PREDICTION OF PILE DRIVABILITY FROM CPT AND WEAPANALYSISByJiawei WangB. Sc., Tong Ji University, ChinaM. Eng., Tong Ji University, ChinaA THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE FACULTY OF GRADUATE STUDIESCIVIL ENGINEERINGWe accept this thesis as conformingto the requi±ed standardTHE UNIVERSITY OF BRITISH COLUMBIASeptember 19920 Jiawei Wang, 1992In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.(Signature)________________________________Department of 4/v/ /‘Je-/The University of British ColumbiaVancouver, CanadaDate /DE-6 (2/88)AbstractPile 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’sone-dimensional wave equation model with input appropriate soil resistances during piledriving. There is no general consensus to date on one approach for estimating the drivingresistances in all types of soils.The cone penetration test (C PT) is a useful tool for detailed profiling of soil conditionsat a site and has been found by many researchers to provide a reliable estimate of longterm pile capacity as determined from a static loading test. An attempt has been madein this thesis to use the CPT directly to estimate pile driving resistance for use in piledrivability 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 thefield 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 wereanalyzed. The piles included steel pipe piles of both closed and open ended as well as Hpile.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 calculatedfrom correlations with CPT q,, data but was then multiplied by a set of empirical determined reduction factors. The application of the proposed method to a steel pipe pileat another site location (not included in the above data base) is illustrated. Reasonable11agreement is obtained between calculated and measured blow counts.Although the data base in this study is limited, the proposed method appears promising. More research is needed to check the applicability of this method to different soilcondition and other pile types.111Table of ContentsAbstract iiList of Tables viiiList of Figures xAcknowledgement xiv1 Introduction 12 In Situ Test and Research Sites 52.1 Outline 52.2 The Cone Penetration Test 52.3 Research Sites 62.3.1 UBC Pile Research Site 62.3.2 Tilbury Island Site 92.3.3 ECNU ( Evanston Campus of Northwestern University ) Site 93 Pile Installation 143.1 Outline 143.2 UBC Piles 143.3 Tilbury Piles 163.4 ECNU Piles (Finno et al.,1989) 174 Application of CPT in Pile Capacity Prediction 20iv4.1 Introduction.4.2 Methods of Predicting Pile Capacity from CPT4.3 LCPC CPT method (Bustamante and Gianeselli, 1982)4.4 Pile Capacity Predicted from LCPC CPT Method• 202024275 Wave Equation Analysis of Piles 335.1 Introduction 335.2 GRLWEAP Program 335.2.1 Background of GRLWEAP Program 335.3 Preliminary Analysis of the GRLWEAP Program 425.3.1 Outline 425.3.2 Basic Pile and Soil Conditions 435.3.3 Analyses and Results 435.3.4 Conclusion 486 Drivability Analysis - Blow Count Prediction 506.1 Introduction 506.2 Analysis Parameters Selection 546.3 Selection of Prediction Methods Based on Pile Capacity 566.4 Blow Count Prediction Based on Pile Shaft Resistance 616.4.1 Introduction 616.4.2 Determination of Reduction Factor 636.4.3 Results 666.4.4 Influence Factors 756.4.5 Dynamic Effects during Driving 816.5 Conclusion 87V7 Statistical Analysis and Criteria for Blow Count Prediction 897.1 Introduction 897.2 Linear Regression Analysis 907.3 Confidence Interval Analysis 917.4 Criteria Study of Predicted Blow Counts Based on Statistical Analysis 947.4.1 Outline 947.4.2 Steel Pipe Pile 947.4.3 II Piles 997.5 Conclusion 1018 Application 1038.1 Outline 1038.2 Predicting Steps 1038.3 Results 1048.4 Comparison between Predicted and Measured Blow Counts 1048.5 Conclusion 1089 Summary and Conclusion 109Bibliography 111Appendices 115A Pile Driving Records 115B Definition of Confident Interval 124C Predicted Blow Count with Different Confidence 127viD Input and Output Data Sample of Application 132viiList of Tables3.1 Summary of UBC Piles (after Davies, 1987) 153.2 Summary of Driving and Testing Details of UBC Piles (after Davies, 1987) 153.3 Summary of Tilbury Piles 173.4 Summary of Driving Details of Tilbury Piles 173.5 Summary of ECNU Piles (after Finno, 1989 183.6 Summary of Measured Capacities of ECNU Piles (after Finno et al. 1989) 184.1 Pile Capacity Prediction Methods Evaluated (after Robertson et al., 1987) 234.2 Bearing Capacity Factor k (from Bustamante and Gianeselli, 1982) . . 284.3 Friction Coefficient, a (from Bustamante and Gianeselli, 1982) 295.1 Parameters of UBC Pile 3 and Driving Data 436.1 Proposed Quake and Damping Values Used in Study 566.2 Predicted Blow Counts Based on Total Pile Capacity Method 596.3 Record of Soil Plug and Calculated R8/R ‘726.4 Blow Counts Comparison For UBC pile 4 and 5 807.1 Regression Analysis Results 918.1 Predicted and Measured Blow Counts at the Same Hammer Blow Rate. 107C.2 Predicted Blow Counts Based on Confident Interval for UBC Pile 5 . . . 128C.2 Predicted Blow Counts Based on Confident Interval for UBC Pile 5 Continuous 129C.3 Predicted Blow Counts Based on Confident Interval for UBC Pile 6 . . 130C.3 Predicted Blow Counts Based on Confident Interval for UBC Pile 6 Continuous 131C.4 Predicted Blow Counts Based on Confident Interval for Tilbury H Pile . 131ixList of Figures1.1 The Basic Procedure of Blow Count Prediction of Driven Pile 22.1 Cone Penetrometer Used at UBC ‘72.2 Simplified Soil Classification Chart for the CPT (after Robertson andCampanella, 1986) 82.3 Location of UBC Pile Research Site and Tilbury Island Site 102.4 CPT results of UBC Pile Research Site 112.5 CPT Results of Tilbury Island Site 122.6 CPT Results of ECNU Site 133.1 Load-Displacement Results of Load Testing for UBC Piles (after Robertson et al. 1987) 164.1 de Beer Scale Effect Diagram for CPT Pile Predictions (Adapted fromNottingham, 1975 214.2 Pile Capacity Distribution (after Bustamante and Gianeselli, 1982) . 254.3 LCPC CPT Method to Determine Equivalent Cone Resistance at Pile Tip,(after Bustamante and Gianeselli, 1982) 254.4 Pile Capacity Predicted from LCPC CPT Method on TJBC Pile 1, 2, 3, 4and5 314.5 Pile Capacity Predicted from LCPC CPT Method on ECNU Pipe Pile 314.6 Pile Capacity Predicted from LCPC CPT Method on ECNU 14x73 H Piles 325.1 Schematic Representation of Driving System for Wave Equation Model. 34x5.2 Hammer Driving System model for ECH hammer (adapted from GRLWEAP Menu) 375.3 Pile and Soil Model used in GRLWEAP Program (adapted from GRLWEAP Menu) 375.4 Definition of Soil Quake 405.5 Definition of Soil Viscous Damping 405.6 Block Diagram of Predictor Corrector Analysis for GRLWEAP Program(adapted from GRLWEAP Menu) 415.7 Effect of Friction Percentage 445.8 Effect of Friction Percentage with Different Values of Quake 445.9 Effect of Hammer Efficiency 465.10 Effect of Hammer Efficiency with 465.11 Effect of Smith’s Damping 475.12 Effect of Skin Quake 475.13 Effect of Toe Quake 495.14 Bearing Graph of Pile 3 at Depth of 55 feet 496.1 Increase of Load Capacity with Time (after Soderberg, 1962) 526.2 Strength Loss by Remoulding for Normally Consolidated clays. (afterFenske and Hirsch, 1986) 526.3 Proposed Model for qea 556.4 Shaft Resistance Distribution of IJBC Pile 5 at a Depth of 102 Feet . . 576.5 Predicted Blow Counts for TJBC Pile 5 Based on Total Pile Capacity Method 606.6 and R81. Determined from Analysis of Blow Counts Prediction Basedon End Bearing Method 62Xl6.7 Shaft Resistance to Total Pile Capacity vs Depth with CPT q, UBC PileResearch Site Pile 1 to 5 646.8 Calculation Steps of Establishing Empirical Correlation between Rsr/Rujand q6 for Specified Depth 676.9 Rsr/Rut vs qea for UBC Pile 3 and 5 686.10 Rsr/Rut vs ea for UBC Pile 6 686.11 Rsr/Rut vs q for UBC piles 706.12 Rsr/Rut vs qea for Tilbury piles 706.13 Rgr/Rut vs ea for UBC and Tilbury piles 716.14 Rsr/Rut vs ea for Tilbury H12x53 pile 736.15 R8/R vs qea for ECNU H piles 746.16 Rsr/Rut VS ea for H piles 746.17 Influency of Soil Profile Change to the R3r/Rut 776.18 Depth Influence to Rsr/Rut 796.19 Displacements around Driving Pile in Sand (after Robinsky and Morrison,1964) 826.20 Working Principle of a Liquid Injection Open End Diesel Hammer (adaptedfrom GRLWEAP Menu) 846.21 Influence of Diesel Hammer Blow Rate to the Pile Blow Counts with Constant 856.22 Bearing Graph of Tilbury Pile 2 at Depth of 56 feet 866.23 Influence of Diesel Hammer Rate with Constant Energy to the Pile BlowCounts 867.1 Probability of Student Distribution 917.2 Bands of Different Confident Interval for UBC Pile 5 92xi7.3 Bands of Different Confident Interval for UBC Pile 6 927.4 Bands of Different Confident Interval for Tilbury Pile 2 937.5 Bands of Different Confident Interval for Tilbury H12x53 Pile 937.6 Blow Counts Predicted from Regression Line, Lower and Upper boundswith Different Confident Interval for UBC Pile 5 957.7 Blow Counts Predicted from Regression Line, Lower and Upper boundswith Different Confident Interval for UBC Pile 6 967.8 Blow Counts Predicted from Regression Line, Lower and Upper boundswith Different Confident Interval for Tilbury H Pile 1007.9 Proposed R3r/Rut VS. ea for Predicting Blow Counts of Drven Piles . . 1028.1 Procedure for Blow Count Prediction 1058.2 Predicted and Measured Blow Counts for Tilbury Pile 3 106xiiiAcknowledgementThe author wishes to express his gratitude to his research advisor , Professor R. G. Campanella, for his support, encouragement, patient and valuable suggestions throughout thisresearch and thesis preparation. The author is very thankful to Alex Sy for suggestingthe research topic and providing data. His advice and suggestions during our discussionsproved invaluable. The author also expresses his sincere gratitude to Professors LiamFinn and Y. P. Vaid for reading this thesis and suggesting improvements.The financial support in the form of a Research Assistantship from the Natural Science and Engineering Research Council of Canada, which made this study possible, isgratefully acknowledged.The author is deeply indebted to Mr. Matt Kokan for reviewing and making improvements to the thesis. His suggestions and friendly helps was invaluable. Thank youMatt.Appreciation is extended to my colleagues, G. X. Wu and Y. C. Shi, for their valuablediscussions.A big thank you also to my parents and my sister, S. N. Wang, for their continuedencouragement, understanding and support throughout my studies in UBC. I wish tothank my dear wife Z. Q. Yang, whose love, friendship and understanding I treasure.This thesis is dedicated to them.xivChapter 1IntroductionIn design of driven pile foundations for axial loadings, two types of analyses are usuallyconducted. Given the design load and soil conditions, the pile type, pile size and lengthare determined from a static analysis. Then a dynamic analysis is carried out to select anoptimum pile driving hammer system and to determine whether the pile can be installedto the design depth. If the pile has to penetrate a dense or hard layer, it may be necessaryto predict how long the driving will continue be through the layer and to evaluate whetherthe driving stresses will exceed the structural strength of the pile. Where the pile tip isdriving into in a very dense stratum, a practical refusal criterion or final set (displacementper 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 equationprogram 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 fora given displacement increment, typically onefoot. The prediction of set or blow countis commonly referred to as a pile drivability problem. Blow counts are often predictedthroughout the penetrated soil profile, i.e. as a function of depth. Given the hammerblow rate (blows per minute), the time required to drive a pile can be estimated fromthe predicted total number of blows.Pile drivability consideration is important in offshore and onland piling for construction planning, scheduling and cost estimating purposes. The prediction of blow count,1Chapter 1. Introduction 2Reduction FactorsHammerModelPileModelSoilModelWEAPProgramPredictedBlow CountsFigure 1.1: The Basic Procedure of Blow Count Prediction of Driven Pilehowever, is a difficult task because it requires a knowledge of the resistances acting onthe pile during driving. The driving resistances depend on the complex dynamic pile-soilinteraction characteristics. Studies (Soderberg, 1962 and Hereema, 1980) have show thatthe driving resistance, particularly along the pile shaft, is less than the resistances acting on the pile from a static loading test conducted oa the pile after construction porepressure have dissipated. In this study, the pile resistance from a static loading test arereferred to as static long term resistance.The current practice in pile drivability prediction involves two basic steps. First, thelong term pile shaft and toe resistances are evaluated from in-situ tests and these aremultiplied by some reduction factors based on soil type and/or pile length to arrive atthe estimated driving resistances. In the second step, the estimated driving resistanceis used in a wave equation analysis of the hammer-pile-soil system to predict the blowcounts as a function of pile penetration. This basic procedure is illustrated in Figure 1.1.Long TermPile ResistanceResistanceDuring Pile DrMngK/Qhapter 1. Introduction 3There is no general consensus on the reduction factors applied to the static long termpile resistance. Some results of studies (Aurora, 1980; Tang et al, 1988) have shown thatthe static resistance during driving changed with the undrained shear strength of theclay. In their approach (Tang et al, 1988) a factor of 0.5 was used to multiply undrainedshear strength to estimate shaft resistance during driving in clay. The toe resistancein clay and both shaft and toe resistances in sand use the static resistances determinedfrom pile load testing. Chow et al (1988) determined both shaft and toe resistances usingremoulded undrained shear strength. They used a reduction factor of 0.33 to determineshaft resistance during driving, but a factor of 9 times the remoulded undrained shearstrength was used to determine toe resistance during driving. Heerema (1980) presenteda “friction fatigue” theory for describing pile driving behaviour in clay. In his study, thegradual decrease in skin friction during pile driving was considered to be caused not onlyby clay remoulding but also by the decrease in horizontal stress in the soil around thepile. In their research, using the WEAP86 program, Alm et al (1989) considered theblow count as a non-linear function of input energy and static resistance during drivingat a given depth. They used statistical analyses to estimate static resistance duringdriving from input energy and pile capacity. However, comparison of similar hammersdemonstrates that all hammers, unfortunately, do not provide a consistent amount ofenergy into the pile (Rausche et al, 1985). It is therefore difficult to predict drivabilityaccurately if the effects of input energy and static pile resistance during driving are notconsidered separately.The cone penetration test (CPT) provides a repeatable and reliable means of characterizing soil conditions (Campanella and Robertson, 1986). Because the CPT is a modelof 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 methodhas been found to provide pile capacities in good agreement with static loading tests atChapter 1. Introduction 4several 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 moredirect approach to pile drivability analysis. Data from three sites: UBC Pile ResearchSite, Tilbury Island Site and Evanston Campus of Northwestern University (ENCU) Sitewere used. The pile types include steel pipe piles, both closed and open ended, and Hpiles.A method is finally proposed which uses the CPT cone bearing (qc) values directlyto estimate the static toe resistance during driving and uses a reduced shaft resistancecalculated from the LCPC CPT pile capacity prediction method. It is shown that thedetailed profiling of the CPT provides a promising method of evaluating the drivingresistance for use in a wave equation analysis to predict blow counts.Chapter 2In Situ Test and Research Sites2.1 OutlineThe study in this thesis was based on the data obtained from three pile research sites.The research sites include:UBC Pile Research SiteTilbury Island SiteECNU ( Evanston Campus of Northwestern University, USA ) siteFor each site, the blow count data were analyzed in conjunction with cone penetrationtest (CPT) data to establish a method which can predict blow counts of different typesof driven piles accurately. The CPT tests were done near or at the location of pile testsections. Data from different sites with different soil types were used in the analysis toconsider the effects of soil type on the analysis.2.2 The Cone Penetration TestThe cone penetration test (CPT) is a quasi-.static penetration test. The CPT allowsfor near continuous delineation of stratigraphy. The small end area and low pushingrate, make the CPT a very good tool for modelling pile performance. The CPT has thefollowing advantages:a) continuous loggingb) rapid procedure5Chapter 2. In Situ Test and Research Sites 6c) good repeatabilityd) easy standardizationThe CPT can be used to rapidly assess soil variability at a site. The major disadvantage of the CPT is that it can not be used in some soil conditions such as gravels orheavily cemented soils. Hence, the forthcoming analysis will be restricted to soils thatare well suited to penetration testingThe electric cone is illustrated in Figure 2.1. It has a tip having base area of 10 cm2and an apex angle of 600. The friction sleeve located immediately behind the cone tiphas a standard area of 150 cm2. The cone is pushed into the soil at a constant rate of2 cm/sec and has the ability to sample on five different channels at 2.5 cm intervals,measuring the cone bearing (q), sleeve friction (f5), pore pressure (U), temperature (T)and inclination (I). Robertson and Campanella (1986) provide a comprehensive reviewof equipment, testing procedure and data interpretation. Soil classification from CPTdata is based on cone bearing and friction ratio as shown in Figure 2.2 (Robertson andCampanella, 1986).2.3 Research Sites2.3.1 UBC Pile Research SiteUBC Pile Research Site is located on the eastern end of Lulu Island, within the postglacial Fraser River delta, as shown in Figure 2.3. The surficial geology of this part ofLulu 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 laiddown in a swamp or marsh environment extend to a depth of about 50 feet. Below thisupper layer, a medium dense sand deposit, locally silty, prevails to roughly 90 feet inChapter 2. In Situ Test and Research Sitesamall,,iQuil and ar.afriction $i..vi(150cm’ ar.