the effective beginning depth since shaft resistance is not large enough. For example, a very low value of Rsr/Rt, 18 percent was determined corresponding to a value of 99 bars qea at a depth of 3 feet for UBC pile 5. The effective beginning depth is affected by the pile size because the shaft resistance increases with increasing pile size. For example, the effective beginning depth is 5 feet for UBC pile 6, and 10 feet for UBC pile 5. Chapter 6. Drivability Analysis - Blow Count Prediction 60 0 I 0— ._L...L.._I. Qo (bors) 100 I,iiii I 77 200 150 111111 III Pt2&3 = 120 I I— I I I I I I I I 100 Rsr/Rut (%) Figure 6.17: Influencyof Soil Profile Change to the /R 27 R Chapter 6. Drivability Analysis - Blow Count Prediction 78 The value of Rsr/Rut tends to be constant with increasing depth when qea is greater than 110 bars, as shown in Figure 6.12 on Tilbury pile 1. From Figure 6.12, Rsr/Rut tends towards a constant value of 52 percent below the depth of 70 feet while ea changes from 110 to 160 bars. This means that shaft resistance increases with increasing depth at the same rate as increases. If qea is the same at different depths, then Rsr/Rut will increase with increasing depth. Therefore, higher values of R 3 /R have to be selected corresponding to the same qea value when a pile is driven deeper. observed at shallower depths for large piles since This tendency is increases with increasing pile size. Figure 6.18 shows that the average value of Rsr/Rut increases from 47 to 52 percent while the depth increases from 55 to 90 feet. It is important to understand what depths are likely to show increase in caused solely by further increase in embedded depth. An adjustment is proposed based on statistical analysis in the following chapter. Soil Plug From the test records, the measured blow counts of UBC pile 4 are nearly the same as measured blow counts of other UBC piles when the piles were driven in cohesive soils above 50 feet. While the open-ended pile has a small end bearing compared to the closed-ended pile, the may be nearly same for both kinds of piles because the low end bearing of the open-ended pile is balanced by the increased friction between soil plug and pile interface. When the tip of UBC pile 4 was driven in sand, relatively low blow counts were measured compared to the measured blow counts of the closed-ended pile of the same size, and the of open-ended pile was smaller than the of the closed ended pile. The record shows that relative movement between soil plug and pile did take place during pile driving. Table 6.4 gives a comparison of blow counts at normalized hammer energies for TJBC pile 4 and pile 5. There were some difference in length and wall thickness between pile 4 and pile 5, but, from preliminary analysis, these difference Chapter 6. Drivability Analysis 0 10 20 50— - Blow Count Prediction RaUo of Rf/Rut 30 40 50 60 I I I I 70 I 80 I 90 0 0 = () 79 D 60- 65- >00 IZI 0 0 0 0 70 0 0 C 0 * 0 - 0 85- 0 0 C 0 90— TILBURY PILE 1 95 — 100— 00000 qco CODOC qca >>>>> qca X**qca qca = = = = = 110 120 130 140 150 to to to to to 120 130 1 40 150 1 60 bars bars bars bars bars - Figure 6.18: Depth Influence to Rar/Rut 100 Chapter 6. Drivability Analysis - Blow Count Prediction 80 Table 6.4: Blow Counts Comparison For UBC pile 4 and 5 Depth (feet) 48 50 55 60 65 68 71 76 Pile 4 Energy Used in Pile 5 2.6 3.4 6.2 5.3 5.2 8.7 7.8 9.4 Measured 3 4 7 6 6 10 9 10 should not affect the results significantly. The ratio of R r/ 3 Pile 5 Measured 3 5 7 6 10 9 10 17 is higher for open-ended pile than that for closed-ended pile at the same pile size. An average value of 5 percent is suggested to be added to Rsr/Rt for open-ended pile based on same size closed-ended pile. When the soil inside the pile is fully compacted, the length of soil plug in the pile will no longer increase. In this case the end bearing for both closed and open ended piles may be similar and an may be determined by using the same value of Rsr/Rt. In the GRLWEAP program, when modelling the pile with soil plug, it is assumed that the soil has negligible stiffness compared to the pile. Thus, the pile area and modulus specified described the pile stiffness, and the specific weight of the pile reflects the combined pile and soil properties. A non-uniform pile model was used in the analysis. To do more accurate analysis, a model that considers friction between the soil plug and the pile may be necessary, such as in the model proposed by Heerema and de Jong (1979). Chapter 6. Drivability Analysis 6.4.5 - Blow Count Prediction 81 Dynamic Effects during Driving The CPT is a quasi-static penetration test yet the analysis of pile driving is a dynamic problem. The fact that the LCPC CPT method is very good at predicting pile capacity is a result of the fact that the CPT measures parameters that are more relevant to soil failure during static loading, such as during a pile load test. Since pile driving is a dynamic process it is not too surprising that in some cases measured static parameters do not apply while static and dynamic resistance of soil are related, the relationship can not be expressed simply. An example of this problem is the comparison of SPT N to q, carried out by Roberson and Campanella (1986). The authors showed clearly that the relationship was a function of grain size and the relationship is non-linear. The dynamic effects caused by driving must be considered in determining from qea, especially for cohesionless soils. Since the grain size of soil is relatively small compared to the size of the pile the soil is usually compacted by the displacement and vibration during driving. Detailed investigation of extent of compaction of sand and the increase in relative density around the pile have been carried out by Meyerhof (1959) and Robirisky and Morrison (1964). The tests of Robinsky and Morrison showed that the process of sand displacement and compaction below a pile tip is followed by sand movement adjacent to the pile sides. These movements tend to decrease the sand density in the immediate vicinity of the sides and thus nullify some of the benefits gained by the primary compaction. The pattern of displacements around a typical pile is shown in Figure 6.19. From the results of Tilbury pile 2, the values of Rsr/Rut scatter below the linear best fit line as shown in Figure 6.13. It seems that the best fit line under-estimated the values of RSr/RUt for Tilbury pile 2 more than for the UBC piles. The reason for this is that the q values increase with increasing density of sand in Tilbury site below 14 feet. The values of ea are lower in loose sand than in dense sand. However the soil resistance Chapter 6. Drivabilitv Analysis - 82 Blow Count Prediction Figure 6.19: Displacements around Driving Pile in Sand (after Robinsky and Morrison, 1964) increases because of the increase of relative density caused by the driving. Therefore the calculated values of R,r/R.jt in loose sand corresponds to slightly lower values of qea More research on this problem is required to better understand the limitation of the CPT. Correction for Diesel Hammer Blow Rate In the analysis of Tilbury piles, the blow counts as well as hammer blow rate were recorded for the D30-13 diesel hammer. The hammer blow rate is related to the hammer energy and soil resistance during pile driving. The higher the hammer blow rate, the lower is the energy delivered to the pile. Figure 6.20 shows the working principle of a liquid injection open end diesel hammer. . The hammer energy changes with combustion chamber pressure. If the fuel is adjusted Chapter 6. Drivability Analysis - Blow Count Prediction 83 to a lower level, the combustion chamber pressure is lower. Also, the hammer rebound will be lower resulting in higher hammer blow rate. If the is higher, the hammer rebound will be higher which results in the lower hammer blow rate. In order to consider driveability when using a diesel hammer, the hammer blow rate is always recorded along with blow count. The different blow counts can be observed corresponding to different hammer energy delivered to the pile, however the ratio of 8 R / R is the same for a specified depth. In