&)7fItting to lock14 conductor cablawfr•s iDlicid to cabi•kisida tubaa•ismem•t•rsansor,— Quad rInQQIQSS lot con•0•ilstrain gages for.friction load cellporous*5.88mm 0.0.Figure 2.1: Cone Penetrometer Used at UBCChapter 2. In Situ Test and Research Sites 81000— 11 Al I Il I10/ J\ 12 JIOw 3Z0C-)__________2I——••——————————O I 2 3 4 5 6 7 8FRICTION RATIO (%)Zona ac/N Soil B.hovlour Typo1) 2 •onsltivo fina gralnod2) 1 organic motorlal3) 1 clay4)- 1.5 silty clay to clay5) 2 clays>, silt to silty clay5) 2.5 sandy silt to claysy silt7) 3 silty sand to sandy silt8) 4 sand to silty sand5 sand- 10) 6 gravelly sand to sand11) 1 vsry stiff fine grairied C.)- 12) 2 sand to claysy sand Ca)(a) overconsolidated or camantadFigure 2.2: Simplified Soil Classification Chart for the CPT (after Robertson and Campanda, 1986)Chapter 2. In Situ Test and Research Sites 9depth. A normally consolidated clayey silt containing sand layers underlies the abovesequence to a depth of 700- 800 feet depth (Davies, 1987).2.3.2 Tilbury Island SiteTilbury Island Site is located in the middle of the Fraser River delta, as shown in Figure 2.3. The CPT results are shown in Figure 2.5. There is a filled sand layer whichis medium dense to dense up to 6.5 feet in depth. Below this layer is a silt depositcontaining thin clay layers to 14 feet in depth. Next, a fine to medium grained sandlayer with interbedded silt layers exists up to 25 feet depth. Below 25 feet are sandswhich appear to increase in density with depth. Above 41 feet depth, the sand is finegrained and loose to medium dense. Below 41 feet depth, this sand layer changes to fineto medium grained sand with some coarse grained sand, the density of this sand layerchanges from medium dense to dense.2.3.3 ECNU ( Evanston Campus of Northwestern University ) SiteThe 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 islocated 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 groundsurface, 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 bedrockis encountered. The water levels are static within the clay deposits (Finno, 1989).10Chapter 2. In Situ Test and Research Sites,—;::‘::ThburSil DE TA[wand Site. : •VANCOUVERaU.,SCA.t:0I a 3 4Figure 2.3: Location of UBC Pile Research Site and Tilbury Island Site.4-, a) a) 4- -C .4-, 0 a) 0CONEBEARINGSLEEVEFRICTIONFRICTIONRATIOINTERPRETEDPROFILEIUBCINSITUTESTINGSiteLocation:ANNAPLTCPTDote:850813MDASDWPageNo:ONSiteLoc:CPTPRIConeUsed:UBC8STDTPCqmments:NEARCASINGQc(bars)50100Fs(bars)Rf()softor9anicsiltyCLAYmediumdenseSANDminorsiltySANDlensesnormaflyconsolidatedcloyeySILTw/thinSANDlayersFigure2.4:CPTresultsofUBCPileResearchSiteoCD0)0’oo0000Depth(feet)-C_.j1’.)—o0000zU)CD0C)-oCD0C0Cl)C-)Czmr9>zC)VICD0C0—IUCCDCp II..11111111111liiiIIiiiiiiiICDC)-c-‘1C),U-C—‘CI)U)II,,P1-IC-,—ICzCD00—ICDCC—CD00CDC-P1crj -zC)C)-0-‘z.Cz-IP1-UP1-lP1ID0‘1IP1CCDCDz0C-) 033CDLi)zI,,>C) >crzC,pU-ssujg.radvqCONEBEARINGSLEEVEFRICTIONFRICTIONRATIOINTERPRETEDPROFILEQc(bars)0100200300ti)0—iiIiiiljiiiIij•_£ 4-, V 050:6070 80-_______________ASCEPILELOADTESTINGNSITUTESTINGSiteLocation:NWUCAMPUSCPTDate:87032509:44:08•PageNo:1ONSiteLoc:LAKEFILLConeUsed:ISMES—MK7—20Comments:NEARCASING400Rf(%)510SANDFineSnadSANDSiltyCLAYFigure2.6:CPTResultsofECNUSiteChapter 3Pile Installation3.1 OutlineThe test piles installed at the three research sites included H pile and steel pipe piles withclosed 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 loadtesting are presented in tables in order to make comparison between predicted and measured pile capacity. The details of both the piles used and the load testing programs arefrom Davies (1987) for UBC piles and Finno (1989) for ECNU piles.3.2 UBC PilesSix piles were driven at UBC Pile Research Site during Aug. 15 to 19 1985. All of thesepiles were steel pipe piles. The details of these piles are given in Table 3.1. A summaryof the driving and load testing is shown in Table 3.2. The complete driving record canbe found in Appendix A (Davies, 1987).All piles were driven with a steel drop hammer using a metal helmet and plywoodcushion. Pile 1 was driven with a 4400 lb hammer and the others were driven with a6200 lb hammer. Pile 1,2,3,5, and 6 were driven closed-ended with the base-plate flushwith the diameter of the piles. Pile 4 was driven open-ended. Soil plug monitoring was14Chapter 3. Pile Installation 15Table 3.1: Summary of TJBC Piles (after Davies, 1987)Pile Embedded Hammer Drop Driving Testing CapacityLength Weight Height Date(s) Date(s)No. (in) (ib) (feet) (kips)1 47.0 4400- 4 19 Aug. 85 9 Nov. 85 38.02 45.0 6200- 3 16 Aug. 85 1 Mar. 86 50.03 55.0 6200 - 4 16 Aug. 85 9 Nov. 85 137.04 76.0 6200- 5 16 Aug. 85 1 Mar. 86 270.05 102.0 6200- 6 to 7 15 Aug. 85 22 Sep. 85 241.06 103.0 6200- 10 max 14 Aug. 85 NOT TESTED15 Aug. 85performed on pile 4 during driving. After final driving, the top of the soil plug was 26.47feet 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 LoadTest Method of axial loading ( similar to ASTM 43-81 Section 5.6) was used. Davisson’smethod (1973) of interpreting axial pile load test data was used in determining pilecapacities of each pile. Experience has shown that piles driven in the Fraser delta reachtheir ultimate capacity after 4 to 5 weeks. Thus all capacities given in Table 3.2 arejudged to be at their ultimate. Figure 3.1 presents a summary of the load-displacementtest results. Based on this data, pile 1, 2, and 5 are interpreted as predominantly shaftPile Outside Wall Cross Section Pile Open/ClosedDiameter Thickness area Length EndedNo. (in) (in) (in2) (feet)1 12.75 0.375 18.56 50.0 C2 12.75 0.375 18.56 50.0 C3 12.75 0.375 18.56 60.0 C4 12.75 0.375 18.56 90.2 05 12.75 0.500 19.24 106.0 C6 24.00 0.500 36.91 118.3 CTable 3.2: Summary of Driving and Testing Details of UBC Piles (after Davies, 1987)Figure 3.1: Load-Displacement Results of Load Testing for UBC Piles (after Robertsonet al. 1987)resistance piles, whereas pile 3 and 4 had significantly larger contribution to their totalcapacity from end bearing (Robertson et al, 1987).3.3 Tilbury PilesFour piles were driven in Tilbury Island Site during March 4th to 10th 1991. Amongthese 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 diameterwas slightly larger than the diameter of the pile. The details of these piles are given inTable 3.3. A summary of driving data is given in Table 3.4. A complete driving recordcan be found in Appendix A.For the first 15 and 24 feet, respectively, piles 1 and 2 were driven by a drop hammerwith a ram weight of 12000 lb. For the first 20 feet, pile 3 was driven by a drop hammerChapter 3. Pile InstallationAXIAL LOAD(kN)400 600 80016z-J005I0152025303540 “4Chapter 3. Pile Installation 17Table 3.3: Summary of Tilbury PilesPile Outside Wall End-Plate End-Plate Cross Section Pile EmbeddedDiameter Thickness Diameter Thickness Area Length LengthNo. (in) (in) (in) (in) (in2) (feet) (feet)1 20.00 0.500 20.75 1.375 30.63 82.0 80.02 20.00 0.375 20.75 1.375 23.12 62.0 60.03 12.75 0.375 12.00 1.500 18.56 82.0 80.0HP 12x53 15.50 100.0 97.0Table 3.4: Summary of Driving Details of Tilbury PilesPile Embedded Drop Hammer DrivingDepth Weight Drop Height Depth Date(s)No. (feet) (lb) (feet) (feet)1 80.0 12000 4 15.0 9/10 Mar. 912 60.0 12000 4 24.0 9/10 Mar. 913 80.0 8000 4 24.0 9/10 Mar. 91HP 97.0 8000 4 97.0 10 Mar. 91D30-13 hammer was used after driving depth of drop hammerwith 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 in2and 415.5 in2 for up to 16 in. and to 24 in. diameter piies respectively. The HP pilewas driven by a drop hammer with a ram weight of 8000 lb. The blow rates of the dieselhammer 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 pilewas closed-ended, having a slightly oversized base plate. All test piles were embedded 50feet. The details are given in Table 3.5 (Finno, 1989).Chapter 3. Pile Installation 18Table 3.5: Summary of ECNU Piles (after Finno, 1989Pile Outside Wall End-Plate End-Plate Cross Section Pile EmbeddedTypes Diameter Thickness Diameter Thickness Area Length Length(in) (in) (in) (in) (in2) (feet) (feet)Pipe 18.00 0.375 19.00 0.750 20.76 53.0 50.0HP1 14x73 21.40 52.0 50.0HP2 10x42 12.40 60.0 50.0TableAll piles were driven with a Vulcan 06 hammer. The driving system consisted of theVulcan 06 hammer, a 5 in. cushion of alternating plates of 1/2 in. thick aluminum (4plates) and 1 in. thick micarta (3 plates) and a 1000 lb steel helmet. The cushion plateswere 11-1/4 in. outside diameter with a 3 in. diameter hole in their centres. A 2-1/2in. thick striker plate topped off the aluminum-micarta cushion. The driving records ofthe piles are given in Appendix A. Note that a 12 in. diameter hole was preaugered to adepth 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 methoddescribed in ASTM D-1143-81. Loads were applied until pile failure. Table 3.6 presentsthe summary of measured capacity for each test with different elapsed time after piledriving (Finno et al., 1989). The ultimate (long term) capacity was not reached in 5weeks, which was typical of the Fraser delta piles in sand, but took much longer asshown by the increase in capacity from 5 to 43 weeks. Thus longer time to ultimate3.6: Summary of IVleasured Capacities of ECNU Piles (after Finno et al. 1989)Pile Measured Capacity (kips)frpe 2 weeks 5 weeks 43 weeksHP 180 194 220jpe 140 160 233Chapter 3. Pile Installation 19strength is likely due to the firm local sands which took longer time for pore pressuredissipation.Chapter 4Application of CPT in Pile Capacity Prediction4.1 IntroductionThe 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 ofdifferent methods of pile capacity prediction is represented by Davies (1987). In this studythe pile capacity estimated from the CPT is adopted as a basic parameter to determinethe ultimate resistance of a pile. Current methods of predicting pile capacity from CPTare based on measured CPT parameters as well as empirical factors. These methodinclude: direct methods that use the CPT data without evaluating any intermediatevalues; and indirect methods that required intermediate correlations, such as coefficientsof earth pressure, friction angle, etc..4.2 Methods of Predicting Pile Capacity from CPTIn his earlier research, de Beer demonstrated that a scaling factor must be used to obtainthe pile capacity using q,, from CPT. As shown in Figure 4.1, when a probe of zerodiameter penetrate into soil layer, the penetration resistance will follow the idealizedcurve .4BGD. That means the device would feel the entire effect of a lower soil layerimmediately upon penetration. If a large diameter pile is pushed into the layer, the pointresistance would not equal that of zero diameter probe until the pile reached a greaterdepth, at point E. This depth is often termed the critical depth (Dc). De Beer showed20Chapter 4. Application of OPT in Pile Capacity Prediction 21xI0UCFigure 4.1: de Beer Scale Effect Diagram for CPT Pile Predictions (Adapted from Nottingham, 1975PENETRATION RESISTANCEAUIDEAL CURVE(ZERO DIAMETER)CACTUALCURVEPRCURVEDChapter 4. Application of CPT in Pile Capacity Prediction 22that it is reasonable to assume that the pile resistance curve between point B and E varieslinearly; thus, the pile resistance at any intermediate depth could be determined if theidealized penetration resistance curve and D were known. Although there is no probeof zero diameter, the diameter of the cone is sufficiently small that it can be assumed toapproximate this condition, following curve ABC’D. Meyerhof, de Beer, and others haveshown that D is a function of foundation size and soil stiffness. Therefore, it is morelogical 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 forthe pile before the influence of another layer is felt.There are twelve prediction methods based on CPT data. A summary of these methods was made by Robertson (1986) and is shown in Table 4.1. Based on the study ofDavies (1987), the direct and indirect methods both provide reasonable predictions of themeasured pile capacity for smaller piles. For large piles however it was shown that whiledirect methods predict the pile capacities quite satisfactorily, indirect methods predictedpile capacities that were significantly in error and non-conservative when compared to themeasured results for the large pile. Since indirect methods rely on correlations betweenthe CPT data and intermediate parameters, they can give erroneous results in complicated soil conditions. Based on his research, Davies suggested that the method of LCPCCPT provided the best prediction for pile capacity. The LCPC CPT method does notrequire the CPT sleeve friction value other than to define soil type. This is a desirablefeature since cone bearing is generally obtained with more accuracy and confidence thanthe sleeve friction, (Bustamante et al, 1982). In the following analysis, the LCPC CPTmethod was selected to predict pile capacity.Chapter 4. Application of OPT in Pile Capacity Prediction 23Table 4.1: Pile Capacity Prediction Methods Evaluated (after Robertson et al., 1987)Direct Methods References Notes1. Schmertmann and Schmertmann (1978) Proven CPT MethodNottingham CPT2. de Ruiter and de Ruiter and Beringen European (Fugro)Beringe CPT (1979)3. Zhou et al CPT Zhou et al (1982) Chinese RailwayExperience4. Van Mierlo and Van Mierlo and Original DutchKoppejan CPT Koppejan (1952)5. Laboratorie Central LCPC - Bustamante French Methoddes Ponts et and Gianesalli (1982)Chaussees CPT (LCPC)Indirect Methods6. API RP2A American Pet. Inst. Offshore(1980)7. Dennis and Olson Dennis and Olson Modified API(1983 a and b)8. Vijayvergiya Vijayvergia and “\“ Methodand Focht Focht (1972)9. Burland Burland (1983) ‘NB” Method10. Janbu Janbu (1976) NIT11. Myerhof Myerhof (1976 ) Original BearingConventional Theory12. Flaate and Selnes Flaate and Selnes NGI(1977)Chapter 4. Application of CPT in Pile Capacity Prediction 244.3 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 were31 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 pilestested were loaded axially. Efforts were made to defined the real geometry of the shaftand the properties of soil around the shaft of pile.The LCPC CPT method is based upon the work of Begemann (1963) and Van derWeen (1957) for point resistance calculation and Dinesh Mohan (1963) for skin frictioncalculation. The calculated limit load QL of a deep foundation is the sum of two terms,as shown in Equation 4.1.QL=Q+Q (4.1)In equation 4.1 Q is the limit resistance under the pile tip. Q is the limit skinfriction on the shaft of embedded length of the pile. They are calculated as follows:{Q:1Ijmfs (4.2)where:qca is the equivalent cone resistance at the level of the pile tip (in kN/m2)k is the penetrometer bearing capacity factorf. is the limit unit skin friction over the thickness of the layer iA is the area of pile tipis the area of pile shaft over the thickness of the layer iChapter 4. Application of CPT in Pile Capacity Prediction 25Figure 4.2: Pile Capacity Distribution (after Bustamante and Gianeselli, 1982)ea1D20.7Figure 4.3: LCPC CPT Method to Determine Equivalent Cone Resistance at Pile Tip,(after Bustamante and Gianeselli, 1982)caChapter 4. Application of CPT in Pile Capacity Prediction 26i is the number of soil layersThe 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 inseveral steps. Firstly, the curve of the cone resistance q, is smoothened so as to removethe local irregularities of the data. To be conservative, the smoothened curve is made topass closer to the valleys than to the peaks. Then using the smoothened curve, q’ iscalculated which is the mean of the smoothened resistance between the values —a to +awhere a is 1.5 times the diameter of pile. Finally, the equivalent cone resistance ca iscalculated after clipping the smoothened curve. This peak clipping is carried out so asto eliminate only the values higher than 1.3 q’ under the pile tip, whereas the valueshigher than 1.3 qca’ and lower than 0.7 q,’ are eliminated above the pile tip (Bustamanteand Gianeselli, 1982).The value of k depends on the nature of the soil, the value of q and also, on the pileplacement techniques. For driven piles, the value of k, is without reserve to closed endedpipe piles. For open ended pipe piles and H piles, the value of k must be reduced unlessit can be demonstrated, either with reference to similar cases or preferably as a result offull-scale loading test, that a soil plug occurs under the pile point, capable of taking upthe equivalent forces of a point whose section would be determined by the circumscribedperimeter.For each layer i, the limit unit skin friction f, is calculated by dividing the coneresistance q corresponding to the given level by a coefficient a as shown in Equation 4.3which accounts for the nature of the soil, the pile type and the placement method. Inselecting the values of a, it is not necessary to account for the diameter of the pile ormore precisely for the radius of curvature of the foundation.Chapter 4. Application of OPT in Pile Capacity Prediction 27f_E (4.3)The values of bearing capacity factors k and f, are given in Table 4.2 and Table 4.3(Bustamante and gianeselli, 1982).4.4 Pile Capacity Predicted from LCPC CPT MethodThe comparison between predicted pile capacities using the LCPC CPT method and theresults of load testing are shown in Figure 4.4 on UBC Pile 1, 2, 3, 4, and 5, Figure 4.5on 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 thecurves for total and frictional capacity is the end bearing capacity. Pile capacity is afunction of elapsed time since driving (Soderberg, 1962). Normally, the load testing isperformed after sufficient elapsed time. According to the study of Davies (1987) the loadtestings of UBC piles were performed based on the elapsed time which pore pressure fullydissipated. The elapsed times after driving were 38 days for pile 5, 84 days for pile 1 and3 and 210 days for pile 2 and 4, respectivelly. The pile capacity determined from loadtesting was considered to be a long term pile capacity.In general. predicted pile capacities show very good agreement with measured pilecapacities. From the results of the ECNU load testing given in Table 3.6, the predictedpile capacity agree best with the pile capacity measured after a long elapsed time. Thisdemonstrates that the pile capacity determined from LCPC CPT method represents along term pile capacity. Because of the small end area of the H piles, the determinedpile capacities of H piles show a very low end bearing capacity. The frictional componentcomprises the bulk of total pile capacity. Thus H piles are characteristically friction piles.Chapter 4. Application of OPT in Pile Capacity Prediction 28Table 4.2: Bearing Capacity Factor k (from Bustamante and Gianeselli, 1982)Nature of Soil q Factors k(MPa) Group I Group IISoft clay and mud < 1 0.40 0.50Moderately compact clay 1 to 5 0.35 0.45Silt and loose sand < 5 0.40 0.50Compact to stiff clay and compact silt > 5 0.45 0.55Soft chalk 5 0.20 0.30Moderately compact sand and gravel 5 to 12 0.40 0.50Weathered to fragmented chalk < 5 0.20 0.40Compact to very compact sand and gravel < 12 0.30 0.40Group I:Plain bored pilesMud bored pilesMicro piles (grouted under low pressure)Cased bored pilesPiersBarrettesGroup II:Cast screwed pilesdriven precast pilesPrestressed tubular pilesDriven cast pilesJacked mental pilesMicropiles (small diameter piles grouted under high pressurewith diameter < 250 mm)Driven grouted piles (low pressure grounting)Driving mental pilesDriving rammed pilesJacked concrete pilesHigh pressure grouted piles of large diameterChapter 4. Application of CPT in Pile Capacity Prediction 29Table 4.3: Friction Coefficient, a (from Bustamante and Gianeselli, 1982)Coefficients, a Maximum Limit of f,, (MPa)Nature of Soil Category(MPa) I II II IIIA B AW A B A B A BSoft clay < 1 30 30 30 30 0.015 0.015 0.015 0.015 0.035and mudModerately I to 5 40 80 40 80 0.035 0.035 0.035 0.035 0.08 0.1compact (0.08) (0.08) (0.08)claySilt and 5 60 150 60 120 0.035 0.035 0.035 0.035 0.08loose sandCompact to > 5 60 120 60 120 0.035 0.035 0.035 0.035 0.08 0.2stiff clay and (0.08) (0.08) (0.08)compact siltSoft chalk 5 100 120 100 120 0.035 0.035 0.035 0.035 0.08Moderately 5 to 12 100 200 100 200 0.08 0.035 0.08 0.08 0.125 0.2compact sand (0.12) (0.08) (0.12)and gravelWeathered to > 5 60 80 60 80 0.12 0.08 0.12 0.12 0.15 0.2fragmented (0.15) (0.12) (0.15)chalkCompact to > 12 150 300 150 200 0.12 0.08 0.12 0.12 0.15 0.2very compact (0.15) (0.12) (0.15)sand andgravelIA - Plain bored piles - JIB - Driven metal pilesmud bored piles Jaked metal pilesHollow auger bored piles -Micropiles (grouted under low pressure) -Cast screwed piles IIIB - High pressure grouted pilesPiers with diameter> 250 mmBarettes Micro piles grounted underhigh pressureTB - Cased bored piles Note:Driven cast piles Max. limit unit skin friction, f:bracket values apply to carefulhA - Driven precast piles execution and minimum disturbancePrestressed tubular piles of soil due to construction.Jacked concrete pileslilA - Driven grouted pilesDriven_rammed_pilesChapter 4. Application of CPT in Pile Capacity Prediction 30Similarly for both open and closed ended pipe piles (UBC Pile 1 to 5), when the piletip is embedded in a soft soil layer (lower q,), the end bearing is a small componentof the total capacity, and the frictional resistance comprises the bulk of the total pilecapacity. From Figure 4.4 (pile 1 and pile 2), Figure 4.5 and Figure 4.6, the predictedpile capacities agree well with measured pile capacities. These results suggest that notonly the total pile capacity but also frictional resistance can be predicted well by usingLCPC CPT method. Since frictional resistance and end bearing are estimated separatelyin the LCPC CPT method, both frictional resistance and end bearing can be estimatedseparately in predicting long term pile capacity020 -40 —60--c080 —100 -Figure 4.4: Pile Capacityand 5Predicted from LCPC CPT Method on UBC Pile 1. 2, 3, 431Chapter 4. Application of CPT in Pile Capacity PredictionPredicted Pile Capacity (kips)0 100 200 300I I—I I i I I i I I I I III400LEGENDshafttotalPile 1Pile 2Pile 3Pile 4Pile 5120Figure4.5:PileCapacityPredictedfromLCPCCPTMethodonECN[PipePileFigure4.6:PileCapacityPredictedfromLCPC(PTMethodonECNU11x73HPiles0 0 CD 0 C.) CD I—. 0PredictedPileCapacity(kips)02505007501000IIIIIiiil__i__IIIIIPredictedPileCapacity(kips)0 20—40—60-0 Ii)-80-100—1 20-Measured(after43weeks)LEGENDshafttotal60-c 0 IDLIChapter 5Wave Equation Analysis of Piles5.1 IntroductionSince the research of Isascs (1931), it has been recognized that the behaviour of drivenpiles does not follow the simple Newtonian impact as assumed by many simplified pile-driving formulas. Hence a computational tool for the analysis of pile driving, knownas the Wave Equation, was developed based on a one-dimensional wave equation. In1950, Smith developed a solution to the wave equation that could be used to solveextremely complex pile-driving problems. The solution was based on a discrete elementidealization of an actual hammer-pile-soil system using a high-speed digital computer. Ina paper published in 1960, he dealt exclusively with the application of wave theory to theinvestigation of dynamic behaviour of pile during driving. Figure 5.1 shows a schematicrepresentation of the wave equation model. Many Wave Equation programs have beendeveloped. One of the most widely used today is a program called WEAP, developedby Goble and Rausche. Since the original program in 1976, WEAP was updated toWEAP87 and then to GRLWEAP which is the program used in the subsequent analysis.5.2 GRLWEAP Program5.2.1 Background of GRLWEAP ProgramThe pile driving process provides information regarding the soil resistance. The greaterthe permanent set, S, of a pile under a hammer blow with energy Ek, the less the total33Chapter 5. Wave Equation Analysis of Piles 34TransducersattachedhereI IPile SoilFigure 5.1: Schematic Representation of Driving System for Wave Equation ModelChapter 5. Wave Equation Analysis of Piles 35driving resistance R, which opposes the pile penetration. The energy formula describingthe driving process can be expressed as follows:edehEr — E1— E1 = RS (5.1)where:ed is coefficient less than 1 to consider energy loss in driving systemeh is hammer efficiency;E,. is rated energy given by manufacturer;E1 is energy lost in pile;E31 is energy lost in soil;R is total drying resistance, andS is permanent set of a pile under one blow.Assuming Er is known, the values ed, eh, E1 and E51 can be estimated, the followingcan be done:a) Compute the set s using predicted value of R before the pile is driven. The blowcount, B, is then merely the inverse of s.b) During pile driving, B may be observed and R computed. This process is knownas a dynamic pile test.c) A bearing graph can be constructed with the ultimate soil capacity plotted versusblow count for corresponding depth. This is an analysis of drivability.The wave equation approach differs from the energy formula in that the parametersed, E1, and E31 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 36Hammer ModelThe 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 oftenused in analysis. For slender rams, often encountered in diesel and modern hydraulicunits, more than one ram segment may be necessary for simulation. As a rule, ramsegments should not be shorter than 2.5 feet or unnecessary computational efforts willresult.Driving System ModelThe driving system consists of striker plate, hammer cushion, helmet and, for concretepiles, pile cushion. The spring for the pile cushion is modeled in series with the firstpile spring. For external combustion hammers, the hammer cushion spring acts in serieswith 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 ModelThe pile model consists of springs, masses and dashpots, as shown in Figure 5.3. Thepile 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 thesegment i with respect to the overall length of the pile.Chapter 5. Wave Equation Analysis of Piles 37Figure 5.2: Hammer Driving System model for ECH hammer (adapted from GRLWEAPMenu)DIVISiONSSKIN—.—FRICTIONPILE TOERESISTANCEFigure 5.3: Pile and Soil Model used in GRLWEAP Program (adapted from GRLWEAPMenu)Chapter 5. Wave Equation Analysis of Piles 38Therefore:j a. = 1.0, j = 1,2,... ,N. (5.3)i=1The 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 pileelement, 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 is a non-dimensionalized input quantity and EA/c is the impedance of the piletop and Cdp is assumed equal for all elements.Soil modelThe soil model basically consists of a spring and dashpot, as shown in Figure 5.3. Thequake and viscous damping are defined as shown in Figures 5.4 and 5.5. The elasticspring yields at a pile segment displacement equal to q, (quake). Beyond the quake, thereno further increase in static resistance, R3, with increasing displacement, u. Thus,f R82 = for u, < qj (o.7)R3 = for u, > qiis the ultimate static resistance during driving at segment i. at each segmentis determined from the total ultimate static resistance during driving, which isChapter 5. Wave Equation Analysis of Piles 39divided into two parts, friction and end bearing. The percentage of friction is determinedby 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 thatis 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)whereRd is a dynamic resistance at segment i;sj is the Smith damping factor at segment i;T is the velocity of pile segment i , andR3 is the static resistance at segment i.Smith’s damping factor has units of time/length.Numerical Procedure and IntegrationThe 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 numbergreater than 1.0. The analysis steps are shown in Figure 5.6.Analysis Stop CriteriaAccording to the stop criteria given by GRLWEAP program, the analysis is run untilthe specified elapsed time, tmax, has been covered. The limitation of tma is 499 ms. Fora drop hammer, if the user does not specified a time, the analysis will cover an elapsedChapter 5. Wave Equation Analysis of Piles 40RRutRdFigure 5.4: Definition of Soil QuakeV = VelocityRut = Ultimate Resistanceq = Quakeu = Displacementq URd = Dynamic ResistanceJ = DampingVFigure 5.5: Definition of Soil Viscous DampingChapter 5. Wave Equation Analysis of Piles 41f tNTIAL.IZEICOMPUTE FORCE. F;ACTING ON ELEMENT ICOMPUTE ACCELERATIONAND INTEGRATERECOMPUTE FORCE. F1.jrCOMPARE VELOCITYCURRENT VS PREVIOUSOK OR.OF ITERATIONSEXCEEDED NOYESyT COMPUTE ALL.( RESISTANCE FORCESINCREMENT“P[ END ANALYSIS IFigure 5.6: Block Diagram of Predictor Corrector Analysis for GRLWEAP Program(adapted from GRLWEAP Menu)PREDICT VELOCITIESAND ACCELERATIONSChapter 5. Wave Equation Analysis of Piles 42time 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+Smsor 50 ms, whichever is longer.Non Residual Blow Count ComputationThe difference between the maximum toe displacement, Umt, and the toe quake, qt, isused as a prediction of the final net set of the pile. An average quake used in program iscomputed as follows:1N+1qav= J D (5.10)z1 11and qj are the individual ultimate static resistance and quake, respectively, andis the total ultimate staic capacity. N is the number of pile segments. The N + 1resistance is the end bearing. The predicted permanent pile set is then computed asfollows:.9 = Umt — q (5.11)and the blow count is calculated as follows:= (5.12)5.3 Preliminary Analysis of the GRLWEAP Program5.3.1 OutlineWhen using the GRLWEAP program for pile driving analysis, the hammer, pile andsoil parameters are selected according to the driving equipment used, the type of pileand soil type. Some parameters are suggested by the program based on experience andcorrelations with past field tests. In practice, it is recommended that the influence ofthe different parameters be examined so that the sensitivity of the parameters can beChapter 5. Wave Equation Analysis of Piles 43Table 5.1: Parameters of UBC Pile 3 and Driving Datadetermined. 