order to make comparison between predicted and measured blow count, adjustment of blow count according to hammer blow rate is proposed for diesel hammer. Figure 6.21 shows the blow counts change with hammer blow rate for different values of When is lower, the change in hammer blow rate does not cause significant change in blow counts, However, when in large change in measured blow counts. is higher, changes in hammer blow rate result This can be explained by Figure 6.23 and also by the bearing graph Figure 6.22. By setting combustion chamber pressure, the input hammer energy is fixed. Then the hammer blow rate only changes with as shown by the family of curves in Figure 6.23. The hammer blow rate decreases with increasing R, but tends to a constant value when increases. This is because ram rebound increases with increasing resistance towards an upper limit. When is large enough the gravity energy of ram rebound balances the maximum energy provided by the explosion of combustion chamber pressure. The height of the ram rebound remain constant and the hammer blow rate become constant as well. The combustion chamber pressure provided the hammer energy. The higher the combustion chamber pressure, the higher the hammer energy. The higher the ram rebound, the lower the hammer blow rate. Figure 6.23 shows the hammer blow rate decreases with increasing energy and it also shows that the values of that cause ram rebound to become constant increase with increasing energy. Before a constant hammer blow rate is reached, the blow counts Chapter 6. Drivability Analysis - Blow Count Prediction 84 CYLZN D ER ZR M USt AMBER HAMME GUS HIC B A A) B) C) D) C D FREE FALL & FUEL INJECTiON IMPACT & IGNITION EXHAUST SCAVAGING Figure 6.20: Working Principle of a Liquid Injection Open End Diesel Hammer (adapted from GRLWEAP Menu) Chapter 6. Drivabiiity Analysis Blow Count Prediction - 85 50: 48 46 C E 45- I 44- Diesel Hammer D30—13 ocCo A - DO - 43 2 **** - -_ — 42= - I! I “ I IT’ - 41 00000 *ev. = 4011 0 I i_i 50 100 - I II Rut Rut Rut Rut Rut Rut ut Rut Rut Rut 111111 200 50 Blow Counts (bpf) 250 = = = = = = = = = 250 300 350 400 450 500 550 600650 700 11111 300 350 Figure 6.21: Influence of Diesel Hammer Blow Rate to the Pile Blow Counts with Con stant R, Chapter 6. Drivability Analysis Blow Count Prediction - 86 800 600 (/) - - 400 - PERCS=45% 200— I_ I I I I 100 O I I I 300 200 Blow Counts I I 400 (bpf) 500 600 Figure 6.20: Eearin(Graphof Tilbury Pile 2 at Depth of 56 feet 50 Rut 49 = 250 kips 305Q 48Qo • -D , -j 46- 0:: 0 45- L 0 - 42 41 4 - Diesel Hammer D30—13 - 4.0] 0 i I JI 100 50 - I liii 150 200 Blow Counts (bpf) I 250 300 350 Figure 6.23: Influence of Diesel Hammer Rate with Constant Energy to the Pile Blow Counts Chapter 6. Drivability Analysis increase slowly with increasing - Blow Count Prediction 87 After a maximum hammer rate is reached, the blow counts increase faster with increasing This explains the nonlinear characteristic of versus blow counts shown in Figure 6.22. When R, is lower, the hammer energy does not operate efficiently because the ram rebound is low. When a constant cam rebound is reached, corresponding to a value of value of the hammer energy is at its maximum. The at this stage is noted as Rutm. If R. is smaller than Rutm, the energy transmitted to the pile will increase with increasing due to increasing ram rebound. The energy needed to cause pile penetration is provided by the hammer. As the hammer blow rate increases, the blow count increases slowly. If is larger than Rutm, the energy transmitted to the pile can not increase because the energy is at its maximum. In this case the hammer blow rate becomes constant, but blow count increases significantly, as a small increase in causes a notably increase in blow counts. In Figure 6.21, point 1 shows the blow counts of 33 blows/foot with hammer rate of 42 blows/mm for Tilbury pile 2. Point 2 shows the blow counts of 38 blows/foot with hammer rate of 43 blows/mm of 57 feet where for Tilbury pile 1. Both piles were embedded at a depth was predicted at 459 kips. Thus a great difference in blow count may be caused by a small difference in hammer blow rate. If a diesel hammer is used when predicting blow counts, the predicted blow count must be presented with hammer blow rate. It is suggested that for every types of diesel hammers a figure like Figure 6.21 should be developed. 6.5 Conclusion Three assumptions based on pile capacity determined from CPT were made to establish the empirical correlation between and q, in order to predict the blow counts of driven piles. From the results, the assumption based on shaft resistance component was selected, Chapter 6. Drivability Analysis - Blow Count Prediction and empirical correlations between Rsr/Rut and qea 88 were found for steel pipe piles and H piles. The ratio of Rsr/Rut is considered to reflect the resistance condition of pile embedded in different soil layers. The values of ea determined from CPT q are affected by the size of pile. The main conclusions are as follows: (1) The ultimate resistance R,.Lt during pile driving is smaller than pile capacity deter mined from CPT. A reduction must be properly selected in order to model soil resistance condition during pile driving. The reduction factor is a function of soil type. (2) The ratio of Rsr/Rt reflects pile resistance conditions, i.e. shaft resistance or end bearing pile. qea, can be evaluated through establishing correlation between 3 R / R and where q. reflect soil strength. (3) For the same kind of piles, the correlations between Rsr/Rt and show very similar results. The correlations are independent of driving system and are affected by the soil strength, pile embedded depth and pile size. (4) For the different types of piles, the correlations between Rsr/Rt and q are very different. The difference are related to the pile resistance distribution. (5) There is potential to use the method proposed in this study to predict blow count of driven concrete and timber piles. But, it is necessary to determine reasonable reduction factor and establish the correlation analogous to those established in this study. Chapter 7 Statistical Analysis and Criteria for Blow Count Prediction 7.1 Introduction The main purpose of predicting blow counts of a driven pile is to ensure good performance of the hammer-pile-soil system. Good performance should be defined realistically in terms of error limits with a given confidence level. The predicted blow counts change with the ultimate resistance which can be determined from q, and Rsr. Due to the non-linear nature of the bearing graph, as shown in Figure 6.22, at lower levels of causes a small change in blow count. But, at higher levels of change of a change of even a very small will, cause a big change in blow count. In Chapter 6, it was observed that points of computed 3 R r /Rut were scattered in a band. The band width and slope was different for different types of piles and different magnitude of qea. The values of determined from the 3 R r /Rut versus error in ea figures depend on the slope of the bands. Any will result in an error in predicted blow count, especially at high level. Statistical analysis allows a better understanding of limitation of the proposed method and gives an indication of the reliability in applying the proposed method. Statistical analysis can be used to draw information about a sample, and thus estimate values that help characterize the population from which the sample was chosen. Through statistical analysis, a confidence interval for an expected value can be determined to indicate the reliability of the result. Because of the limited availability of data, we would choose one pile sample to do a single pile statistical analysis. The statistical analysis 89 Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 90 performed herein will consider UBC pile 5 and 6, Tilbury pile 2 and Tilbury H12x53 pile. 7.2 Linear Regression Analysis In order to describe a correlation between two variables, the analysis must combine the distributions of all the measured variables with respect to a particular reference to reach the accuracy with which some variables can be determined from the others. In the drivability analysis, the measured parameter proposed that the computed value qea RSr/RUt qea is an independent variable. It is is a dependent variable. The extent to which controls the computed values of R r /Rt will determine the correlation coefficient. 8 A linear correlation is assumed between 3 R r /Rut and q€ based on the results of Chapter 6. The following assumptions are made in proposing a straight line regression through the data. 1. The qea values based on q are error free. 2. The regression of Rsr/Rt and 3. The deviations Y — ea is linear. E(Y(X) are mutually independent, where x is q and y is Rsr/Rut. 4. These deviations have the same variance (2, not usually known exactly). 5. These deviations are normally distributed. Based on above assumptions the regression straight line can be described by Equa tion 7.1 [44]. = a + bx + If the estimators of a and b is , - ) 2 N(0, a and b, the estimator of y is (7.1) . The equation = a + bx is called a regression function. The Least Square Method is used to estimate a and b [44]. The results of the linear Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 91 Table 7.1: Regression Analysis Results Pile UBC P5 UBC P6 TIL P2 TIL HP a 92.216 97.195 91.134 101.570 -0.3622 -0.3917 -0.3597 -0.1527 8.7457 8.7178 9.2687 5.6972 fir a/2 a/2 tj 0 e,2 Figure 7.1: Probability of Student Distribution regression for different piles are given in Table 7.1. The unbiased estimator of & of the maximum likelihood estimator, o are determined [44] and given in Table 7.1 also. The figures of Rgr/Rt versus qea br the same type of piles. 7.3 in Chapter 6 show that the linear regression lines agree well - - - Confidence Interval Analysis In the regression analysis, y 1 was assumed to be normally distributed for each fixed z. Supposing, it is necessary to know a 100(1-a) percent confidence interval of y for z = . 0 x Thàt probability of 100(1-c) percent confidence interval is defined the white area, as shown in Figure 7.1 144]. The mathematic details of confidence interval are presented in Appendix B [44]. Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 92 110- - — 100- 20- ‘•:........ ‘10- - — I I I 0 60 0 20 I I 80 100 qea (bars) I 120 I 140 I 160 180 Figure 7.2: Bands of Different Confident Interval for UBC Pile 5 110 100 70 °‘ - oo 70 90 20 10I 0 0 II 20 III I 40 1.111111 60 100 80 qea (bars) II III 120 III 140 II 160 Figure 7.3: Bands of Different Confident Interval- for UBC Pile 6 180 Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 93 10 9Q -fl 0 40 20 60 100 80 qea (bars) 120 140 160 180 Figure 7.4: Bands of Different Confident Interval for Tilbury Pile 2 1D - 0 20 - 40 60 ‘100 80 (bars qea ) 120 140 160 180 Figure 7.5: Bands of Different Confident Interval for Tilbury H12x53 Pile Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 94 The calculated results of yo for selected values of x , together with the lower and 0 upper bounds for different 100(1-a) percent confidence interval, on UBC pile 5 and 6, Tilbury pile 2 and H12x53 pile are shown in Figures 7.2 to 7.5. 7.4 Criteria Study of Predicted Blow Counts Based on Statistical Analysis 7.4.1 Outline From the results of statistical analysis, lower and upper bounds are determined according to different confidence levels. For a given value of q and 3 R r , the lower bound relates to a lower value of RSr/RUt which give a larger value of and the upper bound relates to a higher value of 3 R r /Rt which give a smaller value of count increases with increasing The predicted blow if other parameters remain constant. The range of predicted blow counts corresponds to lower and upper bounds of depends on the various confidence levels. In order to keep the error of predicted blow counts as low as possible, different levels of confidence have to be taken into account for different value ranges of qea Due to the nonlinear relationship between in Figure 6.22, when the and blow counts, as shown value is high, a small error in estimated can result in a very large difference in the predicted blow counts. An error study of predicted blow counts based on the regression and confidence analysis results was made on UBC piles 5 and 6, and the Tilbury H pile. The purpose of this section is to examine the change of predicted blow counts with a defined confidence and to give a reliable lower and upper bound in determining 7.4.2 The results of this study are given in Appendix C. Steel Pipe Pile Figures 7.6 and 7.7 show the results of predicted blow counts using the values at re gression line, lower and upper bounds with different confidences for UBC piles 5 and 6. 0 -c 1 20 100 80 60 ‘ 0 - q’ Ir 9I * *-4-4-# * 4-*-#* o -e--o = I 90 % Measured Reg. Line Lower Bound Upper Bound Confidence I Blow Count (blow/foot) 50 100 1 50 0 II) Cl -1z 120 100 80 60 40 20 0 Blow Count (blow/foot) Figure 7.7: Blow Counts Preáicted from Regression Line, Lower and Upper bounds with Different Confident Interval for UBC Pile 5 120- 80— 60- 40- 20 20 40 0 0 Qea (bars) C;’ 0 C.) 0. I, 0 C) 0 ‘1 I I 90 % 120 120 100 60 100 -4-, -4-, 40 20 C 8I - oo000 Measured •-..-. Reg. Line **-*-4-4e Lower Bound *4-*-4-4 Upper Bound Confidence 80 80 60 40 20 0 Blow Count (blow/foot) 40 0 *4-*-*-4 **-*-* oooo = 70 Measured Req. Line Lower Bound Upper Bound Cqnfidence Blow Count (blow/foot) 20 40 60 Figure 7.8: Blow Counts Predicted from Regression Line, Lower and Upper bounds with Different Confident Interval for UBC Pile 6 0 0 120 -c r Qea (bars) 80 80 C,, 1 C,, C, j) Cl) Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 97 The values of q and measured blow counts are shown as well. The following tends are observed based on these figures. When the value of ea is smaller than 30 bars, the value of Rgr /R is larger than 70 percent. The pile behaves as a friction pile and the blow count calculated from on the linear regression line agrees well with the measured blow count. Up to 30 percent error can be expected based on lower and upper bound corresponding to the 90 percent confidence, for predicted blow counts. 30 percent error is acceptable at relatively lower levels of blow count. For example, for the results from 102 feet depth of pile 6, the predicted blow counts based on lower and upper bound are 32.4 and 21.7, respectively, while the measured blow count is 25. If a lower confidence is used, a more reliable range of blow counts can be predicted. In comparing the results for the pile tip embedded in soft soils at different depths, it was shown that the errors of predicted blow count are not influenced by increasing depth, and the blow count only increases with increasing shaft resistance for friction pile condition. When the values of qea are from 30 to 100 bars, the values of R r /R change from 3 about 80 to 60 percent, as shown in Figures 7.6 and 7.7. This happens when pile tip is embedded in depths of 60 and 62 foot for UBC pile 5, and in depths of 56, 61, and 82 feet for UBC pile 6. In this range, the calculated blow count based on linear regression line agrees well with measured blow count. The errors of predicted blow counts based on lower and upper bound are small, except that predicted lower blow counts of 60 foot depth of UBC pile 6 has an error of 28 percent. It seems that a better way to predict the blow counts is based on the range between regression line and lower bound. When the values of q are between 100 and 120 bars, the values of 5 R / R are about 54 percent and the pile behaves as an end bearing pile. The calculated blow counts based on the linear regression line agrees well with the measured blow counts. Since the values of are higher, a small change in will result in a large change in blow count. Even Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction though the percentage of error may be the same as for lower values of 98 q, there are some difference between predicted blow count and measured blow count, depending on if the prediction is based on lower or upper bound. A very large error can be made when predicting blow count using lower bound, especially for deep piles. For example, the error of blow count predicted with 90 percent