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 ConditionsUBC pile 3 is a steel pipe pile, the details of this pile are summarized in Table 5.1 withdriving data. The ultimate capacity of UBC pile 3 determined from the static axial loadtest is 133 kips. The soil condition are given in Figure 2.4.5.3.3 Analyses and ResultsThe influences of various parameters are examined and the results of the analysis areshown in Figures 5.7 to Figure 5.13. The results are discussed below in terms of theparameters considered.A. Effect of Friction (IPERCS)From the results shown in Figure 5.7 and Figure 5.8, the effect of IPERCS is small. Forthe same ultimate total resistance, the blow count generally increased with increasingIPERCS, (i.e. with increasing skin friction and decreasing tip resistance). The blowcount is only increased 3.3% from 25% IPERCS to 75% IPERCS for an ultimateLength Outside Wall Ended EmbeddedPile Diameter Thickness Depth(feet) (in) (in) (feet)60.00 12.75 0.375 closed 55.00Hammer Hammer CushionDriving weight drop area E modulus thicknessData (kips) (feet) (in2) (ksi) (in)6.20 4.00 144.00 100.0 2.25Chapter 5.250200150(1)ciDo 1QQ50H250__1 50-U)-100-50-Wave Equation Analysis of Piles 44Figure 5.8: Effect of Friction Percentage with Different Values of QuakeJskin/Jtoe = 0.20/0.1500000 IPERECS = 25%ocn IPERECS = 5DIPERECS = 750 I I I I I j I I I I I I I I I I I I Ii I I I 1I I I I0 10 20 30 40 50Blow Counts (b/foot)Figure 5.7: Effect of Friction Percentage00000 IPERCS = 25%DD IPERCS = 75%/7 IPERCS = 25%IPERCS = 75%Solid Line Qs/Js = 0.1 0/0.20Dashed Line Qt/Jt 025/0.100 I liii 1111111 III I I II I 11111 III liii I I0 10 20 30 40 50Blow Counts (b/foot)Chapter 5. Wave Equation Analysis of Piles 45capacity equal to 133.0 kips. Figure 5.8 also shows that the influence of IFERCS issmall for constant Quake/J for both skin and tip.B. Effect of Hammer EfficiencyThe effect of hammer efficiency is very important in the analysis, as shown in the Figures 5.9 and 5.10. Figure 5.9 shows that the blow count increases about 42% whenthe 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 differentultimate capacities, as shown in Figure 5.10. When the ultimate resistance is low, lowerenergy is needed to drive the pile to the desired depth. In this case increasing hammerefficiency does not effect the blow count significantly. When the ultimate resistance ishigh, the energy needed to drive the pile to the desired depth increases. The higher theultimate resistance, the more sensitive is the blow count to hammer efficiency.From the above results, it is clear that hammer efficiency has an important influenceon 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 DampingSmith’s damping values have some influence on the results. The effect depends on thecombination of J3j4,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 occursif both J3. and J0 are increased at the same time. According to the definition of thedamping model shown in Equation 5.8, the dynamic soil resistance is proportional to thedamping factor. The dynamic resistance will increase with increased soil damping factor.Similarly the total soil resistance during pile driving increases with damping. The blowcount will increase with increasing soil resistance for a given hammer energy. On theChapter 5. Wave Equation Anaiysis of Piles 46250 =200 -1 50 =(‘)oHammerEfficency= 25%100-50H____DCDCD Hammer Efficiency = 50%Hammer Efficiency = 750- i I I I I I I I I I I I I I I I I I I I I I I I I I I I0 10 20 30 40 50Blow Counts (b/foot)Figure 5.9: Effect of Hammer Efficiency80 -7fl—L)>U-CU- -- -60-uJ-ci)EE__0 5Q - 00000 Rut = 50 kpsDDDDO Rut = 100 kipsRut = 150 kipsRut = 200 kips4o I I I I I I I I I I I I I I I I I 1_i I I I I I I I I0 20 40 60 80Blow Counts (b/foot)Figure 5.10: Effect of Hammer Efficiency withChapter 5. Wave Equation Analysis of Piles 472502001 50(1) -cs -iooI__50j250j200 -__1 50U,000000 Jskin/Jtoe = 0.10/0.15CCCCD Jskin/Jtoe = 0.15/0.15Jskin/Jtoe = 0.20/0.15**** Jskin/Jtoe = 0.15/0.10Jskin/Jtoe = 0.20/0.100 11111 I II 1111111111 11111 III liii0 10 20 30 40Blow Counts (b/foot)Figure 5.11: Effect of Smith’s DampingI II II5000000 Qskin/QtoeDacco Qskin/QtoeQskin/Qtoeocj.o’ Qskin/QtoeD500• I I I I I I0 200.15/0.106= 0.18/0.106= 0.18/0.250= 0.18/0.33040Blow Counts (b/foot)I I I I60 80Figure 5.12: Effect of Skin QuakeChapter 5. Wave Equation Analysis of Piles 48other hand, the results are nearly the same when one parameter is increased and theother 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 ofJ3k/Jt€ is not as important as the overall value ofthe damping JsIcjn. + Jtoe. Damping is generally selected based on experience, howeverthis points out the need for good in situ damping values to confirm the assumption thatare being madeD. Effect of QuakeThe effect of the quake parameters is similar to Smith’s damping parameters, althoughquake 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 about30% for values of q,kin/qtoe increasing from 0.10/0.106 to 0.18/0.33. Keeping the q3constant, the blow count increased about 10% with the qtoe increased from 0.106 to 0.25at ultimate capacity equal 133 kips, as shown in Figure 5.13. The results show that thequake values have an important influence on the final result of analysis using GRLWEAPprogram.5.3.4 ConclusionBased on the preliminary analysis for UBC Pile 3, it can be concluded that hammerefficiency is the most important factor in the analysis, followed by quake, and Smith’sdamping values. The influence of IPERCS is very small, and the result of the analysiswill not change very much for different proportions of skin friction to end bearing.250200 -1 50 HU)D50HChapter 5. Wave Equation Analysis of Piles 4900000 Qskin/Qtoe00000 Qskin/QtoeQskin/QtoeQskin/Qtoe= 0.10/0.106= 0.10/0.150= 0.10/0.200= 0.10/0.25005004003002001 000U)00—I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I0 10 20 30 40 50Blow Counts (b/foot)Figure 5.13: Effect of Toe QuakeIPERCS=75%—I 11111 II350 400I I j0 50 100 1 50Blow200CountsI I j I I I I I250 300(b/foot)Figure 5.14: Bearing Graph of Pile 3 at Depth of 55 feetChapter 6Drivability Analysis - Blow Count Prediction6.1 IntroductionThe purpose of the drivability analysis presented herein is to attempt to predict the blowcounts of a driven pile. This will be done using the program GRLWEAP and the pilecapacity evaluated from CPT data.For a given hammer-pile-soil system, the blow count varied with penetration resistance during pile driving. The total driving resistance consists of both static and dynamicsoil resistance. According to the definitions in the GRLWEAP program, the static anddynamic soil resistance are determined from the theory of one-dimensional wave equationpropagation in the pile and depends on soil parameters such as quake, damping, and ultimate static resistance during driving, The penetration resistance can be calculatedfrom Equation 6.1.Rt=R8+Rd (6.1)where:R is the total driving resistanceR3 is a static resistance component of total driving resistance acting on pile shaft andtipRd is a dynamic resistance comppnent of total driving resistance acting on pile shaftand tip50Chapter 6. Drivabiity Analysis - Blow Count Prediction 51The R8 increases linearly with pile penetration displacement before the pile penetration displacement reaches the value of quake. When the pile penetration displacementreaches and exceeds the quake, the R3 keeps a constant value of is ultimatestatic resistance during driving as defined in Equation 5.7, which has to be estimated indrivability analysis. The Rd is defined in Equation 5.8, in which the Rd is a compositefunction of static resistance, pile velocity and damping factor.The pile capacity determined from the LCPC CPT method (Bustamante and Gianeselli, 1982) was chosen to evaluate which is used as an input parameter in theGRLWEAP program. As discussed in Chapter 4, the ultimate pile capacity determinedfrom LCPC CPT method represents the long term pile capacity. For many soils, particularly cohesive soil, the ultimate pile capacity several days after pile installation can besignificantly greater than the ultimate static resistance during driving and immediatelyafter driving (Fenske and Hirsch, 1986). Figure 6.1 shows how the pile capacity increaseswith time for piles in cohesive soils (Soderberg, 1962). The maximum load capacity ofa 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 capacityof a pile depended on the excess pore pressure developed around the pile, and the rate ofdissipation of these pore pressure. In prediction long term pile capacity, both shaft resistance 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 piletip. Furthermore, shear failure takes place at the surface between soil and pile shaft during large displacement encountered in continuous pile diving. The elapsed time betweenblows is very short compared to the soil set up processes described above. Therefore, thesoil strength during pile driving will be less than the in-situ soil strength before pile driving. Typical in-situ and remoulded soil shear strength profiles for normally consolidatedclays are shown in Figure 6.2 after Fenske and Hirsch, (1986).Chapter 6. Drivability Analysis - Blow Count Prediction 52Time. HoursFigure 6.1: Increase of Load Capacity with Time (after Soderberg, 1962)Figure 6.2: Strength Lossand Hirsch, 1986)U000-c--0C,Cby Remoulding for Normally Consolidated clays. (after Fenske0Seed and ReeseCUIG)C.0EEx2030405060708090HouselBurton Quay- .5 1 10 1 00 1000Shear Strength ol Soil. Xips/F123Chapter 6. Drivability Analysis - Blow Count Prediction 53We can estimate during pile driving from long term pile capacity by introducingreduction factors, less than 1,0, into the components of pile capacity. Estimating thereduction factors for different soils requires some assumptions to be made that reflect theeffects of driving.The distribution of soil resistance between pile shaft and pile tip is determined by thetype of soil in which the pile is embedded. For example, the pile shaft resistance can bemuch larger than the end bearing if the pile tip is embedded in a soft soil layer as in thecase of a friction pile. The pile is considered as an end bearing pile when the pile tip isembedded in hard soil layer. In this study, piles are divided into friction and end bearingpiles according to pile resistance distribution. In both cases, the values of CPT q, areused to evaluate the soil conditions.Three methods were used to predict blow count of driven piles. These methods arebased on total pile capacity, end bearing component, and shaft resistance component,respectively. By using different analysis methods, the writer expects to find the best empirical correlation between and CPT q,. Based on a preliminary study, the methodbased on shaft resistance component appears to give most reasonable results. The latter method is done in three steps. The first step is to determine reduction factors forcalculating shaft resistance during driving, Rsr, from long term shaft resistance. This isdone based on experience and in-situ test results. The second step is to determinethrough simulated modelling calculation using the GRLWEAP program. The simulatedmodelling calculated blow counts to match the measured blow counts using different values of The third step establishes empirical correlations for different types of pilesby using R5 determined in the first step, determined in second step and ea whichdetermined from CPT q. qea is known as the equivalent cone bearing. It estimates thesoil influence range corresponding to the depth of the blow counts recorded during piledriving. From the empirical correlations developed, can be evaluated from long termChapter 6. Drivabthty Analysis - Blow Count Prediction 54pile capacity and CPT q. The details of the result and their limitations as well as factorsthat affected the results are given in following sections.6.2 Analysis Parameters SelectionThe 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 foreach 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. Therange includes 1.5 D above the pile tip and one foot plus 1.5 D below the pile tip, whereD is the diameter of pile. The additional one foot below the pile tip, that is differentfrom the LCPC CPT method, is used since we need to know the penetration resistancein one foot penetration intervals. In this way, the penetration resistance at the pile tipcan be considered by taking average values of q corresponding to the depth of blowcounts recorded during pile driving. The equivalent cone bearing will be used as an insitu measured parameter in the empirical method proposed. The remaining parametersused to determine pile capacity from CPT are the same as those proposed in the LCPCCPT method.In the GRLWEAP analysis, program parameters, such as quake, damping, and hammer efficiency, etc., were selected from the GRLWEAP menu. Based on the study onthe sensitive of wave equation by Ramey (1977) the skin damping is third of the toedamping. For stratified sand and clay with pile tip in sand a reasonable skin damping is0.067 to 0.10. Therefore, the damping for clay and sand use the values recommended byGRLWEAP, however a skin damping value of 0.10 s/ft for silt was used in this study. Table 6.1 shows the values of quake and damping for different types of soils as recommended55Chapter 6. Drivability Analysis - Blow Count PredictionD ft D = Pile Diametera=1.5D-b=1.Ofoota)qeaa)Figure 6.3: Proposed Model for qeain GRLWEAP (1987). In Table 6.1, d is the effective toe diameter for a displacementpile. For open cross sections, the full pile width or diameter is only applicable if the soilforms 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 tothe 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 chamberpressure (GRLWEAP, 1987). The blow rate of a diesel hammer effects the energy transmitted to the pile. The lower the hammer blow rate, the higher the energy delivered intothe pile. An adjustment method is proposed when using diesel hammers to normalizethe predicted blow count in terms of blow rate. This is necessary so that predicted andmeasured blw count can be compared at a similar hammer efficiency. —Each pile was divided into 5 foot segments for the analysis. An adjustment was madeChapter 6. Drivability Analysis - Blow Count Prediction 56Soil Type Quake (in) Damping (s/ft)Skin Toe Skin ToeClay 0.10 cl/120 0.20 0.15Silt 0.10 d/120 0.10 0.15Sand 0.10 d/120 0.05 0.15for each segment so that it could be located in only one type of soil.A relative dimensionless magnitude distribution of skin friction was selected, whichis defined by option ITYS = —1 in GRLWEAP. With this option, the shaft resistanceis accumulated with increasing penetrating depth. Figure 6.4 shows a an example of theskin friction distribution on UBC pile 5 which was installed to a depth of 102 feet.6.3 Selection of Prediction Methods Based on Pile CapacityIn 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 werecarried out based on total pile capacity, end bearing component, and shaft resistancecomponent.The analysis based on total pile capacity multiplies a reduction factor. less than 1.0, tothe pile capacity determined from LCPC CPT method to get RutS Rut is then input intothe GRLWEAP program, which computes blow counts. The ratio of shaft resistance tototal pile capacity before reduction was taken as IPERCS. It is assumed that IPERCSis constant at a given depth. The reduction factor, defined as the ratio of Rut to total pilecapacity, was determined by making the blow counts calculated equal to the blow countsmeasured during driving. The calculated blow counts were determined by GRLWEAPsimulated modelling calculations. A reduction factor of 0.70 was determined for all soiltypes based on the results of total pile capacity analysis on UBC pile 5. Figure 6.5Table 6.1: Proposed Quake and Damping Values Used in StudyChapter 6. Drivability Analysis - Blow Count Prediction.JLI G)‘I—-ctTh —L)L/ D809010057Friction Resistance (kips)6 8 10 12 1401020304070110Figure 6.4: Shaft Resistance Distribution of UBC Pile 5 at a Depth of 102 FeetChapter 6. Drivability Analysis - Blow Count Prediction 58shows comparison of predicted blow counts to measured blow counts of UBC pile 5. Twohammer drop heights, 6 feet and 7 feet, were used in the modelling according to the fieldtests. The same reduction factor was used in the analysis of TJBC pile 6. Table 6.2 showsresults from UBC pile 5 and 6. The results in Table 6.2 show that predicted and measuredblow 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, 80and 90 feet for pile 6. Large differences are made in end bearing pile condition for UBCpile 6 because of overestimation of In order to fit the predicted and the measuredblow counts better, a smaller reduction factor, less than 0.7, is required to estimatefor UBC pile 6 where end bearing pile conditions are present. The predicted blow countsare 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 ofpiles for end bearing conditions. Hence pile size must be considered in total pile capacityanalysis. When the pile size increases, the pile capacity increases for both shaft resistanceand end bearing. The shaft resistance changes because the pile shaft area increase. Astudy by Meyerhof (1982) showed that the ultimate unit skin friction of driven piles insand of a given density is practically independent of the pile diameter. The results of thisstudy also show that a good blow count prediction was made by using the determinedfrom one reduction factor for friction piles of different sizes. However it was not clearhow pile size affects the end bearing during pile driving.The results from LTBC piles 5 and 6 suggest that it is difficult to determinefrom 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 andanother reduction factor for end bearing. The results also show that the end bearingand shaft resistance during pile driving are smaller than that of long term capacity. TheChapter 6. Drivability Analysis - Blow Count Prediction 59Table 6.2: Predicted Blow Counts Based on Total Pile Capacity MethodSoil Depth Pile 5 Pile 6Type (feet) Predicted Measured Predicted MeasuredClay 48 3.5 3.0 8.8 5.0Sand 60 8.6 6.0 33.8 21.0Sand 80 13.1 15.0 45.0 29.0Sand 90 16.0 19.0 61.1 37.0Clay 102 11.5 11.0 27.7 25.0general agreement observed for UBC pile 5 suggests that using reduced pile capacity inthe CRLWEAP program to predict blow counts produces reasonable results.The method based on end bearing uses the end bearing component of long term pilecapacity to determine end resistance during driving. Again, a reduction factor, less than1.0, is multiplied to the end bearing component of pile capacity to get the end resistanceduring driving for a given depth. Then, in the modelling calculation, end resistance iskept constant while is changed until the calculated blow counts fit the measuredblow counts. In the calculation, the end resistance was kept constant and the IPERCSwas allowed to vary with The final and IPERCS was then used to determineshaft resistance, Rsr. Figure 6.6 shows the results of and Rsr determined from endbearing analysis on UBC pile 5. A reduction factor of 0.7 was used to calculate endresistance during driving from long term end bearing. It is observed from Figure 6.6 thatthe R87. reaches its maximum value of 180 kips at a depth of 84 feet, then, decreasesas low as 50 kips at a depth of 91 feet. In practice, accumulated friction resistancehas to increase as pile penetrating depth increases. The results from Figure 6.6 suggestthat the accumulated friction resistance decreases considerably with increasing depth.This apparent contradiction occurs because the end resistance is difficult to model usingonly one reduction factor as mentioned in total pile capacity method. The higher theend bearing, the lower the shaft resistance for a given R,.1. If a higher shaft resistanceChapter 6. Drivabitity Analysis - Blow Count Prediction 60Blow Count per Footo io 20 30 --I I I I I I I I I I I I I I I I20 = Fie’d Record-ooco-o Hammer Drop = T feet -- - -•..