confidence for UBC pile 6 at 80 feet is 83 percent with predicted blow count of 53 comparing to measured blow count of 29. However, the error of blow count calculated from the upper bound is smaller. The above phenomenon can be explained by the depth and pile size influence which result in increasing shaft resistance with increasing depth. The increase of shaft resistance with depth causes the values of RSr/RUt to be higher and the values of R r/Rt determined from lower bound 3 to be lower. This can be seen by comparing pile 5 and pile 6. Since the size of pile 5 with a diameter of 12.75 in. is smaller than that of pile 6 with a diameter of 24 in., the Rsr of pile 6 is about twice the R r of pile 5 at the same depth. For pile 5 at a depth 8 of 88 feet, the blow count calculated from lower bound is 29.1 comparing to measured blow count of 20, and for pile 6 at a depth of 89 feet, the predicted blow count from lower bound is 134.5 comparing to measured blow count of 43, with a confidence of 90 percent. It is recommended that at higher values of qea, the lower bound is not reliable to predict blow count, especially for large size piles embedded in deep pile condition. The band between the upper bound and linear regression line with 70 percent confidence is suggested to predict blow counts. When the values of q are between 120 bars and 150 bars, the predicted blow counts appear to have bigger errors, with a maximum error of up to 213 percent, from lower bound with a 90 percent confidence. At the same time, the blow count predicted based on linear regression line is higher than the measured blow counts for deep piles like pile 5 at a depth of 90 feet and pile 6 at a depth of 89 feet. Obviously, shaft resistance increases with increasing depths. From the analysis results, it can be concluded that Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction upper bound gives more reliable 99 values than lower bound even with a confidence of 50 percent in predicting blow counts. This shows that the assumption of linear regression is no longer applicable at higher levels of qea, especially for large size piles. However, a combination of upper bound of 80 percent confidence with linear regression line can be used to predict the blow count for the piles of small size. A combination of upper bound of 70 percent confidence with linear regression line can be used to predict blow counts for the piles of large size. In this range of qea, the linear regression line can be considered as lower bound and the values of 3 R / R in the range between regression line and upper bound can be used to predict blow counts. 7.4.3 H Piles Figure 7.8 shows the predicted blow counts for the Tilbury H pile. As shown in Figure 7.8 the linear regression line is the characteristic of a shaft resistance pile. The values of Rsr/Rut are larger than 75 percent when the values of are up to 170 bars. From Figure 7.8, the predicted blow counts based on lower bound overestimated depths even though the values of qea in all are higher than 160 bars. The error increases with increasing depth. This results from increasing shaft resistance with lower end bearing at depths. The predicted blow count based on regression line with upper bound represents a reliable interval when compared to measured blow counts. When the values of q are lower than 30 bars, a value of 100 percent for Rsr/Rt can used to estimate confidence interval of one o, or about 70 percent, can be used to predicted the band of the regression line and upper bound. A within - - 100- 80 60- 40 20 0 III 40 120 200 - - 100— 80 60- 4c 20 0 0 90 % \ Measured ••-- Req. Line **-*-* Lower Bound *-*-*- Upper Bound G-G-G-e-o Confidence Blow Count (blow/foot) 50 100 150 100- 80- 60- 0. -I-. .1:: - 40- 20 0- I i DilCerciit Cotitident Interval fur ‘i’iLbtiry 11 Pile 70 % ii *1 Measured ••-.. Req. Line 4-*-9-4-4 Lower Bound -*-4-* Upper Bound oo000 = ii Confidence iii Blow Count (blow/foot) 0 50 150 100 Figure 7.9: Blow Coituts Predicted Ironi I{egrcssun Line, Lower and Upper bounds with II 160 1111.111.11 80 Qea (bars) I 0 I C. C. Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 7.5 101 Conclusion The above analysis is only based on limited data. More analysis is recommended to do for piles at different sites. Some conclusions based on statistical analysis are summarized as follows. For steel pipe piles: (1) In predicting blow count of friction pile, which values of RSr/RUt are larger than 75 percent, a confident interval of 90 percent can present reliable (2) When ea value. is smaller than 30 bars the linear regression line can only be used to predict blow count. (3) When qea is between 30 and 80 bars the lower bound can be used to determine especially in the case of pile is embedded in loose sand. (4) When ea is between 80 bars and 150 bars, the lower bound should not be used to predict blow count if the pile tip is embedded in a depth more than 80 feet, due to the fact that increasing depth causes increasing shaft resistance. especially for large size piles. (5) When q is between 100 to 150 bars, the upper bound with 70 percent confidence can be used with linear regression line to predict blow count. (6) When is larger than 150 bars, the upper bound along with the linear regression line can be used to predict blow counts for small size piles. When a large size pile is embedded in depth over 100 feet, the upper bound with a correction of depth can be used to predict blow count, but in this case the linear regression line is no longer suitable. Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 102 110 100 80 —- 70 0 60 D cc - Co cc 50 40 Pipe Pile 30 20 H Pile 10 Number refer to pile embedded depth 0 0 20 40 60 80 100 120 140 160 180 q ea (bars) 1) When q is lower than 40 bars a value of 90 to 100 percent Rsr/Rut can be used to estimate (2) When q. is higher than 40 bars the linear regression line with upper bound can be used to determine R. in predicting the blow counts. In Figure 7.9, each different band with correction depth was suggested to determine for different types of piles. Chapter 8 Application 8.1 Outline In this chapter, a case history of using the method proposed in previous chapters to predict blow count of driven pile is presented to show the applicability of this method in practice. This method is based on CPT q and shaft resistance determined from LCPC CPT method. to determine Figure 7.10 derived based on statistical analysis in Chapter 7 is used with 70 percent confidence. The prediction was performed based on Tilbury pile 3 which is a closed ended steel pipe pile. The details of the soil conditions have been presented in previous chapters. Comparison between predicted and measured blow count were made to show the accuracy of the predicted results. A sample of input and output data is presented in Appendix D. 8.2 Predicting Steps The predicting steps are as follows: (1) Calculate average q for each one foot depth from data file of q measured every inch by using CPTINT program and calculated qea; (2) Predict pile capacity, shaft resistance by using LCPC CPT method; (3) Determine shaft resistance during pile driving, 5 R r , by introducing reduction factors, 0.7 for sand and 0.5 for clay, into shaft resistance part of pile capacity; (4) Calculate Rjr/Rt based on Figure 7.10 recommended in Chapter 7; 103 Chapter 8. Application (5) Determine (6) Input 104 with known q, and R ; 8 with soil, hammer and pile parameters into GRLWEAP program to calculate blow counts for each one foot depth until final pile penetration. 8.3 Results The results based on above prediction steps are shown in Figure 8.1 with qea, Rsr, and predicted blow counts. For each depth the is used in prediction. Parameters, such as soil quake and damping, and hammer efficiency, were taken from the GRLWEAP menu as recommended for the D30-l3 diesel hammer, type of pile and soil conditions of Tilbury Site. According to the record given in Appendix A, the hammer fuel value was adjusted to minimum to give lowest combustion chamber pressure. Therefore, the hammer fuel setting option, IFUEL was set to 4 corresponding to the minimum pressure value of 914 psi. The hammer stroke option IOSTR was set to 0 or 1 to make the calculated hammer blow rate at a given depth the same as that recorded during pile driving. Since the recorded hammer blow rates are not available for every depths, an average hammer blow rate between two recorded hammer blow rates is used at the depth where no hammer blow rate was recorded. The predicted blow counts are presented in Figure 8.2 with measured blow counts during pile driving. 