-. Hammer Drop = 6 feet - -oo:120:Figure 6.5: Predicted Blow Counts for UBC Pile 5 Based on Total Pile Capacity MethodChapter 6. Drivability Analysis - Blow Count Prediction 61is desired, a smaller end bearing is required. Therefore a smaller reduction factor (lessthan 0.7) must be used. Figure 6.6 demonstrates that when one reduction factor is usedfor all soil depths to obtain end resistance, it is not possible to keep the shaft resistanceincreasing with increasing depth.The analysis based on shaft resistance component of pile capacity is very similar tothat of the end bearing, the only difference is that the long term shaft resistance ismultiplied by a reduction factor to obtain the shaft resistance during driving. Based onthe following result, this method is considered to be useful. The details of this methodare given in following sections.6.4 Blow Count Prediction Based on Pile Shaft Resistance6.4.1 IntroductionThe blow count prediction based on shaft resistance component of pile capacity wascarried out by establishing an empirical correlation between R3.,. / and C PT q. R81.is the shaft resistance during pile driving and is determined by introducing a reductionfactor, less than 1.0, into the long term shaft resistance determined from LCPC CPTmethod. Since is summation of static shaft and toe resistance during driving thedetermination of reflects the static toe resistance during driving if Rsr known.As discussed previously, in the total pile capacity method, it is possible to use asingle reduction factor to estimate for friction pile because the predicted blow countsmatched well with the measured blow counts. When predicting pile capacity from theLCPC CPT method, the shaft resistance increases steadily with increasing depth. Theratio of shaft to total pile capacity at a given depth can be used to indicate static resistantcondition of the pile. This ratio is larger for friction piles and smaller for end bearingpiles as demonstrated in Figure 6.7. Figure 6.7 also shows that the change in the ratioChapter 6. Drivability Analysis - Blow Count Prediction5062Figure 6.6: R., and Rar Determined from Analysis of Blow Counts Prediction Based onEnd Bearing MethodRut & Rsr (kips)100 150 200 250 300I I I I I i i i i I020 —40 —80 —-120/RsrRutUBC Pile 5Chapter 6. Drivability Analysis - Blow Count Prediction 63of shaft to total pile capacity is inversely proportional to the values of q. If the sameinverse relationship exists between Rgr/Rut and q during pile driving, it is possible thata correlation between Rsr/Rut and CPT q can be found to determine assuming thefollowing criteria are met:(1) for a particular soil a single reduction factor can be used to determine R3,. fromlong term shaft resistance;(2) Rsr remains constant for a given depth during pile driving;(3) Rsr increases with increasing depth.An empirical correlation between R3r/Ru and q, was established using GRLWEAPprogram stimulated modelling calculations. In the following discussion, data from different sites and pile types are used to find empirical correlation between Rsr /R and q. Theanalysis was initiated by estimating the reduced shaft resistance during pile driving, Rsr,from long term pile capacity. Then, different values of were input into GRLWEAPprogram to calculate blow counts until the calculated blow counts equal those measuredduring pile installation for a specific depth. A final and ratio of Rgr/Rut was determined for every one foot depth increment. Finally an empirical correlation betweenRsr/Rut, (IPERCS), and equivalent cone bearing, qea, was established to determine6.4.2 Determination of Reduction FactorThe shaft resistance during driving, Rsr, was determined by introducing a reductionfactor to the long term shaft resistance determined from the LCPC CPT method. Forcohesive 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 significantlyless 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 64qc ave. (bars) Shaft/Total Pile Capacity (%)o 40 80 120 160 50 100_______________________I I I I I___________-Soil Profile25— ClAY50- I: SAND(. silty75 lensesIi)- I100- ClAY25- -150—_ __ _I_ _Figure 6.7: Shaft Resistance to Total Pile Capacity vs Depth with CPT q, UBC PileResearch Site Pile 1 to 5Chapter 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 displacement and vibration, resulting in permanent rearrangement and some crushing of soilparticles. The frictional resistance depends on the stress history of soil, the shape androughness of pile, and other factors (Meyerhof, 1982). It is usually assumed that in sandthe 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 studyby Tang et al (1988). However in research on soil set up, Fellenius et al (1989) showedthat the frictional resistance increases significantly up to twice of the frictional resistanceduring initial driving in restriking after 1 day when a pipe pile embedded in stratum ofsandy clay and silty sand. In the following analysis, reduction factors, less than 1.0, wereused for both clay and sand. The reduction factors were determined using friction pileconditions 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 weresmaller than 10 bars;(2) (IPERCS) remains constant at 90 % in the GRLWEAP simulated modelling calculation.Based on the proceeding assumptions, was determined for each one foot depthincrement and Rsr was calculated from using Rsr/Rut of 90%. The reduction factorswere finally determined by comparing R3r to long term shaft resistance. In this manner,reduction factors of 0.5 for clay and 0.7 for sand were determined. Many factors, suchas OCR, structural failure of soil, volume displacement of the soil and pile geometry willaffect the shaft resistance of the pile (Meyerhof, 1982). The reduction factors chosenreflect the comprehensive influence of these factors.Chapter 6. Drivability Analysis - Blow Count Prediction 666.4.3 ResultsThe analysis procedure is presented in the flow chart shown in Figure 6.8. Each pile typewas analyzed separately. For each pile, R5,. was first calculated from long term shaftresistance 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 blowcounts. In the calculation procedure, the IPERCS varied with Final IPERCS,i.e. RSr/RUt, and were determined for each one foot depth interval. The correlationbetween Rsr/Rut and qea was established and a linear best fit was applied to the data toindicate the proposed correlation.Results on Closed-Ended Steel Pipe PileThis analysis is based on UBC pile 1, 2, 3, 5 and 6 as well as Tilbury pile 1 and 2. Thedetails 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, thepile tip was embedded in cohesive soil where values of q, were lower than 20 bars. Thecalculated ratios of Rsr/Rut were 80 to 100 percent, indicating a lower end bearing, andconfirming the friction pile behaviour. When the pile was driven from 50 to 90 feet,the calculated values ofR3r/Rut varied from 70 to 45, indicating higher end bearing andshowed end bearing pile behaviour. The Rsr/Rut versus qea is shown in Figure 6.9 forTJBC pile 3 and 5 and in Figure 6.10 for UBC pile 6. Since UBC pile 1 and 2 were onlydriven into depth of 47 feet and 45 feet, respectively, all calculated values of R8/Rwere in the range of 80 to 100. Clearly when the pile tip was embedded in hard soil theRsr/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 correlationbetween Rsr/Rut and qea can be established to show the tendency of Rgr/Rut to changeChapter 6. Drivability Analysis - Blow Count PredictionFor each considered depthV67:_-Recording Rut1PERCS: Rsr/Rut.4Figure 6.8: Calculation Steps of Establishing Empirical Correlation between andea for Specified DepthShaft Resistanceqeafrom LCPC CPT MethodReduced Shaft ResistanceRsr 0.5 Clay, 0.7 SandAssume RutIPERCS = Rar/RutVGRLWEAP ProgramCal. Blow CountsHammer, Pile & SoilParametersParametersfrom WEAP Menu—I I I I I I I I I I I I I I I I I I0 20 40 60 80 100 120 140 160 1qea (bars)Figure 6:9: Rgr/Rut VS qea for UBC Pile 3 and 51100— I_I 11111111 I 11111 11111 II II0 20 40 60 80 100 120 140 160 180qea (bars)Figure 6.10: R37/Ru vs q for UBC Pile 6Chapter 6. Drivabiiity Analysis - Blow Count Prediction 681101 00 -90 -80:----. 70-_60-503020-10-0—e.______Pile 5000000 Pile 3..: - •Point 1••.•••••0 •..•.‘ .-SDU,90- 8100 0 0080->° 0 070- 00060—000050-0 Point 340 -- Point 230 -20 -10-Chapter 6. Drivability Analysis - Blow Count Prediction 69with soil variation. The results show values of R8r/Rut decrease with increasing qeaWhen a linear best fit line is plotted through the data, the trend of Rgr/Rt versus qfor UBC piles 5 and 6 agree well, as shown in Figure 6.11. At the range of ea from 50to 110 bars, most data points are located below the line for pile 5. For pile 6 the lineappears to give an average values. The calculated values of Rsr/Rut were unreasonablelow above 10 feet for pile 5 and above 5 feet for pile 6 for the values of qea A thresholdpenetration depth must be reached to get reasonable results. This depth is defined as theeffective beginning depth. Some values deviate significantly from the best fit lines. Thisis somewhat controlled by soil type, especially when the soil type changes either fromsoft 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. Adiesel 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 valuesof qea at different depths. For a given value of and pile end bearing, R3/R increaseswith increasing depth because shaft resistance increases. Pile 1 was used to examine thedepth influence. From the pile 1 data in Figure 6.12, the values of Rsr/Rut tend to bea constant value of about 50 percent. A non-linear tendency is clearer for the Tilburypiles than for the UBC piles.A comparison of UBC and Tilbury piles is given in Figure 6.13. All three linearbest fit lines agree well. For UBC pile 5 and both Tilbury piles, most of the points arescattered 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. Thevariation of Rsr/Rt with ea will be discussed later in the statistical analysis.Chapter 6. Drivability Analysis - Blow Count Prediction 70110-10Q-o 0. -o— I I I I I I I I I I I0 20 40 60 80 100 120 140 160 180ceo (bars)Figure 6.11: vs q0 for UBC piles110-1 00 -0- iii ii III I II I III II 1111110 20 40 60 80 100 120 140 160 180qea (bars)Figure 6.12: vs q0 for Tilbury pilesChapter 6. Drivabiiity Analysis - Blow Count Prediction 711101009080— 706050403020100Results on Open-Ended Steel Pipe PileThe analysis on open ended steel pipe pile was carried out oily for UBC pile 4. Fromthe observation (Davies, 1987) soil plug was formed during pile driving. However in theGRLWEAP program, the pipe pile with soil plug is simply modelled as a nonuniformpile. Such that the properties of the pile change in the sçil plug reflect the combined pileand soil properties (Goble et al.,1987). According to this model, relative movement andfriction between soil plug and pile interface are ignored.Field records of the soil plug are given in Table 6.4 (Davies, 1987). The results ofcalculated R,r/Rut for piles 4 and 5 are given in Table 6.3 to show the difference betweenopen-ended and closed-ended piles. The field record shows that relative movement be- -tween soil plug and pile did take place during tile driving. This movement causes anincrease in shaft resistance. The resistance of open-ended pile tends to increase becauseqea (bars)Figure 6.13: vs qea for UBC and Tilbury pilesChapter 6. Drivability Analysis - Blow Count Prediction 72Table 6.3: Record of Soil Plug and Calculated Rsr /RPile 4 Pile 5Data Measured Calculated CalculatedEmbedded Length Length Interval Interval R3/R Rsr/RutDepth Without of Embedded PlugPlug Plug Depth(feet) (feet) (feet) (feet) (feet) (%) (%)48 21,2 29.1 78 7650 59.6 30.6 2 1.5 65 5455 56.0 34.2 5 3.6 56 5160 52.3 37.9 5 3.7 73 6765 48.8 41.4 5 3.5 85 5068 46.7 43.5 3 2.1 62 6171 44.3 45.9 3 2.4 73 6176 40.8 49.4 5 3.5 70 48of friction between the soil plug pile interface, however the resistance also tends to decrease because of smaller end bearing of the open-ended piles. The results indicate thatthe values ofR3/R of open-ended pile are larger than for equal diameter closed-endedpiles. Looking at depths below 60 feet, the open-ended pile 4 behaved like a frictionpile, whereas the closed-ended pile 5 behaved like an end bearing pile. It can be concluded that for open-ended piles, when relative movement exists between soil plug andpile, the resistance is mainly composed of shaft resistance, hence an open-ended pile willbehave like a friction pile. Also Rsr/Rut is larger for an open-ended pile than that for aclosed-ended pile.Results on H PilesThe 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 likefriction pile as discussed in Chapter 4.Chapter 6. Drivability Analysis - Blow Count Prediction 73110-1 00 - cnxrxD L00 00 O8009Q 0- 0O 0 O0 ° 0 080- 0 0 0 0 oo0g0- 0—. 70- 060-3020 -10-I I I I I I I I I I I I I I I I I I I I0 20 40 60 80 100 120 140 160 180qea (bars)Figure 6.14: R57/R vs q0 for Tilbury H12x53 pileThe calculated values of Rar/Rut were fiom 75 to 100 percent. Figure 6.14 shows theresults of the Tilbury H pile. Figure 6.15 shows the results of the ECNTJ H piles. Inthese figures, numerous points are located in a band of 25 percent where the values ofqea change irom 45 to 170 bars. The linear best fit line appears to give average values ofcalcu1atd values; The linear best fit lines agree well for these three H piles even thoughthe points scattered in relative wider band,V as shown in Figure 6.16.VIn this study, unreasonable results were made in determining end bearing for somedepths.- The end bearing, determined by calculated R,4 minus the shaft resistance duringdriving, was larger than the end bearing predicted from the CPT. In order t. explainthis problem it is necessary to consider the determination of shaft resistance of the Hpiles. 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. Thissoil plug will affect the magnitude of the shaft resistance. Estimating the change in shaftChapter 6. Drivability Analysis - Blow Count Prediction 7440-30 --••... H1Q*42opooo H14*73I I I I I i ‘ I I I I IC 20 40 60 80 100 120 140 160 180 200 220 240qea (bars)Figure 6.15: R,r/R,.j vs ea for ECNU H piles110-DD40-30-____ECNU H10*42-- °-°-Q ECNU H14*7320- TIL H12*530— I’ I II II III ii I_T I0 20 40 60 80 100 12Q 140 160 180 200 220 2qea (bars)Figure 6.16: R87/R VS ea for H pilesChapter 6. Drivability Analysis - Blow Count Prediction 75resistance is more complex since it is difficult to model the types of soil plug formed in Hpiles. Based on the results, as shown in Figure 6.16, an average value of 90 for R8/Rcan be used to predict when q is smaller than 75 bars. The relationship given bythe best fit line in Figure 6.16 can be used when qea is larger than 75 bars. This will bediscussed further in the statistical analysis.6.4.4 Influence FactorsThe empirical correlation between Rsr/Rut and qea is affected by many factors. Therecorded blow count during pile driving is influenced by many variables within thehammer-pile-soil system. Firstly, soil conditions change from site to site. The valuesof CPT qc may be very different even for the same kind of soil. In this study, the CPTresults are only used to indicate the relative strength of the soil. Secondly, the valuesof Rsr/Ru will change with increasing depth and pile size. Also the soil plug formedwithin open-ended piles must be considered when more complex soil resistance conditioninfluence the values of Rsr/Rut. The final and perhaps most important factor is the hammer efficiency. The energy of diesel hammer changes with different combustion chamberpressure and soil conditions. The recorded blow count is always related to a hammerblow 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 Profileea is an average of q,, values and therefore describes the soil resistance to end bearingtype penetration. The soil profile of each site is described by CPT data. When the valuesof q are small, the soil is considered soft. In soft soils the pile end bearing is low, and thepile behaves like a friction pile. Hence the values of R3r/R determined are large. Whenthe values of q are large, the soil layer is considered hard. In hard soils the pile endChapter 6. Drivability Analysis - Blow Count Prediction 76bearing is high, and the pile behaves like an end bearing pile. The values of RSD/RUt arethen small. The values of qea normally provide reasonable predictions of However,in some instances values of R3/R deviate significantly from what is expected. Thishappens especially when soil layers changed from soft to hard or from hard to soft. Inthese situations the q is influenced by the values of q for different soils, as shown inFigure 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. Thereason is that the value of ea is influenced by hard soil layer, but the pile tip is embeddedin a soft soil layer. The contrary phenomena can be seen in Figure 6.10, where points2 and 3 show low values of Rsr/Rut which normally correspond to high values of qea atdepth 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 makebetter prediction, a comparison of blow count has to be made when the pile tip locatesin both the upper and lower soil layers in the boundary zone where soil types change.Pile Embedded Depth and Pile ScaleFrom the results, the ratio of R3r/Rut reflect the pile resistance conditions, i.e. frictionpile or end bearing pile. However this ratio only make sense after a certain depth, definedas effective beginning depth. The values of Rsr/Rt are not reliable above the effectivebeginning depth since shaft resistance is not large enough. For example, a very lowvalue of Rsr/Rt, 18 percent was determined corresponding to a value of 99 bars qea at adepth of 3 feet for UBC pile 5. The effective beginning depth is affected by the pile sizebecause the shaft resistance increases with increasing pile size. For example, the effectivebeginning depth is 5 feet for UBC pile 6, and 10 feet for UBC pile 5.Chapter 6. Drivability Analysis - Blow Count Prediction 77Qo (bors)0 60 100 150 2000— I ._L...L.._I. II,iiii 111111 IIIPt2&3=I I— I I I I I I I I120 100Rsr/Rut (%)Figure 6.17: Influencyof Soil Profile Change to the R27/RChapter 6. Drivability Analysis- Blow Count Prediction 78The value of Rsr/Rut tends to be constant with increasing depth when qea is greaterthan 110 bars, as shown in Figure 6.12 on Tilbury pile 1. From Figure 6.12, Rsr/Ruttends towards a constant value of 52 percent below the depth of 70 feet while ea changesfrom 110 to 160 bars. This means that shaft resistance increases with increasing depthat the same rate as increases. If qea is the same at different depths, then Rsr/Rut willincrease with increasing depth. Therefore, higher values of R3/R have to be selectedcorresponding to the same qea value when a pile is driven deeper. This tendency isobserved at shallower depths for large piles since increases with increasing pile size.Figure 6.18 shows that the average value of Rsr/Rut increases from 47 to 52 percent whilethe depth increases from 55 to 90 feet. It is important to understand what depths arelikely to show increase in caused solely by further increase in embedded depth. Anadjustment is proposed based on statistical analysis in the following chapter.Soil PlugFrom the test records, the measured blow counts of UBC pile 4 are nearly the same asmeasured blow counts of other UBC piles when the piles were driven in cohesive soilsabove 50 feet. While the open-ended pile has a small end bearing compared to theclosed-ended pile, the may be nearly same for both kinds of piles because the lowend bearing of the open-ended pile is balanced by the increased friction between soil plugand pile interface. When the tip of UBC pile 4 was driven in sand, relatively low blowcounts were measured compared to the measured blow counts of the closed-ended pileof the same size, and the of open-ended pile was smaller than the of the closedended pile. The record shows that relative movement between soil plug and pile did takeplace during pile driving. Table 6.4 gives a comparison of blow counts at normalizedhammer energies for TJBC pile 4 and pile 5. There were some difference in length andwall thickness between pile 4 and pile 5, but, from preliminary analysis, these differenceChapter 6. Drivability Analysis - Blow Count Prediction 79RaUo of Rf/Rut ()0 10 20 30 40 50 60 70 80 90 10050— I I I I I I=60-65-700 -85-90—95 —100— -Figure 6.18: Depth Influence to Rar/Rut0 0D>00IZI000000C* 0000C0TILBURY PILE 100000 qco = 110 to 120 barsCODOC qca = 120 to 130 bars>>>>> qca = 130 to 1 40 barsX**qca = 140 to 150 barsqca = 150 to 1 60 barsChapter 6. Drivability Analysis - Blow Count Prediction 80Table 6.4: Blow Counts Comparison For UBC pile 4 and 5Depth Pile 4 Pile 5(feet) Energy Used in Pile 5 Measured Measured48 2.6 3 350 3.4 4 555 6.2 7 760 5.3 6 665 5.2 6 1068 8.7 10 971 7.8 9 1076 9.4 10 17should not affect the results significantly. The ratio of R3r/ is higher for open-endedpile than that for closed-ended pile at the same pile size. An average value of 5 percentis suggested to be added to Rsr/Rt for open-ended pile based on same size closed-endedpile.When the soil inside the pile is fully compacted, the length of soil plug in the pile willno longer increase. In this case the end bearing for both closed and open ended piles maybe similar and an may be determined by using the same value of Rsr/Rt. In theGRLWEAP program, when modelling the pile with soil plug, it is assumed that the soilhas negligible stiffness compared to the pile. Thus, the pile area and modulus specifieddescribed the pile stiffness, and the specific weight of the pile reflects the combined pileand soil properties. A non-uniform pile model was used in the analysis. To do moreaccurate analysis, a model that considers friction between the soil plug and the pile maybe necessary, such as in the model proposed by Heerema and de Jong (1979).Chapter 6. Drivability Analysis - Blow Count Prediction 816.4.5 Dynamic Effects during DrivingThe CPT is a quasi-static penetration test yet the analysis of pile driving is a dynamicproblem. The fact that the LCPC CPT method is very good at predicting pile capacityis a result of the fact that the CPT measures parameters that are more relevant to soilfailure during static loading, such as during a pile load test. Since pile driving is adynamic process it is not too surprising that in some cases measured static parametersdo not apply while static and dynamic resistance of soil are related, the relationship cannot 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 therelationship was a function of grain size and the relationship is non-linear. The dynamiceffects caused by driving must be considered in determining from qea, especially forcohesionless soils. Since the grain size of soil is relatively small compared to the size ofthe 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 densityaround 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 displacementand 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 sidesand thus nullify some of the benefits gained by the primary compaction. The pattern ofdisplacements 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 bestfit line as shown in Figure 6.13. It seems that the best fit line under-estimated the valuesof RSr/RUt for Tilbury pile 2 more than for the UBC piles. The reason for this is thatthe q values increase with increasing density of sand in Tilbury site below 14 feet. Thevalues of ea are lower in loose sand than in dense sand. However the soil resistanceChapter 6. Drivabilitv Analysis - Blow Count Prediction 82Figure 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. Thereforethe calculated values of R,r/R.jt in loose sand corresponds to slightly lower values ofqea More research on this problem is required to better understand the limitation of theCPT.Correction for Diesel Hammer Blow RateIn the analysis of Tilbury piles, the blow counts as well as hammer blow rate wererecorded for the D30-13 diesel hammer. The hammer blow rate is related to the hammerenergy and soil resistance during pile driving. The higher the hammer blow rate, thelower is the energy delivered to the pile. Figure 6.20 shows the working principle of aliquid injection open end diesel hammer. .The hammer energy changes with combustion chamber pressure. If the fuel is adjustedChapter 6. Drivability Analysis - Blow Count Prediction 83to a lower level, the combustion chamber pressure is lower. Also, the hammer reboundwill be lower resulting in higher hammer blow rate. If the is higher, the hammerrebound will be higher which results in the lower hammer blow rate. In order to considerdriveability when using a diesel hammer, the hammer blow rate is always recorded alongwith blow count. The different blow counts can be observed corresponding to differenthammer energy delivered to the pile, however the ratio of R8/R is the same for aspecified depth. In order to make comparison between predicted and measured blowcount, adjustment of blow count according to hammer blow rate is proposed for dieselhammer.Figure 6.21 shows the blow counts change with hammer blow rate for different valuesof When is lower, the change in hammer blow rate does not cause significantchange in blow counts, However, when is higher, changes in hammer blow rate resultin large change in measured blow counts. This can be explained by Figure 6.23 andalso by the bearing graph Figure 6.22. By setting combustion chamber pressure, theinput hammer energy is fixed. Then the hammer blow rate only changes with asshown by the family of curves in Figure 6.23. The hammer blow rate decreases withincreasing R, but tends to a constant value when increases. This is because ramrebound increases with increasing resistance towards an upper limit. When is largeenough the gravity energy of ram rebound balances the maximum energy provided bythe explosion of combustion chamber pressure. The height of the ram rebound remainconstant and the hammer blow rate become constant as well. The combustion chamberpressure provided the hammer energy. The higher the combustion chamber pressure, thehigher the hammer energy. The higher the ram rebound, the lower the hammer blowrate. Figure 6.23 shows the hammer blow rate decreases with increasing energy and italso shows that the values of that cause ram rebound to become constant increasewith increasing energy. Before a constant hammer blow rate is reached, the blow countsChapter 6. Drivability Analysis - Blow Count Prediction 84HAMMEGUS HICM UStAMBERA) FREE FALL & FUEL INJECTiONB) IMPACT & IGNITIONC) EXHAUSTD) SCAVAGINGFigure 6.20: Working Principle of a Liquid Injection Open End Diesel Hammer (adaptedfrom GRLWEAP Menu)CYLZN D ERCZRA B DChapter 6. Drivabiiity Analysis - Blow Count Prediction 8550:484645-C -44- I Diesel Hammer D30—13E ocCo Rut = 250- A - DO Rut = 30043 2 Rut = 350--**** Rut = 400—-_ I! I Rut = 450-I“ Rut 50042= IT’ ut = 550-Rut = 600-00000 Rut = 65041 = *ev. Rut = 7004011 i_i I I II 111111 111110 50 100 50 200 250 300 350-Blow Counts (bpf)Figure 6.21: Influence of Diesel Hammer Blow Rate to the Pile Blow Counts with Constant R,Chapter 6. Drivability Analysis - Blow Count Prediction 86800600(/) -PERCS=45%- 400 -200—I I_ I I I I I I I IO 100 200 300 400 500 600Blow Counts (bpf)Figure 6.20: Eearin(Graphof Tilbury Pile 2 at Depth of 56 feet50Rut = 250 kips49 305Q• 48Qo-D-j, 46-0::45-0L0 -442 -41 - Diesel Hammer D30—134.0] i JI I I liii I0 50 100 150 200 250 300 350-Blow Counts (bpf)Figure 6.23: Influence of Diesel Hammer Rate with Constant Energy to the Pile BlowCountsChapter 6. Drivability Analysis - Blow Count Prediction 87increase slowly with increasing After a maximum hammer rate is reached, the blowcounts increase faster with increasing This explains the nonlinear characteristic ofversus blow counts shown in Figure 6.22. When R, is lower, the hammer energy doesnot operate efficiently because the ram rebound is low. When a constant cam reboundis reached, corresponding to a value of the hammer energy is at its maximum. Thevalue of at this stage is noted as Rutm. If R. is smaller than Rutm, the energytransmitted 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 hammerblow rate increases, the blow count increases slowly. If is larger than Rutm, the energytransmitted to the pile can not increase because the energy is at its maximum. In thiscase the hammer blow rate becomes constant, but blow count increases significantly, asa 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 of42 blows/mm for Tilbury pile 2. Point 2 shows the blow counts of 38 blows/foot withhammer rate of 43 blows/mm for Tilbury pile 1. Both piles were embedded at a depthof 57 feet where was predicted at 459 kips. Thus a great difference in blow countmay be caused by a small difference in hammer blow rate. If a diesel hammer is usedwhen predicting blow counts, the predicted blow count must be presented with hammerblow rate. It is suggested that for every types of diesel hammers a figure like Figure 6.21should be developed.6.5 ConclusionThree assumptions based on pile capacity determined from CPT were made to establishthe empirical correlation between and q, in order to predict the blow counts of drivenpiles. From the results, the assumption based on shaft resistance component was selected,Chapter 6. Drivability Analysis - Blow Count Prediction 88and empirical correlations between Rsr/Rut and qea were found for steel pipe piles andH piles. The ratio of Rsr/Rut is considered to reflect the resistance condition of pileembedded in different soil layers. The values of ea determined from CPT q are affectedby the size of pile. The main conclusions are as follows:(1) The ultimate resistance R,.Lt during pile driving is smaller than pile capacity determined from CPT. A reduction must be properly selected in order to model soil resistancecondition 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 endbearing pile. can be evaluated through establishing correlation between R3/R andqea, where q. reflect soil strength.(3) For the same kind of piles, the correlations between Rsr/Rt and show verysimilar results. The correlations are independent of driving system and are affected bythe soil strength, pile embedded depth and pile size.(4) For the different types of piles, the correlations between Rsr/Rt and q are verydifferent. The difference are related to the pile resistance distribution.