8.4 Comparison between Predicted and Measured Blow Counts The predicted blow counts agree well with the measured blow counts when the pile tip was embedded in depths above 21 feet. Soils above 21 feet change from clayey silt to sand and silt. Lower blow counts were measured during pile driving because pile was U a. 4- 100 20 Step 1 Step 4 Rsr/Rut (%) 1 50 Step S Rut U Ips) 200 Figure 8. I: Procedure for Blow Count Prediction St,ep 2&3 Rsr U tps) 160 Step 6 B C (blows/foot) 40 60 20 01 0 106 Chapter 8. Application io o ‘—, - . . • Blow Count (blows/foot) 50 20 40 30 e I _.o I I I I I 60 I - -• -: - Measured •••‘• Predicted o-o-eeo • 20• - •. ;• .• -0 50: 80 - 90- Figure 8.2: Predicted and Measured Blow Counts for Tilbury Pile 3 Chapter 8. Application 107 Table 8.1: Predicted and Measured Blow Counts at the Same Hammer Blow Rate Depth Blow Counts (blows/foot) Hammer Blow Rate (feet) Predicted Measured (blows/mm) 52 26 28 54 23 59 25 51 66 36 31 51 72 35 30 50 40 78 35 50 ENTHRU stands for transferred energy ENTHRU (kip ft) 17.8 22.3 22.4 22.2 21.3 — embedded in soft soil where q, values are low. The same lower blow counts are predicted in terms of lower values determined from lower q values. From depth of 22 to 42 feet, the soil is sand with density increasing from medium loose to dense with depth. The predicted blow counts are much lower compared to the measured blow counts, even though larger bound in Figure 7.10. values were determined from the lower This means that the values of were under-estimated. Q values are affected by soil density with q,,, increasing with increasing density. In loose sand, q values are lower because of low density however this density will increase during pile driving because the soil is compacted by displacement and vibration. lower Therefore, values are determined by using the values of qea However, the prediction can be used as a reference, even though the error is high, because the expected blow counts is limited to a lower level that will not cause refusal in pile driving. The highest error was made when the recorded blow counts had values smaller than 30 blows/foot. Over depths of 42 feet, the sand is dense. The predicted blow counts agree well with measured blow counts except slight over-predicting at depths from 65 to 68 feet. The determined by using q is more reliable in dense sand than in loose sand. Table 8.1 gives comparison between predicted and measured blow counts at the same hammer blow Chapter 8. Application 108 rate. From this table it is clear that for a fixed hammer blow rate at depth greater than 50 feet, accurate prediction can be made. 8.5 Conclusion The above results show that reasonable predictions can be made using the method de scribed, especially in cases where high blow counts may be expected. The prediction is affected by the soil density. A useful evaluation of the drivability prediction can be made using this method before pile driving. Chapter 9 Summary and Conclusion The main purpose of this study was to examine methods of using CPT in conjunction with the one-dimensional wave equation program to predict blow counts of driven pile. Three sites were included in this study: UBC Pile Research Site, Tilbury Island Site and Evanston Campus of Northwestern University (ECNU) Site. The pile types include steel pipe piles, both closed and open ended, and H piles. An empirical correlation was established to estimate the driving toe resistance directly from the CPT q. The shaft resistances during driving were estimated from static long term pile resistances evaluated from the LCPC CPT method and multiplied by a set of reduction factors. Reduction factors, 0.5 for clay and 0.7 for sand, were used. The driving resistances were input into a commonly used program, GRLWEAP, to calculated blow counts for a given hammer-pile-soil system. The proposed correlation of CPT to pile toe resistance during driving depends on soil density as characterized by CPT q values, pile embedded length and pile size. A statisti cal analysis was conducted to evaluate the confidence limits of the proposed correlation. A case history was presented to show the application of the proposed method. The results show reasonable prediction of blow counts, The results of wave equation analysis are sensitive to the input hammer efficiency and soil quake and damping. Average values of observed hammer efficiency , and typical values of soil quake and damping based on predominant soil type are compiled by the GRLWEAP menu used in this study. 109 Chapter 9. Summary and Conclusion 110 It is recommended that further correlation studies include dynamic measurement of pile driving to directly determine the transmitted energy into the pile and to allow back calculation of the soil parameters. Bibliography [1] Aim, T., Bye, A. and Kvalstad, T. J. (1989), “A New Interpretation of Soil Resis tance for Pile Drivability Analysis”, Proceedings of the 12th International Confer ence on Soil Mechanics and Foundation Engineering, Rio de Janeiro, 1989, Vol. 2, pp. 1085-1088. [2] Aurora, R. P. (1980), “Case Studies of Pile Set-Up in the Gulf of Mexico”, Pro ceedings of 12th Offshore Technology Conference, Houston, Texas, USA, (1980), Vol.3, pp. 281-290. [3] Begnemann, H. K. (1963), “The Use of the Static Soil Penetrometer in Holland”, New Zealand Engineering Fey. [4] Blunden, R. H. (1975), “Urban Geology of Richmond, British Columbia”, Adven tures in Earth Sciences Series No. 15, B. C. Govt. Publications. [5] Burland, J. B. (1973), “Shaft Friction of Piles in Clay Approach”, Ground Engineering, Vol.6, No.3, pp. 30-42. - A Simple Fundamental [6] Bustamante, M. and Gianeselli, L. (1982), “Pile Bearing Capacity Prediction by Means of Static Penetrometer CPT” Penetrating Testing, ESOPT II, Amsterdam, Vol.2, . 500 493 pp. [7] Campanella, R. G. and Sy, A., Davies, M. P. and Robertson, P. K., Davies, M. P., (1989), “Class a Prediction of Driven Piles Behaviour”, Soil Mechanics Series, No. 135, Department of Civil Engineering, The University of British Columbia, Vancouver, Canada. [8] Chow, Y. K., Wong, K. Y., Karunaratne, G. P. and Lee, S. L. (1988), “Wave Equation Analysis of Piles A Rational Theoretical Approach”, Third International Conference on Application of Stress-Wave Theory to Piles, Ottawa, Canada, 1988, pp. 208-218. - [9] Davies, M. P. (1987), “Prediction Axially and Laterally Loaded Pile Behaviour Using In-situ Testing Method”, M.A.Sc. Thesis, Department of Civil Engineering, The University of British Columbia, Vancouver, Canada. [10] Davisson, M. T. (1973), “High Capacity Piles”, Proceedings, Lecture Series, Inno vations in Foundation Construction, ASCE, Illinois Section, Chicago, USA. 111 Bibliography 112 [11] de Beer, E. E. (1963), “The Scale Effect in Transportation of the Results if Deep Sounding Tests on the Ultimate Bearing Capacity of Piles and Caisson Founda tions”, Geotechnique, Vol.8, p. . 