(5) There is potential to use the method proposed in this study to predict blow countof driven concrete and timber piles. But, it is necessary to determine reasonable reductionfactor and establish the correlation analogous to those established in this study.Chapter 7Statistical Analysis and Criteria for Blow Count Prediction7.1 IntroductionThe main purpose of predicting blow counts of a driven pile is to ensure good performanceof the hammer-pile-soil system. Good performance should be defined realistically in termsof error limits with a given confidence level. The predicted blow counts change with theultimate resistance which can be determined from q, and Rsr. Due to the non-linearnature of the bearing graph, as shown in Figure 6.22, at lower levels of a change ofcauses a small change in blow count. But, at higher levels of even a very smallchange of will, cause a big change in blow count. In Chapter 6, it was observed thatpoints of computed R3r/Rut were scattered in a band. The band width and slope wasdifferent for different types of piles and different magnitude of qea. The values ofdetermined from the R3r/Rut versus ea figures depend on the slope of the bands. Anyerror in will result in an error in predicted blow count, especially at high level.Statistical analysis allows a better understanding of limitation of the proposed methodand gives an indication of the reliability in applying the proposed method.Statistical analysis can be used to draw information about a sample, and thus estimatevalues that help characterize the population from which the sample was chosen. Throughstatistical analysis, a confidence interval for an expected value can be determined toindicate the reliability of the result. Because of the limited availability of data, we wouldchoose one pile sample to do a single pile statistical analysis. The statistical analysis89Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 90performed herein will consider UBC pile 5 and 6, Tilbury pile 2 and Tilbury H12x53pile.7.2 Linear Regression AnalysisIn order to describe a correlation between two variables, the analysis must combinethe distributions of all the measured variables with respect to a particular reference toreach the accuracy with which some variables can be determined from the others. Inthe drivability analysis, the measured parameter qea is an independent variable. It isproposed that the computed value RSr/RUt is a dependent variable. The extent to whichqea controls the computed values of R8r /Rt will determine the correlation coefficient.A linear correlation is assumed between R3r/Rut and q€ based on the results ofChapter 6. The following assumptions are made in proposing a straight line regressionthrough the data.1. The qea values based on q are error free.2. The regression of Rsr/Rt and ea is linear.3. The deviations Y— E(Y(X) are mutually independent, where x is q and y isRsr/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 Equation 7.1 [44].= a + bx + , - N(0,2) (7.1)If the estimators of a and b is a and b, the estimator of y is . The equation = a + bxis called a regression function.The Least Square Method is used to estimate a and b [44]. The results of the linearChapter 7. Statistical Analysis and Criteria for Blow Count PredictionPile aUBC P5 92.216 -0.3622 8.7457UBC P6 97.195 -0.3917 8.7178TIL P2 91.134 -0.3597 9.2687TIL HP 101.570 -0.1527 5.69727.3 Confidence Interval AnalysisIn the regression analysis, y1 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 = x0.Thàt probability of 100(1-c) percent confidence interval is defined the white area, asshown in Figure 7.1 144]. The mathematic details of confidence interval are presented inAppendix B [44].Table 7.1:91Regression Analysis Resultsfira/20 tj e,2a/2Figure 7.1: Probability of Student Distributionregression for different piles are given in Table 7.1. The unbiased estimator of & of themaximum likelihood estimator, o are determined [44] and given in Table 7.1 also. Thefigures of Rgr/Rt versus qea in Chapter 6 show that the linear regression lines agree wellbr the same type of piles. ---Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 92- 110-100- —20-‘•:........‘10- -— I I I I I I I I0 20 0 60 80 100 120 140 160 180qea (bars)Figure 7.2: Bands of Different Confident Interval for UBC Pile 5110100__70 - °‘ oo7020 9010-0I II I III 1.111111 II III III II0 20 40 60 80 100 120 140 160 180qea (bars)Figure 7.3: Bands of Different Confident Interval- for UBC Pile 6Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 93109Q-fl0 20 40 60 80 100 120 140 160 180qea (bars)Figure 7.4: Bands of Different Confident Interval for Tilbury Pile 21D--0 20 40 60 80 ‘100 120 140 160 180qea (bars)Figure 7.5: Bands of Different Confident Interval for Tilbury H12x53 PileChapter 7. Statistical Analysis and Criteria for Blow Count Prediction 94The calculated results of yo for selected values of x0, together with the lower andupper 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 Analysis7.4.1 OutlineFrom the results of statistical analysis, lower and upper bounds are determined accordingto different confidence levels. For a given value of q and R3r, the lower bound relatesto a lower value of RSr/RUt which give a larger value of and the upper bound relatesto a higher value of R3r/Rt which give a smaller value of The predicted blowcount increases with increasing if other parameters remain constant. The range ofpredicted blow counts corresponds to lower and upper bounds of depends on thevarious confidence levels. In order to keep the error of predicted blow counts as low aspossible, different levels of confidence have to be taken into account for different valueranges of qea Due to the nonlinear relationship between and blow counts, as shownin Figure 6.22, when the value is high, a small error in estimated can result ina very large difference in the predicted blow counts. An error study of predicted blowcounts based on the regression and confidence analysis results was made on UBC piles 5and 6, and the Tilbury H pile. The purpose of this section is to examine the change ofpredicted blow counts with a defined confidence and to give a reliable lower and upperbound in determining The results of this study are given in Appendix C.7.4.2 Steel Pipe PileFigures 7.6 and 7.7 show the results of predicted blow counts using the values at regression line, lower and upper bounds with different confidences for UBC piles 5 and 6.-c 0‘ 60-80—I I ‘1 0 C) 0Qea(bars)00BlowCount50(blow/foot)100II1500 20-40-BlowCount(blow/foot)0o-e--o*4-*-#***-4-4-#20 40 60 80 1001 2020MeasuredReg.LineLowerBoundUpperBound40Confidence=90%Ir q’9I60-1z ClII)080 100120-120Figure7.7:BlowCountsPreáictedfromRegressionLine,LowerandUpperboundswithDifferentConfidentIntervalforUBCPile5I, 0.C.) 0 C;’-4-,-4-,Qea(bars)800BlowCount(blow/foot)4080BlowCount(blow/foot)020406080C 20r 0Confidence90%oo000Measured•-..-.Reg.Line**-*-4-4eLowerBound*4-*-4-4UpperBoundCqnfidence=7020 40 60-c 080 100120ooooMeasuredReq.Line**-*-*LowerBound*4-*-*-4UpperBound-12040 60 8I 100120Cl)j) C,C,,C,,1Figure7.8:BlowCountsPredictedfromRegressionLine,LowerandUpperboundswithDifferentConfidentIntervalforUBCPile6Chapter 7. Statistical Analysis and Criteria for Blow Count Prediction 97The values of q and measured blow counts are shown as well. The following tends areobserved based on these figures.When the value of ea is smaller than 30 bars, the value of Rgr /R is larger than 70percent. The pile behaves as a friction pile and the blow count calculated from onthe linear regression line agrees well with the measured blow count. Up to 30 percenterror can be expected based on lower and upper bound corresponding to the 90 percentconfidence, for predicted blow counts. 30 percent error is acceptable at relatively lowerlevels of blow count. For example, for the results from 102 feet depth of pile 6, thepredicted 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 rangeof blow counts can be predicted. In comparing the results for the pile tip embedded insoft soils at different depths, it was shown that the errors of predicted blow count are notinfluenced by increasing depth, and the blow count only increases with increasing shaftresistance for friction pile condition.When the values of qea are from 30 to 100 bars, the values of R3r/R change fromabout 80 to 60 percent, as shown in Figures 7.6 and 7.7. This happens when pile tip isembedded in depths of 60 and 62 foot for UBC pile 5, and in depths of 56, 61, and 82feet for UBC pile 6. In this range, the calculated blow count based on linear regressionline agrees well with measured blow count. The errors of predicted blow counts basedon lower and upper bound are small, except that predicted lower blow counts of 60 footdepth of UBC pile 6 has an error of 28 percent. It seems that a better way to predict theblow counts is based on the range between regression line and lower bound. When thevalues of q are between 100 and 120 bars, the values of R5/R are about 54 percentand the pile behaves as an end bearing pile. The calculated blow counts based on thelinear regression line agrees well with the measured blow counts. Since the values ofare higher, a small change in will result in a large change in blow count. EvenChapter 7. Statistical Analysis and Criteria for Blow Count Prediction 98though the percentage of error may be the same as for lower values of q, there aresome difference between predicted blow count and measured blow count, depending onif the prediction is based on lower or upper bound. A very large error can be made whenpredicting blow count using lower bound, especially for deep piles. For example, the errorof blow count predicted with 90 percent confidence for UBC pile 6 at 80 feet is 83 percentwith predicted blow count of 53 comparing to measured blow count of 29. However, theerror of blow count calculated from the upper bound is smaller. The above phenomenoncan be explained by the depth and pile size influence which result in increasing shaftresistance with increasing depth. The increase of shaft resistance with depth causes thevalues of RSr/RUt to be higher and the values of R3r/Rt determined from lower boundto be lower. This can be seen by comparing pile 5 and pile 6. Since the size of pile 5with a diameter of 12.75 in. is smaller than that of pile 6 with a diameter of 24 in., theRsr of pile 6 is about twice the R8r of pile 5 at the same depth. For pile 5 at a depthof 88 feet, the blow count calculated from lower bound is 29.1 comparing to measuredblow count of 20, and for pile 6 at a depth of 89 feet, the predicted blow count fromlower bound is 134.5 comparing to measured blow count of 43, with a confidence of 90percent. It is recommended that at higher values of qea, the lower bound is not reliable topredict blow count, especially for large size piles embedded in deep pile condition. Theband between the upper bound and linear regression line with 70 percent confidence issuggested to predict blow counts.When the values of q are between 120 bars and 150 bars, the predicted blow countsappear to have bigger errors, with a maximum error of up to 213 percent, from lowerbound with a 90 percent confidence. At the same time, the blow count predicted basedon linear regression line is higher than the measured blow counts for deep piles like pile5 at a depth of 90 feet and pile 6 at a depth of 89 feet. Obviously, shaft resistanceincreases with increasing depths. From the analysis results, it can be concluded thatChapter 7. Statistical Analysis and Criteria for Blow Count Prediction 99upper bound gives more reliable values than lower bound even with a confidence of50 percent in predicting blow counts. This shows that the assumption of linear regressionis no longer applicable at higher levels of qea, especially for large size piles. However, acombination of upper bound of 80 percent confidence with linear regression line can beused to predict the blow count for the piles of small size. A combination of upper boundof 70 percent confidence with linear regression line can be used to predict blow countsfor the piles of large size. In this range of qea, the linear regression line can be consideredas lower bound and the values ofR3/R in the range between regression line and upperbound can be used to predict blow counts.7.4.3 H PilesFigure 7.8 shows the predicted blow counts for the Tilbury H pile. As shown in Figure 7.8the linear regression line is the characteristic of a shaft resistance pile. The values ofRsr/Rut are larger than 75 percent when the values of are up to 170 bars. FromFigure 7.8, the predicted blow counts based on lower bound overestimated in alldepths even though the values of qea are higher than 160 bars. The error increases withincreasing depth. This results from increasing shaft resistance with lower end bearing atdepths. The predicted blow count based on regression line with upper bound representsa reliable interval when compared to measured blow counts. When the values of q arelower than 30 bars, a value of 100 percent for Rsr/Rt can used to estimate Aconfidence interval of one o, or about 70 percent, can be used to predicted withinthe band of the regression line and upper bound.Figure7.9:BlowCoitutsPredictedIroniI{egrcssunLine,LowerandUpperboundswithDilCerciitCotitidentIntervalfur‘i’iLbtiry11Pile0Qea(bars)4080120160200III1111.111.11II0BlowCount(blow/foot)50100150BlowCount(blow/foot)50100Iiiiiiiii20-4060-80-100-150Confidence90%G-G-G-e-oMeasured••--Req.Line**-*-*LowerBound*-*-*-UpperBound0 20-4c60-80-100—00-20-40-.1::-I-. 0. 60-80-100-\Confidence=70%oo000Measured••-..Req.Line4-*-9-4-4LowerBound-*-4-*UpperBound*1C.C. I 0 IChapter 7. Statistical Analysis and Criteria for Blow Count Prediction 1017.5 ConclusionThe above analysis is only based on limited data. More analysis is recommended to dofor piles at different sites. Some conclusions based on statistical analysis are summarizedas follows.For steel pipe piles:(1) In predicting blow count of friction pile, which values of RSr/RUt are larger than 75percent, a confident interval of 90 percent can present reliable value.(2) When ea is smaller than 30 bars the linear regression line can only be used topredict blow count.(3) When qea is between 30 and 80 bars the lower bound can be used to determineespecially 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 usedto predict blow count if the pile tip is embedded in a depth more than 80 feet, due tothe fact that increasing depth causes increasing shaft resistance. especially for large sizepiles.(5) When q is between 100 to 150 bars, the upper bound with 70 percent confidencecan be used with linear regression line to predict blow count.(6) When is larger than 150 bars, the upper bound along with the linear regressionline can be used to predict blow counts for small size piles. When a large size pile isembedded in depth over 100 feet, the upper bound with a correction of depth can beused 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 10211010080—- 70060Dcc- 50Cocc40______30201000 20 40 60 80 100 120 140 160q ea (bars)1) When q is lower than 40 bars a value of 90 to 100 percent Rsr/Rut can be used toestimate(2) When q. is higher than 40 bars the linear regression line with upper bound canbe used to determine R. in predicting the blow counts.In Figure 7.9, each different band with correction depth was suggested to determinefor different types of piles.Pipe PileH PileNumber refer to pile embedded depth180Chapter 8Application8.1 OutlineIn this chapter, a case history of using the method proposed in previous chapters topredict blow count of driven pile is presented to show the applicability of this method inpractice. This method is based on CPT q and shaft resistance determined from LCPCCPT method. Figure 7.10 derived based on statistical analysis in Chapter 7 is usedto determine with 70 percent confidence. The prediction was performed based onTilbury pile 3 which is a closed ended steel pipe pile. The details of the soil conditionshave been presented in previous chapters. Comparison between predicted and measuredblow count were made to show the accuracy of the predicted results. A sample of inputand output data is presented in Appendix D.8.2 Predicting StepsThe predicting steps are as follows:(1) Calculate average q for each one foot depth from data file of q measured everyinch 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, R5r, by introducing reductionfactors, 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;103Chapter 8. Application 104(5) Determine with known q, and R8;(6) Input with soil, hammer and pile parameters into GRLWEAP program tocalculate blow counts for each one foot depth until final pile penetration.8.3 ResultsThe 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 GRLWEAPmenu as recommended for the D30-l3 diesel hammer, type of pile and soil conditions ofTilbury Site.According to the record given in Appendix A, the hammer fuel value was adjustedto minimum to give lowest combustion chamber pressure. Therefore, the hammer fuelsetting option, IFUEL was set to 4 corresponding to the minimum pressure value of 914psi. The hammer stroke option IOSTR was set to 0 or 1 to make the calculated hammerblow rate at a given depth the same as that recorded during pile driving. Since therecorded hammer blow rates are not available for every depths, an average hammer blowrate between two recorded hammer blow rates is used at the depth where no hammerblow rate was recorded.The predicted blow counts are presented in Figure 8.2 with measured blow countsduring pile driving.8.4 Comparison between Predicted and Measured Blow CountsThe predicted blow counts agree well with the measured blow counts when the pile tipwas embedded in depths above 21 feet. Soils above 21 feet change from clayey silt tosand and silt. Lower blow counts were measured during pile driving because pile was4- a.UStep4StepSFigure8.I:ProcedureforBlowCountPrediction0 01RsrUtps)160Rsr/Rut(%)501RutUIps)BC(blows/foot)20020406020 100Step1St,ep2&3Step6Chapter 8. Application 106Blow Count (blows/foot)o io 20 30 40 50 60I I I I I I I‘—,. _.o- . e•--•-:-•o-o-eeo Measured•••‘• Predicted20-• •. ;•- .