39 [12] de Mello, V. F. B. (1969), “Foundation of Buildings on Clay”, State of the Art Report: 49 136 Proc. 7th Tnt. Conf. S.M. & F.E., Mexico City. - [13] de Ruiter, J. and Beringen, F. L. (1979), “Pile Foundations for large North Sea Structures”, Marine Geotechnology, Vol.3, No.4, pp. 267-314. [14] Dennis, N. D. and Olson, R. E. (1983a), “Axial Capacity of Steel Pipe Pile in Clay”, Amer. Soc. of Civil Engineers, Geotechnical Practice in Offshore Engineer ing, Austin, pp. 370-387. [15] Dennis, N. D. and Olson, R. E. (1983b), “Axial Capacity of Steel Pipe Pile in Sand”, Amer. Soc. of Civil Engineers, Geotechnical Practice in Offshore Engineer ing, Austin, pp. 389-402. [16] Dinesh Mohan and Virendra Kumar (1963), “Load Bearing Capacity of Piles”, Geotechnique Inter. J. Soil Mechanics Vol.13, 1 Mars. pp. 76-86. [17] Fenske, C. W. and Hirsch, T. J. (1986), “Pile Drivability Analysis”, Planning and Design of Fixed Offshore Platforms, Van Nostrand Reinhold Company, New York, 1986. [18] Finno, R. J. (1989), “Subsurface Conditions and Pile Installation Data: 1989 Foun dation Engineering Congress Test Section”, Geotechnical Special Publication No. 23, ASCE, New York, NY, USA. [19] Flaate, K. and Selnes, P. (1977), “Side Friction of Piles in Clay”, Proceedings of Ninth International Conference on Soil Mechanics and Foundation Engineering, Tokyo, Japan, Vol.1, pp. 517-522. [20] Fellenius, B. H., Riker, R. E., O’Brien, A. J. and Tracy, G. R. (1989), “Dynamic and Static Testing in Soil Exhibiting Set-up”, Journal of Geotechnical Engineering, ASCE, Vol.115, No.7, July 1989. [21] Goble, G. G. and Rausche, F. (1976), “Wave Equation Analysis of Pile Driving WEAP Program”, Vol.1 through 4, FHWA # IP-76-14.1 through # IP-76-14.4, July 1976. - [22] Goble, G. G. and Rausche, F. (1981), “Wave Equation Analysis of Pile Driving WEAP Program”, Vol.1 through 4, FHWA IP-76-14.1 through IP-76-14.4, Updated March 1981. - Bibliography 113 [23] Goble, G. G., Rausche, F. and Hery, P. (1983), “Colorado University Modified Wave Equation Analysis of Pile Driving CUWEAP Program”, Vol.2, Department of Civil, Environmental and Architectural Engineering, University of Colorado, June 1983. - [24] Goble, G. G., Rausche, F. and Likins, G. E. Jr. (1980), “The Analysis of Pile driving A State-of-the-Art”, Proc. Tnt. Seminer on Appl. of Stress-Wave Theory on Piles, Stockholm, pp. 131-150. - [25] Heerema, E. P. and de Jong, A. (1980), “An Advanced Wave Equation Computer Program which Simulates Dynamic pile Plugging through a Coupled Mass-spring System”, Proc. Tnt. Conf. on Numerical Methods in offshore Piling, London, ICE, pp. 37-42. [26] Heerema, E. P. (1980), “Predicting Pile Driveability: Heather as An Illustration of the ‘Friction Fatigue’ Theory”, Ground Engineering, April 1980, Vol.13, No.3. [27] Isaacs, D. V. (1931), “Reinforce Concrete Pile Formula”, Inst. Aust. Engineering Journal, Vol.12. [28] Janbu, N. (1976), “Static Bearing Capacity of Friction Piles”, Proceedings of the European Conference on Soil Mechanics and Foundation Engineering”, Vol.1.2, pp. 479-488. [29] Meyerhof, G. G. (1951), “The Ultimate Bearing Capacity of Foundations”, Geotechnique, Vol.2, No.4, p. . 301 [30] Meyerhof, G. G. (1959), “Penetration Tests and Bearing Capacity of Cohesionless Soils”, J.S.M.F.D. ASCE, Vol.82, SM1, pp. 1-19. [311 Meyerhof, G. 0. (1976), “Bearing Capacity and Settlement of Pile Foundations”, ASCE Journal of Geotechnique Engineering Division, Vol.102, No.GT3, pp. 197228. [32] Meyerhof, G. G. (1982), “Scale Effects of Ultimate Pile Capacity”, Journal of Geotechnical Engineering, ASCE, Vol.109, No.6, June 1982. [331 Middendorp, P. and Zandwijk, C. Van (1985), “Accuracy and Reliability of Dy namic Pile Testing Techniques”, Behaviour of Offshore Structures, Delft, 1985. [34] Nottingham, L. C. (1975), “Use of Quasi-Static Friction Cone Penetrometer Data to Predict Load Capacity of Displacement Piles”, Ph.D. Dissertation, Department of Civil Engineering, University of Florida, USA. Bibliography 114 [35] Robertson, P. K. and Campanella, R. G. (1986), “Guidelines for Use, Interpre tation and Application of the CPT and CPTU”, Soil Mechanics Series, No. 105, Department of Civil Engineering, The University of British Columbia, Vancouver, Canada. [36] Robertson, P. K., Davies, M. P. Campanella, R. G. and Sy, A. (1987), “Capac ity of Driven Piles in Deltaic Soils Using CPT”, Soil Mechanics Series, No. 113, Department of Civil Engineering, The University of British Columbia, Vancouver, Canada. [37] Robinsky, E. I. and Morrison, C. E. (1959), “Sand Displacement and Compaction Around Model Friction Piles”, Canada Geotechnical Journal, Vol.1 No.2, pp. 81. [38] Schmertmann, J. H. (1978), “Guidelines for Cone Penetration Test, Performance and Design”, Federal Highway Administration, Report FHWA-TS-78-209, Wash ington, July, pp. 145. [39] Smith, E. A. L. (1950), “Pile Driving Impact”, Proc. Industrial Computation Sem inar, Sept., International Business Mechanics Corp., New York, USA. [40] Smith, E. A. L. (1960), “Pile Driving Analysis by the Wave Equation”, Journal of the Soil Mechanics and Foundation Division, ASCE, Vol.86, No.EM4. [41] Soderberg, L. (1962a), “Consolation Theory Applied to Foundation Pile Time Ef fects”, Geotechnique, Vol.12. [42] Tang Nian-Ci, Yuan Ji-Hong, Wang Yong and Lu Tong-Shen (1988), “Drivability Analysis of Long Steel Pipe Piles Case History Studies”, Third International Conference on Application of Stress-Wave Theory to Piles, Ottawa, Canada, 1988, pp. 499-512. - [43] Vijayvergiya, V. A, and Focht, J. A. (1972), “A New Way to Predict Capacity of Piles in Clays”, Proc. Fourth Offshore Technology Conference, Houston, Vol.2, pp. 865-874. [44] Zhe Jang University (1979), “Probability and Mathematical Statistics”, Higher Education Publishing House, Beijing, China, 1979 (in Chinese). [45] Zhou, J., Xie, Y., Zuo, Z. S., Luo, U. Y. and Tang, X. J. (1982), “Prediction of Limit Load of Driven Pile by CPT”, Penetration Testing, Proc. 2nd, European Symp. Penetration Testing, ESOPT II, Amsterdam, Vol.2, . 961 957 pp. Appendix A Pile Driving Records 115 Appendix A. Pile Driving Records rj 1J6 0 V 2 0 2 z 0 C z a 0 z 4 — I 1. ‘L) N - a. — w - - 0 a. a—-— i — — -D — — 2 z EEEEE1ETEEEEEE 0 em Appendix A. Pile Driving Records 117 HLMt1i1I1i t1W.l II IWUH Nitt[[fl iiifl1t: LH u1 -d 0 a zww - z 0 I. I UI z UI UI -i 2 I 0 v —I )4—. .,..dp,, .‘‘,. rs I3 1 C (qf4 1 - 0 0.3 i I -, S 2’ a -t U 2 2 a 0 Uk ! H LH i:fl.iflTt. : :6z 1 ‘ I Ij j f * -- II C z v i 0 3 2 — I 1im d 3 : : 11::: WI 0 _: UI tO 1 ‘iIL C) I IUI z ! UI 0. .-4 ,. •0’ N NLI’t° UI 0 C-, -I V I 0. w 4 I.’ 2 •; : ) o ‘-I C — C .0 LLNL± 0 0 • - = HI := -C— z C 0 o o — > z C., ____ Appendix A. Pile Driving Records 118 4 0 4 t. Li’ {11III. iliti I H Iii1+1Lt 2 0 4 .1 us e 0 . ifl N -H±tt 11 1tMti i— z • 0 —--- 334 a ISO — 30 0. 2 Tii ;; — z .I. -v-•-L ; . — I -J . f.!! .!! . !. ! I , 0 LI) 4 S. 2 ‘ I. . a a-I- ñg ‘.4 - Ii .. -4 .: U•. [ t I” fi: It 1 N -4. j . :.:; us ‘I ii 0 ‘U .1 ‘3 II: II.’ L[ C z 1.7 ‘U to 1 0 r— - 04 00 - r. 00 C. I ‘ 3 3 s.d .5 — - fl9U I UI 3 — II r I Li .130 , I r-Li\s.p -s —r 9. .0 . UI -J T 0 : I S ‘ -t - I-I I:I C 4 WI >1.I —1. 1.. 0< 3 : :: : :: Jz: I .7 Appendix A. Pile Driving Records 119 HFiIEf iLlJ Ii .i .4 ‘U -J ‘C ‘3 ‘C 1:1 11111 a z 4 0 4 U — 4. 4 0 . t. U ‘S .4 ‘i. -I ! : t a - I z 4 0 — 0 V C ‘J) H[iI[f[ S ‘4 H ‘C ‘C o r Li: U e r ,- — C r r a. — r z: 1 1H M fli 1 I+it (5 r Fur . : r >‘, ‘SI jjj111 3 ‘U I-. 3 - , — a-So _ 0 IZ z £ 3’3 4 , _cav, — wK4, ;j U 6 1sw’C Ji “I . 0. ., ,.d. . F—- F’ £.. — - — ‘U S.’ 0. 7rf if j;.; , C-4 c-t I •b.C * Vt H — o1 :; ‘4 a — -r 1:! — — a’ Ii z—ã z: - - 7 3 3 I 2. 10 60 I 3’? PILE OATi 2 26 i914. 39 34 ‘s33 >7 jsZ58 H >.i2 ‘ 12 ‘3 ‘ 23 “Io 35 26 24- I,,’),, L. 7.4 30 •‘ 17 45 44 80 79 7 76 71 —3 77 4,9 67 66 65 64 6) 62 61 (L4 I Ur4fl4 S :.LiCsS,OSS P•LC ft(_. 6,6oo jh_ 0 Cl LJ’6.tQ0 fI.Ib ‘.._ILEqr p,_ ogop.!.3 ,r..r 10 I, 14 ‘• 16 ‘5 ‘>•2 2. _‘ ’ 1 g “ Ii) .4 to ‘I L 3 4l6J.b! 2. II ‘3 4) 2 S 3 3 Li2 ..:.. 1l. j;‘ 1 ->j I) IJ4:. - gLows/ri I’’i lu [::I!.1 t Ii qCS ST*NCC ;— 1)5 OCNCIRATION PILE PENETRATION DIAGRAM 7:>. tC4,4NCI*N klq rAii’g._L’fq I —3- • •1 -. _________________ ‘1 0 4 0.. I 39 ‘ 46 I7’ 7 66 3! 3j ci Z — ‘i’ o 21 — — 32.. 33 -. --o .33 . Z3 j4- 13 “ 56 “ -) ‘ :& 7 2 :s 37 1 3 .24 2 — —- 2 Lo 2J 1,0 80 IM,cJ,t’/llJfm - Ac+. .. IXesp1 1k’..i OROUO±JSLdS’S) PE DATA °l2’ A.. 2 zs 3 1S ptJa _. LP 1I .., t.c CL(YV( :o 19 I828Z,?83 c9 39 I •9 3 16; I 13 3312 ‘ “ 20 61 3)2’3o “ I - ,uj — SI -1 41 I3o I-’f ‘.I3 ‘ 26 ‘ Z tI 7 2 5i__ 23 21 5 i I I J I II 113 I I 8:! ; 6 q 4 4 5’’ 3 ‘ -. ‘! ‘- ° I , .::::: c(5,5TMCE ,, ,. tiai;; - -i -;r =--: 4 t4 —. . 1::. - .. 1T(!f 4 JL j--tt “ 1-1 01 DATE 3 — .L 21 5 °_i Ai lCy 43 ‘ E. h_ .fL ..L_ 5 I 3 28 25 j 32 . 48 : 45 . 72 69 68 63 62 3 jf Z ii—., 134, 3 40 38 s “ DATA 64) 58 56 ‘ :::::r i7 . 8 78 — 76 “ i2” /l’/ 10 JJ 5/.-f onop_._. TYPE P((C_....._J’ ,i,. . .TAMM(R.12../b TYPE HAMMER 3 ‘ PILE CttVAt(ON GROUND 20 18 16 ‘ 3 32 - z 6. 7) ‘.-‘fr ‘2 P-141 3 j-p , L ‘° “-‘J ii 3 _ ° iiiL”?P 12 ‘ 8 S .L PtNETRAlON . RE5STANC( ...c . - - - ,L -- - U °I-I1-l4-l-Ii-1iP-4 141.) It TECHNICPAN............P PILE PENETRATION DIAGRAM —- 1 -— --- — 4. I L— 4_&’W n 1 ,t lw zi: -u - gLOWI ffFr’ 1 L.i l0 - -1Ji •j- 1:z: r—Ez ._ I:: .. .. 4E MIi.±(_. j d.:z-i b--—L F--—-- -_+_- — - —. :_.::L ._ L:-L--r- ;T:_.tj :EE: -‘ . IL-i-EfE Il PEN(TRT•O, PILE PENETRATION DIAGRAM ,q •ir 1 0 ,/ — 1 9 rr I-. 0 -5 0 cq >< Appendix A. Pile Driving Records ri .l 0 C) w C, 122 IL , : j3 z •j, 0 : yIII w -J 1 ! ; ,- r - I U i • ddd+ I d • b-1 0 ‘ C) uJ C.— — LI .01 ft... S. A z I L1 L [A; ] I I—I Appendix A. Pile Driving Records t7TIffff li JITf I I1IIIHW :j :R ::Ji ;I;199;lil;IiIii[ C. ) t H±[z EL :} :] h 4 K 123 o , o ç4 ? I., 3• I! ! IP.9 -C, :! a I, Ik .- , ;kki;661; ;k; M:M; iM; *1. V — b..a -C z a Appendix B Definition of Confident Interval 124 Appendix B. Definition of Confident Interval 125 In regression, y is assumed normally distribution for each fixed x. Supposedly, it is needed to know a 100(1-7) percent confident interval for the expected value of y for x = -N(0, 0 , ) yo=aHbo+eo 2 (B.1) — 0 u=y (B.2) Consider random variable The is defined as following Equation: = y + (xo N(a + bx , 0 - [ + )2]) (B.3) where n is the number of samples is the average value of samples x, is the average value of samples y, b is the linear regression factor of which was calculated from Least Square method Then: E(u) Yo and ) 0 E(Y = — ) 0 E( = 0 (B.4) are normally distribution variables and independent to each other, since D(u) = 0 D(y — o) D(yo) = [1 + + = D(yo) + D( ) 0 = (B.5) 2]o2 we get = [1 + o)2 n + — Z_ m 1 (x—x) 2 - (B.6) Appendix B. Definition of Confident Interval 126 Equation B.6 can be expressed as: N(0, 1) (B.7) o-u The unbiased estimator, of n — &2, of square deviation from Least Square has the freedom 2. (B.8) — = m 2 — Then, the following Equation is derived. (m_2)&2 (B.9) 2 ( n-2) Since u/on is independent of (ii 2)&2/o.2, — the following Equation can be derived. U —7= - t(n — 2) Equation B.10 is written as following form: Yo + -2) - + Z( )2 (B.11) A 100(1-7) percent confident interval about the regression line for each value x 0 is defined by Equation B.12. ( +t(n - 2) 1 + + °J )2) (B.12) let S(x) = t(m + - °J )2 (B.13) For the given values of r, a confident interval can be determined in which the regres sion line is located. The band of confident interval is defined by Equation B.14. I (B.14) Y2(X) = + 6(xo) Appendix C Predicted Blow Count with Different Confidence 127 Appendix C. Predicted Blow Count with Different Confidence 128 Table C.2: Predicted Blow Counts Based on Confident Interval for UBC Pile 5 Depth Rsr qea (feet) (bars) (kips) Lower Confident = 90% 27 13.35 2.98 17.3 49 20.65 34.3 23.98 72.10 56 72.0 37.40 61 71.64 45.26 87.0 65 106.04 53.63 137.6 69 106.27 64.39 165.1 77 147.68 81.81 340.9 79 132.70 87.71 302.4 82 75.28 94.01 184.3 107.14 108.46 278.1 88 134.62 113.92 392.8 90 21.21 97 121.11 173.0 Confident =_80%______ 27 13.35 2.98 16,7 49 20.65 23.98 32.4 56 72.10 37.40 68.0 61 71.64 45.26 82.3 65 106.04 53.63 124.8 69 106.27 64.39 153.3 77 81.81 147.68 303.0 132.70 79 87.71 365.8 82 75.28 94.01 174.1 107.14 108.46 258.2 88 134.62 113.92 356.0 90 21.21 97 121.11 165.9 Confident = 70% 27 13.35 2.98 16.3 49 20.65 23.98 31.7 72.10 56 37.40 65.6 61 71.64 45.26 79.4 65 106.04 j 53.63 119.2 (kips) Reg. Line Upper 14.7 28.2 56.7 68.6 99.4 119.2 209.8 199.3 144.7 204.6 258.9 143.5 13.4 24.2 46.2 55.9 78.9 94.7 151.5 148.7 119.0 159.5 194.6 122.3 1.4 3.0 7.0 8.2 12.9 15.1 47.3 35.7 16.0 29.1 68.2 14.3 1.1 2.4 5.5 6.3 9.1 10.8 19.5 18.0 12.1 17.8 25.3 11.4 0.9 1.9 4.3 5.0 7.1 8.1 13.2 12.7 9.4 13.0 16.6 9.0 1 3 5 6 10 11 18 18 12 20 19 12 14.7 28.2 56.7 68.6 99.4 119.2 209.8 199.3 144.7 204.6 258.9 143.5 13.4 25.0 48.6 58.0 82.5 99.1 163.6 156.6 123.7 166.9 207.1 126.2 1.4 2.8 6.6 7.7 11.7 14.0 36.2 27.9 15.0 25.4 50.6 13.6 1.1 2.4 5.5 6.3 9.1 10.8 19.5 18.0 12.1 17.8 25.3 11.4 1.0 2.0 4.6 5.3 7.4 8.6 14.3 13.5 9.9 13.7 17.8 9.5 1 3 5 6 10 11 18 18 12 20 19 12 14.7 28.2 56.7 68.6 99.4 13.4 25.5 49.9 60.4 85.2 1.3 2.7 6.3 7.5 11.2 1.1 2.4 5.5 6.3 9.1 0.9 2.1 4.8 5.5 7.7 1 3 5 6 10 Blow Counts (blows/foot) Lower Reg. Line Upper Measured Blow Count Appendix C. Predicted Blow Count with Different Confidence 129 Table C.2: Predicted Blow Counts Based on Confident Interval for UBC Pile 5 Contin uous Depth q Rsr (feet) (bars) (kips) Confident = 70% 106.27 64.39 69 147.68 81.81 77 132.70 87.71 79 94.01 82 75.28 107.14 108.46 88 134.62 113.92 90 21.21 121.11 97 Confident = 60% 2.98 13.35 27 20.65 23.98 49 72.10 37.40 56 71.64 45.26 61 106.04 53.63 65 106.27 64.39 69 147.68 81.81 77 132.70 87.71 79 82 75.28 94.01 107.14 108.46 88 134.62 113.92 90 21.21 97 121.11 Confident 50% 2.98 13.35 27 20.65 23.98 49 72.10 37.40 56 61 71.64 45.26 106.04 53.63 65 106.27 64.39 69 147.68 81.81 ‘77 132.70 87.71 79 75.28 94.01 82 107.14 108.46 88 134.62 113.92 90 21.21 121.11 97 Lower (kips) Reg. Line Upper 143.0 282.1 250.6 167.9 246.5 335.1 161.5 119.2 209.8 199.3 144.7 204.6 258.9 143.5 102.2 170.4 165.5 127.Q 172.2 214.9 128.8 13.0 31.4 25.2 14.3 23.5 43.2 13.2 10.8 19.5 18.0 12.1 17.8 1.3.2 11.4 8.9 15.0 14.3 10.3 14.2 18.8 9.8 11 18 18 12 20 19 12 15.9 31.1 63.4 76.7 116.6 140.0 263.9 237.1 162.1 235.8 316.4 157.3 14.7 28.2 56.7 68.6 99.4 119.2 209.8 199.3 144.7 204.6 258.9 143.5 13.4 26.1 50.5 61.2 88.0 105.6 177.9 168.7 130.6 177.8 223.4 131.6 1.2 2.7 6.1 7.2 11.0 12.7 27.8 23.1 13.8 21.9 37.7 12.8 1.1 2.4 5.5 6.3 9.1 10.8 19.5 18.0 12.1 17.8 25.3 11.4 0.8 2.1 4.8 5.6 7.9 9.3 15.8 14.6 10.7 14.8 19.9 10.2 1 3 5 6 10 11 18 18 12 20 19 12 15.7 30.4 62.3 75.4 111.8 134.1 247.9 230.8 159.3 230.8 307.9 153.3 14.7 28.2 56.7 68.6 99.4 119.2 209.8 199.3 144.7 204.6 258.9 143.5 13.7 26.4 51.9 62.9 89.4 107.3 181.8 175.4 132.4 183.8 227.8 133.1 1.1 2.5 6.0 7.1 10.5 12.2 25.0 22.1 13.5 21.2 35.5 12.4 1.1 2.4 5.5 6.3 9.1 10.8 19.5 18.0 12.1 17.8 25.3 11.4 0.7 2.1 5.0 5.8 8.1 9.5 16.2 15.3 10.9 15.4 20.5 10.3 1 3 5 6 10 11 18 18 12 20 19 12 Blow Counts (blows/foot) Lower Reg. Line Upper Measured Blow Count Appendix C. Predicted Blow Count with Different Confidence 130 Table C.3: Predicted Blow Counts Based on Confident Interval for UBC Pile 6 Depth Rsr ea (feet) (bars) (kips) Confident = 90% 27 2.95 38.60 48 20.97 57.40 60 74.83 95.57 62 82.02 103.09 66 110.82 121.56 80 109.50 190.75 89 125.03 231.36 102 21.41 273.06 Confident = 80% 27 2.95 38.60 48 20.97 57.40 60 74.83 95.57 62 82.02 103.09 66 110.82 121.56 80 109.50 190.75 89 125.03 231.36 102 21.41 273.06 Confident 70% 27 2.95 38.60 48 20.97 57.40 60 74.83 95.57 62 82.02 103.09 66 110.82 121.56 80 109.50 190.75 89 125.03 231.36 102 21.41 273.06 Confident = 60% 27 2.95 38.60 48 20.97 57.40 60 74.83 95.57 62 82.02 103.09 66 110.82 121.56 80 109.50 190.75 89 125.03 231.36 102 21.41 273.06 - L Lower Reg.(kips) Line I J Upper Blow Counts (blows/foot) Lower Reg. line Upper Measured Blow Count 47.3 76.5 180.3 202.1 311.7 476.9 680.5 369.0 40.2 64.5 140.5 158.6 225.1 353.2 482.0 306.8 38.6 57.4 116.6 128.9 178.8 276.5 367.2 273.1 3.3 6.8 23.0 25.0 30.0 53.0 134.5 32.4 2.7 5.7 16.1 20.5 21.3 31.4 49.9 25.0 45.0 73.6 167.7 190.9 289.4 443.6 625.3 350.1 40.2 64.5 140.5 158.6 225.1 353.2 482.0 306.8 38.6 57.4 121.0 135.7 187.0 289.0 385.6 273.1 3.1 6.5 21.8 23.8 27.5 45.9 98.9 30.0 2.7 5.7 16.1 20.5 21.3 31.4 49.9 25.0 2.6 5.0 13.4 15.0 18.2 24.1 33.0 21.7 3 5 21 19 20 29 43 25 44.4 71.8 162.0 185.5 270.1 423.9 593.3 341.3 40.2 64.5 140.5 158.6 225.1 353.2 482.0 306.8 38.6 57.4 124.0 139.3 193.0 298.1 398.9 278.6 3.1 6.3 21.2 23.1 25.4 42.2 83.9 28.9 2.7 5.7 16.1 20.5 21.3 31.4 49.9 25.0 2.6 5.3 13.8 15.5 18.7 25.0 34.9 22.2 3 5 21 19 20 29 43 25 43.3 70.0 156.7 177.7 264.3 405.9 564.3 337.1 40.2 64.5 140.5 158.6 225.1 353.2 482.0 306.8 38.6 57.4 127.4 141.2 199.3 307.7 413.1 284.4 3.0 6.2 20.7 22.4 24.9 39.1 72.8 28.4 2.7 5.7 16.1 20.5 21.3 31.4 49.9 25.0 2.6 5.3 14.3 18.7 19.2 26.0 37.0 22.8 3 5 21 19 20 29 43 25 - 2.6 5.0 12.9 14.1 17.6 22.9 30.5 21.7 - 3 5 21 19 20 29 43 25 Appendix C. Predicted Blow Count with Different Confidence 131 Table C.3: Predicted Blow Counts Based on Confident Interval for UBC Pile 6 Contin uous Depth 8 R qea (feet) (bars) (kips) Confident = 50% 27 2.95 38.60 48 20.97 57.40 74.83 60 95.57 82.02 62 103.09 110.82 121.56 66 109.50 190.75 80 125.03 231.36 89 102 21.41 273.06 Lower (kips) Reg. Line Upper 42.9 69.2 154.1 174.7 253.3 397.4 550.9 329.0 40.2 64.5 140.5 158.6 225.1 353.2 482.0 306.8 38.6 60.4 129.2 145.2 202.6 317.9 428.4 287.4 Blow Counts (blows/foot) Lower Reg. Line Upper 3.0 6.1 20.5 22.1 23.8 37.7 68.3 27.4 2.7 5.7 16.1 20.5 21.3 31.4 49.9 25.0 2.6 5.3 14.5 18.7 19.4 27.1 39.5 23.1 Measured Blow Count 3 5 21 19 20 29 43 25 Table C.4: Predicted Blow Counts Based on Confident Interval for Tilbury H Pile Depth Rsr ea (feet) (bars) (kips) Confident = 90% 34 44.49 65.53 40 57.07 81.18 162.35 159.68 60 141.70 224.54 74 150.74 228.36 75 164.17 247.76 79 84 122.43 270.43 Confident = 70% 34 44.49 65.53 40 57.07 81.18 162.35 159.68 60 74 141.70 224.54 150.74 228.36 75 164.17 79 247.76 84 122.43 270.43 Lower (kips) Reg. Line Upper Blow Counts (blows/foot) Lower Reg. Line Upper 77.1 97.8 238.3 320.8 331.0 369.8 370.5 69.0 87.3 207.4 280.7 289.1 321.8 325.8 65.5 81.1 185.7 249.5 259.5 288.1 294.0 7.7 9.4 27.9 60.9 69.6 126.2 113.2 6.8 8.2 20.5 36.2 39.7 56.7 55.5 6.4 7.6 16.8 25.3 27.8 35.8 36.2 5 ‘7 20 42 37 44 46 73.6 93.3 224.9 303.4 312.8 349.0 351.2 69.0 87.3 207.4 280.7 289.1 321.8 325.8 65.5 82.0 192.4 261.1 268.7 298.5 303.9 7.3 8.9 24.2 48.2 53.9 86.5 81.3 6.8 8.2 20.5 36.2 39.7 56.7 55.5 6.4 7.7 17.8 28.7 31.0 41.8 42.1 5 7 20 42 37 44 46 Measured Blow Count Appendix D Input and Output Data Sample of Application 132 Appendix D. Input and Output Data Sample of Application 133 GRLWEAP: WAVE EQUATION ANALYSIS OF PILE FQ.JNDATIONS 1987 VERSION 1.00 EngLish Units TILBURY P!LE3 BLOW CJNT PREDICTION HAMMER MCCEL OF: 0 30-13 - ELEMENT WEIGHT (kips) 2.200 2.200 2.200 1.200 2.020 1 2 3 IMP. BLK CAP/RAM MADE BY: DELMAG STIFFNESS COEFF. OF (k/in) RESTITUTION 157707.9 157707.9 96454.2 33390.0 - 1.000 1.000 .900 .800 CAP DAMPO (k/ft/s) D-NL. ft .0100 .0100 .0100 .0100 - 11.3 HAMMER OPTIONS: HAMMER NO. FUEL SETTG. STROKE OPT. HAMMER TYPE DAMPNG-HAMR 4 13 1 2 1 HAMMER PERFORMANCE DATA RAM WEIGHT RAM LENGTH (kips) (in) 6.60 MAX PRESS. (psi) 1254.0 118.10 ACT PRESS. (psi) 914.0 STROKE MAX STROKE (ft) 10.00 EFFICIENCY (It) .720 5.70 COMP/EXPN V START INJ. TIME DELAY Cs) (1n3) .00050 1.350/1.250 .0 THE HAMMER DATA INCLUDES ESTIMATED (NON-MEASURED) QUANTITIES HAMMER CUSHION INSITU RESEARCH THICKNESS (in) 2.000 E-MCCULUS (ksi) 280.0 STIFFNESS (kips/in) 33390.0 05/02/92 GRLWEAP TILBURY PILE3 BLOW CJNT PREDICTION PILE PROFILE: Area L b Top (ft) (ir2) .00 86.00 AREA (fn2) 238.50 146 14.6 E-Mod (ksi) Spec Wt (Lb/ft3) 30000. 30000. 492.000 492.000 Wave TraveL Time - 2L/c Wave S (ft/s) 16806.8 16806.8 - = 10.234 me EA/c (k/ft/s) 26.0 26.0 V V 134 Appendix D. Input and Output Data Sample of Application - No. Weight (kips) .252 1 .252 2 .252 5 .252 6 .252 7 .252 8 .252 9 .252 10 .252 11 .252 12 .252 13 .252 14 252 15 .252 16 .252 17 Toe - PiLe and SoiL ModeL Stiffri C-Sk T-SLk (k/in) (It) (ft) 7205. .010 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 7205. .000 .000 PrLE OPTIONS: N/UNIFORM AUTO S.C. 0 0 SOIL OPTIONS: X SKIN FR % END 80 49 51 261.0 kips for Rut = SoiL-S Soit-D Quake I. b Top Area C0R (ft) (in2) (in) (kips) (sift) 14.6 5.06 .100 000 .0 .850 14.6 10.12 .100 .000 .0 1.000 14.6 25.29 .100 .000 .1 1.000 14.6 30.35 .100 .050 1.3 1.000 14.6 35.41 .100 .100 3.1 1.000 14.6 40.47 .100 .100 4.9 1.000 14.6 45.53 .100 .100 6.7 1.000 14.6 50.59 .100 .100 8.6 1.000 14.6 55.65 .100 .050 10.2 1.000 14.6 60.71 .100 .050 12.0 1.000 14.6 65.76 .100 .050 13.7 1.000 14.6 7082 ;100 .050 15.5 1.000 14.6 75.88 .100 .050 17.3 1.000 14.6 80.94 .100 .050 19.1 1.000 14.6 86.00 .100 .050 20.8 1.000 .106 .150 127.9 - SPLICES DAMPNG-P 0 1 DIS. NO. 1 S DAMPING SMITH1 ANALYSIS/OUTPUT OPTIONS: !TERATNS DTCR/DT() RES STRESS 0 160 0 lOUT 10 127.9 kips, 261.0, Rtoe= Rut= max F, t miii Str, t max Str, No. miii F, t (ksi) (ksi) (kips) (kips) .00, 0 27.79, .0, 0 405.2, 5 1 .00, 0 27.91, .0, 0 407.0, 5 2 .00, 0 28.02, .0, 0 408.5, 5 3 .00, 0 28.13, .0, 0 410.2, 5 4 .00, 0 28.23, 6 411.6, 0 .0, 5 .00, 0 28.36, .0, 0 413.5, 6 6 .00, 0 28.44, .0, 0 414.7, 7 7 .00, 0 28.47, .0, 0 415.1, 7 8 .00, 0 28.31, .0, 0 412.7, 7 9 .00, 0 27.86, .0, 0 406.3, 7 10 .00, 0 27.15, .0, 0 395.8, 8 11 .00, 0 26.65, .0, 0 388.6, 8 12 .00, 0 25.96, .0, 0 378.6, 8 13 .00, 0 25.17, .0, 0 367.0, 9 14 .00, 0 24.02, .0, 0 350.3, 9 15 .00, 0 22.25, .0, 0 324.4, 9 16 .00, 0 22.91, .0, 0 334.0, 10 17 Rut kips 261.0 D-P VALUE (k/f tls) .521 AUTO SGMNT OUTPT INCR 2 0 Time hnc.= max V, t (ft/s) 5 13.69, 5 13.71, 5 13.68, 5 13.61, 6 13.57, 6 13.42, 7 13.14, 7 12.83, 7 12., 7 12.01, 8 11.67, 8 11.30, 8 10.88, 9 10.53, 9 10.35, 9 10.00, 8.66, 10 i,t max Str BL Ct Stroke (ft) miii Str ksi ksi up bpf down .00( 1, 0) 28.47( 4.8 5.7 29.5 ECHO PRINT OF INPUT DATA f,t 8, .119 ins t max 0, (in) 5 1.008, 5 .973, 5 .937, 6 .903, 6 .868, 6 .832, 7 .797, 7 .763, 7 .729, 8 .696, 8 .664, 8 .634, 9 .605, 9 .578, 9 .553, 9 .531, 10 .510, MAX T(rns) 0 t 16 16 15 17 17. 17 17 18 18 18 18 19 19 19 19 19 19 ENTHRU BL Rt kip-ft b/mm 7) 21.5 50.9
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Prediction of pile drivability from CPT and WEAP analysis Wang, Jaiwei 1992
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Title | Prediction of pile drivability from CPT and WEAP analysis |
Creator |
Wang, Jaiwei |
Date Issued | 1992 |
Description | Pile drivability is a difficult problem because of the complex dynamic pile-soil behaviour. The current procedure in predicting blow count during pile driving uses the Smith’s one-dimensional wave equation model with input appropriate soil resistances during pile driving. There is no general consensus to date on one approach for estimating the driving resistances in all types of soils. The cone penetration test (C PT) is a useful tool for detailed profiling of soil conditions at a site and has been found by many researchers to provide a reliable estimate of long term pile capacity as determined from a static loading test. An attempt has been made in this thesis to use the CPT directly to estimate pile driving resistance for use in pile drivability analysis. Several approaches were undertaken to estimate the driving resistances from the CPT, and the predicted blow counts from the wave equation analysis were compared to the field measured blow counts. Pile and soil data from three sites: UBC Pile Research Site, Tilbury Island Site and Evanston Campus of Northwestern University (ENCU) Site were analyzed. The piles included steel pipe piles of both closed and open ended as well as H pile. An empirical correlation approach is proposed which uses CPT cone bearing (qc) directly to estimate the driving toe resistances. The shaft resistances during driving, however, was estimated in a conventional way from static long term resistance calculated from correlations with CPT q,, data but was then multiplied by a set of empirical de termined reduction factors. The application of the proposed method to a steel pipe pile at another site location (not included in the above data base) is illustrated. Reasonable agreement is obtained between calculated and measured blow counts. Although the data base in this study is limited, the proposed method appears promis ing. More research is needed to check the applicability of this method to different soil condition and other pile types. |
Extent | 3324356 bytes |
Genre |
Thesis/Dissertation |
Type |
Text |
File Format | application/pdf |
Language | eng |
Date Available | 2009-02-14 |
Provider | Vancouver : University of British Columbia Library |
Rights | For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. |
DOI | 10.14288/1.0050505 |
URI | http://hdl.handle.net/2429/4597 |
Degree |
Master of Applied Science - MASc |
Program |
Civil Engineering |
Affiliation |
Applied Science, Faculty of Civil Engineering, Department of |
Degree Grantor | University of British Columbia |
Graduation Date | 1992-11 |
Campus |
UBCV |
Scholarly Level | Graduate |
Aggregated Source Repository | DSpace |
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