•-050:80 -90-Figure 8.2: Predicted and Measured Blow Counts for Tilbury Pile 3Chapter 8. Application 107Table 8.1: Predicted and Measured Blow Counts at the Same Hammer Blow RateDepth Blow Counts (blows/foot) Hammer Blow Rate ENTHRU(feet) Predicted Measured (blows/mm) (kip— ft)52 26 28 54 17.859 23 25 51 22.366 36 31 51 22.472 35 30 50 22.278 40 35 50 21.3ENTHRU stands for transferred energyembedded in soft soil where q, values are low. The same lower blow counts are predictedin terms of lower values determined from lower q values.From depth of 22 to 42 feet, the soil is sand with density increasing from mediumloose to dense with depth. The predicted blow counts are much lower compared to themeasured blow counts, even though larger values were determined from the lowerbound in Figure 7.10. This means that the values of were under-estimated. Qvalues are affected by soil density with q,,, increasing with increasing density. In loosesand, q values are lower because of low density however this density will increase duringpile driving because the soil is compacted by displacement and vibration. Therefore,lower values are determined by using the values of qea However, the prediction canbe used as a reference, even though the error is high, because the expected blow countsis limited to a lower level that will not cause refusal in pile driving. The highest errorwas 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 withmeasured blow counts except slight over-predicting at depths from 65 to 68 feet. Thedetermined by using q is more reliable in dense sand than in loose sand. Table 8.1gives comparison between predicted and measured blow counts at the same hammer blowChapter 8. Application 108rate. From this table it is clear that for a fixed hammer blow rate at depth greater than50 feet, accurate prediction can be made.8.5 ConclusionThe above results show that reasonable predictions can be made using the method described, especially in cases where high blow counts may be expected. The prediction isaffected by the soil density. A useful evaluation of the drivability prediction can be madeusing this method before pile driving.Chapter 9Summary and ConclusionThe main purpose of this study was to examine methods of using CPT in conjunctionwith 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 Siteand Evanston Campus of Northwestern University (ECNU) Site. The pile types includesteel pipe piles, both closed and open ended, and H piles.An empirical correlation was established to estimate the driving toe resistance directlyfrom the CPT q. The shaft resistances during driving were estimated from static longterm pile resistances evaluated from the LCPC CPT method and multiplied by a setof reduction factors. Reduction factors, 0.5 for clay and 0.7 for sand, were used. Thedriving resistances were input into a commonly used program, GRLWEAP, to calculatedblow counts for a given hammer-pile-soil system.The proposed correlation of CPT to pile toe resistance during driving depends on soildensity as characterized by CPT q values, pile embedded length and pile size. A statistical 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. Theresults show reasonable prediction of blow counts,The results of wave equation analysis are sensitive to the input hammer efficiencyand soil quake and damping. Average values of observed hammer efficiency , and typicalvalues of soil quake and damping based on predominant soil type are compiled by theGRLWEAP menu used in this study.109Chapter 9. Summary and Conclusion 110It is recommended that further correlation studies include dynamic measurement ofpile driving to directly determine the transmitted energy into the pile and to allow backcalculation of the soil parameters.Bibliography[1] Aim, T., Bye, A. and Kvalstad, T. J. (1989), “A New Interpretation of Soil Resistance for Pile Drivability Analysis”, Proceedings of the 12th International Conference 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”, Proceedings 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”, Adventures in Earth Sciences Series No. 15, B. C. Govt. Publications.[5] Burland, J. B. (1973), “Shaft Friction of Piles in Clay - A Simple FundamentalApproach”, Ground Engineering, Vol.6, No.3, pp. 30-42.[6] Bustamante, M. and Gianeselli, L. (1982), “Pile Bearing Capacity Prediction byMeans of Static Penetrometer CPT” Penetrating Testing, ESOPT II, Amsterdam,Vol.2, pp.493-500[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), “WaveEquation Analysis of Piles - A Rational Theoretical Approach”, Third InternationalConference 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 BehaviourUsing 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, Innovations in Foundation Construction, ASCE, Illinois Section, Chicago, USA.111Bibliography 112[11] de Beer, E. E. (1963), “The Scale Effect in Transportation of the Results if DeepSounding Tests on the Ultimate Bearing Capacity of Piles and Caisson Foundations”, Geotechnique, Vol.8, p.39.[12] de Mello, V. F. B. (1969), “Foundation of Buildings on Clay”, State of the ArtReport: 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 SeaStructures”, Marine Geotechnology, Vol.3, No.4, pp. 267-314.[14] Dennis, N. D. and Olson, R. E. (1983a), “Axial Capacity of Steel Pipe Pile inClay”, Amer. Soc. of Civil Engineers, Geotechnical Practice in Offshore Engineering, Austin, pp. 370-387.[15] Dennis, N. D. and Olson, R. E. (1983b), “Axial Capacity of Steel Pipe Pile inSand”, Amer. Soc. of Civil Engineers, Geotechnical Practice in Offshore Engineering, 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 andDesign of Fixed Offshore Platforms, Van Nostrand Reinhold Company, New York,1986.[18] Finno, R. J. (1989), “Subsurface Conditions and Pile Installation Data: 1989 Foundation 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 ofNinth 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), “Dynamicand 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 ModifiedWave Equation Analysis of Pile Driving- CUWEAP Program”, Vol.2, Departmentof 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 Piledriving- A State-of-the-Art”, Proc. Tnt. Seminer on Appl. of Stress-Wave Theoryon Piles, Stockholm, pp. 131-150.[25] Heerema, E. P. and de Jong, A. (1980), “An Advanced Wave Equation ComputerProgram which Simulates Dynamic pile Plugging through a Coupled Mass-springSystem”, 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 ofthe ‘Friction Fatigue’ Theory”, Ground Engineering, April 1980, Vol.13, No.3.[27] Isaacs, D. V. (1931), “Reinforce Concrete Pile Formula”, Inst. Aust. EngineeringJournal, Vol.12.[28] Janbu, N. (1976), “Static Bearing Capacity of Friction Piles”, Proceedings of theEuropean 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 CohesionlessSoils”, 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. 197-228.[32] Meyerhof, G. G. (1982), “Scale Effects of Ultimate Pile Capacity”, Journal ofGeotechnical Engineering, ASCE, Vol.109, No.6, June 1982.[331 Middendorp, P. and Zandwijk, C. Van (1985), “Accuracy and Reliability of Dynamic Pile Testing Techniques”, Behaviour of Offshore Structures, Delft, 1985.[34] Nottingham, L. C. (1975), “Use of Quasi-Static Friction Cone Penetrometer Datato Predict Load Capacity of Displacement Piles”, Ph.D. Dissertation, Departmentof Civil Engineering, University of Florida, USA.Bibliography 114[35] Robertson, P. K. and Campanella, R. G. (1986), “Guidelines for Use, Interpretation 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), “Capacity 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 CompactionAround Model Friction Piles”, Canada Geotechnical Journal, Vol.1 No.2, pp. 81.[38] Schmertmann, J. H. (1978), “Guidelines for Cone Penetration Test, Performanceand Design”, Federal Highway Administration, Report FHWA-TS-78-209, Washington, July, pp. 145.[39] Smith, E. A. L. (1950), “Pile Driving Impact”, Proc. Industrial Computation Seminar, Sept., International Business Mechanics Corp., New York, USA.[40] Smith, E. A. L. (1960), “Pile Driving Analysis by the Wave Equation”, Journal ofthe Soil Mechanics and Foundation Division, ASCE, Vol.86, No.EM4.[41] Soderberg, L. (1962a), “Consolation Theory Applied to Foundation Pile Time Effects”, Geotechnique, Vol.12.[42] Tang Nian-Ci, Yuan Ji-Hong, Wang Yong and Lu Tong-Shen (1988), “DrivabilityAnalysis of Long Steel Pipe Piles - Case History Studies”, Third InternationalConference 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 ofPiles in Clays”, Proc. Fourth Offshore Technology Conference, Houston, Vol.2, pp.865-874.[44] Zhe Jang University (1979), “Probability and Mathematical Statistics”, HigherEducation Publishing House, Beijing, China, 1979 (in Chinese).[45] Zhou, J., Xie, Y., Zuo, Z. S., Luo, U. Y. and Tang, X. J. (1982), “Prediction ofLimit Load of Driven Pile by CPT”, Penetration Testing, Proc. 2nd, EuropeanSymp. Penetration Testing, ESOPT II, Amsterdam, Vol.2, pp.957-61Appendix APile Driving Records115Appendix A. Pile Driving Recordsza.wa.1J6rj020V2z0Cza1.04—I‘L)N-—--a—-—i———EEEEE1ETEEEEEE0-D2z0—emAppendix A. Pile Driving Records0az0I.IUIzUIUI-i117HLMt1i1I1i t1W.l IIIWUH Nitt[[fl iiifl1t: LHu1-d- zww2 0v.‘‘,. —Irs )4—. .,..dp,,I-I0.300C 1 (qf41 -iU3Sa-,2’-tI 22aLH H i:fl.iflTt.1‘ I Ij j f *:--C:6z:WIUk ! _:UI tO 11im ‘iIL:II0z0I-UIzUI0.UI-I0.11:::vI.’i 3 2— I d 3 C)•0’I! .-4 ,. N NLI’t°4LLNL±- = : =C.00Iwo—> C.,zC-,•; :C20V)‘-I•-C—zo oC0HI0—Appendix A. Pile Driving Records118402t. 4H Iii1+1Lt ifl{11III. iliti I . use4.10N-H±tt11Li’0z0z-Ja1tMtii——---• 334— ISO a 300..I.2Tii ; ;—-v-•-L;.—I.. f.!! .!! !. !, I 0LI)4S.2 ‘ñg. a-I-I.I i____- ...: U•. I”fi:t[ Itj N1 . -4. ii:.:;‘.4us.7II:C‘3z0-UIC.31..CL[04 00 001 3 3 .5— II I .130s.d-— r ,-4‘I0‘U.1II.’1.7‘UtoLi0<>1.Ir—- r. fl9U r-Li\s.pI-s9.I.UI‘—r-JT—1.I:I : :: : :: Jz:34I.0I S: ‘-t-I-I WII0Appendix A. Pile Driving Records 119HFiIEfiLl J Ii .i .4‘U-J‘C‘3‘Caz0-I01:1 111114 4— U 4. 4 0.t.! : aU.4t‘i.z4a.‘SIIU C — —— r r r e ,- r r r r->‘,z:0C‘4H[iI[f[ Fur 1S1H M jjj111H. fliI+itV‘J)‘C(5‘Co ,“I0.‘US.’0.: 1 Li:-£ 3’3 4;j—_cav,wK4, 6 1sw’C‘SI3‘UI-.3,—a-So0_IZz UJi—‘4z:.., ,.d. F’ F—--. £..——7rf, if C-4 c-t -r o1j;.; :;a—— a’1:! Iiz—ã— HI •-b.C * Vt0..4 0 ‘1PILEPENETRATIONDIAGRAM-.klqrAii’g._L’fqItC4,4NCI*N7:>...:..;‘1j->j•11l.Li2(L4I61I)•-3 3 3-S2 2.4l6J.b!L 7OCNCIRATIONqCSST*NCC-gLows/ri_________________1)5luIJ4:.;—4) 44 45 17‘3IIL.I,,’),,24-62 6) 64 65 66 67IiI’’i[::I!.1t4,9—3-.43•‘26Ii)3to3035“‘I_‘1’g‘>•2“Io232.7.4‘3‘52.H‘16>.i2‘s33‘•I‘12>73914jsZ5834I,i914.77 —3 71 76 7 79101060I3’?PILEOATi802266,6oo0jh_ogop.!.3ft(_.ClLJ’6.tQ0fI.Ib,r..r‘.._ILEqrp,_:.LiCsS,OSSP•LCUr4fl4S><cq 0 -5 0I-.IPILEPENETRATIONDIAGRAM•ir,/10,q,,,-‘-5PEN(TRT•O,c(5,5TMCE-gLOWI‘!q212416139l0-.Illw‘4.:::::3I4237-120IL-i-EfE-1n,t5’’5i__4.6J26467663j-;I‘I-’f‘I7’3!.;T:_.tj8:!I3oI‘.I3‘i’33L:-L--r-1::.————-.----‘._,.113I,uj13o21o.33:_.::LIISI-ciZ32..—.II3)2’3otIZ3312-)2J:EE:-I::ffFr’ii‘13‘2—._..1 L..——-r—Ez“I‘j4-“Lo:s.24I-_+_-16;I562:&3F--—--1:z:zi:“3Z3“27b--—L•j-..I828Z,?8319I39c9•937:o°l2’1,0801°—.PEDATAIM,cJ,t’/llJfmMIi.±(_.j--ttCL(YV(OROUO±JSLdS’S)jd.:z-i44JLt.c-Ac+.IXesp11k’..it4“..=--:-..,..4E-1Ji..-;r1T(!fzsptJaLP231S_.1I-i-uA..tiai;;IPILEPENETRATIONDIAGRAMDATElCy°_iTECHNICPAN............P5...c.1.LE.PtNETRAlONRE5STANC(—01—21‘h_.fL..L_U°I-I1-l4-l-Ii-1iP-4141.)1-14362ZIt.L—Ai.1jf134,--—----:—-S325545634_&’WL 8—2848683‘3j.69ii—.,,L‘°ii“-‘J-_iiiL”?P3° 123232723j-p----rr3‘i7“‘2P-141‘“‘JJz‘.-‘fr916s35676—183858782034064)108PILEDATA6.CttVAt(ONGROUNDTYPEHAMMER..TAMM(R.12../b-7),i,.onop_._.-,LTYPEP((C_....._J’5/.-f.:::::ri2”/l’/.122Appendix A. Pile Driving Records.lr ij3•j,yIII1-0C)wC,z0w-J, IL:: L [A; ]!; ,-ri ddd+IU•Id•b-10C)uJ C.—LI.01S.ft...L1‘IIAzI—IAppendix A. Pile Driving Records 123)_____Koo I.,ç4 3•-C,:!_____ ___aH±[zC.4?I!EL :}t7TIffff lit JITf I I1IIIHW,:] :j :R ::JihIP.9I,;I;199;lil;IiIii[!I k ;kki;661; ;k;;M:M;;iM;.- , *1.V—b..a-CzaAppendix BDefinition of Confident Interval124Appendix B. Definition of Confident Interval 125In regression, y is assumed normally distribution for each fixed x. Supposedly, it isneeded to know a 100(1-7) percent confident interval for the expected value of y forx =yo=aHbo+eo,0-N(0,2) (B.1)Consider random variableu=y0— (B.2)The is defined as following Equation:= y + (xo-N(a + bx0, [ + )2]) (B.3)wheren is the number of samplesis 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 methodThen:E(u) = E(Y0)— E(0) = 0 (B.4)Yo and are normally distribution variables and independent to each other, sinceD(u) = D(y0— o) = D(yo) + D(0)D(yo) = (B.5)=[1 + + 2]o2we get= [1 + + —o)2- 2 (B.6)n Z_1m(x—x)Appendix B. Definition of Confident Interval 126Equation B.6 can be expressed as:N(0, 1) (B.7)o-uThe unbiased estimator, &2, of square deviation from Least Square has the freedomof n — 2.= m— 2— (B.8)Then, the following Equation is derived.(m_2)&2(n-2) (B.9)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 + -+ Z( )2 -2) (B.11)A 100(1-7) percent confident interval about the regression line for each value x0 isdefined 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 regression line is located. The band of confident interval is defined by Equation B.14.(B.14)I Y2(X) = + 6(xo)Appendix CPredicted Blow Count with Different Confidence127Appendix C. Predicted Blow Count with Different Confidence 128Table C.2: Predicted Blow Counts Based on Confident Interval for UBC Pile 5Depth qea Rsr (kips) Blow Counts (blows/foot) Measured(feet) (bars) (kips) Lower Reg. Line Upper Lower Reg. Line Upper Blow CountConfident = 90%27 2.98 13.35 17.3 14.7 13.4 1.4 1.1 0.9 149 20.65 23.98 34.3 28.2 24.2 3.0 2.4 1.9 356 72.10 37.40 72.0 56.7 46.2 7.0 5.5 4.3 561 71.64 45.26 87.0 68.6 55.9 8.2 6.3 5.0 665 106.04 53.63 137.6 99.4 78.9 12.9 9.1 7.1 1069 106.27 64.39 165.1 119.2 94.7 15.1 10.8 8.1 1177 147.68 81.81 340.9 209.8 151.5 47.3 19.5 13.2 1879 132.70 87.71 302.4 199.3 148.7 35.7 18.0 12.7 1882 75.28 94.01 184.3 144.7 119.0 16.0 12.1 9.4 1288 107.14 108.46 278.1 204.6 159.5 29.1 17.8 13.0 2090 134.62 113.92 392.8 258.9 194.6 68.2 25.3 16.6 1997 21.21 121.11 173.0 143.5 122.3 14.3 11.4 9.0 12Confident =_80%______27 2.98 13.35 16,7 14.7 13.4 1.4 1.1 1.0 149 20.65 23.98 32.4 28.2 25.0 2.8 2.4 2.0 356 72.10 37.40 68.0 56.7 48.6 6.6 5.5 4.6 561 71.64 45.26 82.3 68.6 58.0 7.7 6.3 5.3 665 106.04 53.63 124.8 99.4 82.5 11.7 9.1 7.4 1069 106.27 64.39 153.3 119.2 99.1 14.0 10.8 8.6 1177 147.68 81.81 303.0 209.8 163.6 36.2 19.5 14.3 1879 132.70 87.71 365.8 199.3 156.6 27.9 18.0 13.5 1882 75.28 94.01 174.1 144.7 123.7 15.0 12.1 9.9 1288 107.14 108.46 258.2 204.6 166.9 25.4 17.8 13.7 2090 134.62 113.92 356.0 258.9 207.1 50.6 25.3 17.8 1997 21.21 121.11 165.9 143.5 126.2 13.6 11.4 9.5 12Confident = 70%27 2.98 13.35 16.3 14.7 13.4 1.3 1.1 0.9 149 20.65 23.98 31.7 28.2 25.5 2.7 2.4 2.1 356 72.10 37.40 65.6 56.7 49.9 6.3 5.5 4.8 561 71.64 45.26 79.4 68.6 60.4 7.5 6.3 5.5 665 106.04 j 53.63 119.2 99.4 85.2 11.2 9.1 7.7 10Appendix C. Predicted Blow Count with Different Confidence 129Table C.2: Predicted Blow Counts Based on Confident Interval for UBC Pile 5 ContinuousDepth q Rsr (kips) Blow Counts (blows/foot) Measured(feet) (bars) (kips) Lower Reg. Line Upper Lower Reg. Line Upper Blow CountConfident = 70%69 106.27 64.39 143.0 119.2 102.2 13.0 10.8 8.9 1177 147.68 81.81 282.1 209.8 170.4 31.4 19.5 15.0 1879 132.70 87.71 250.6 199.3 165.5 25.2 18.0 14.3 1882 75.28 94.01 167.9 144.7 127.Q 14.3 12.1 10.3 1288 107.14 108.46 246.5 204.6 172.2 23.5 17.8 14.2 2090 134.62 113.92 335.1 258.9 214.9 43.2 1.3.2 18.8 1997 21.21 121.11 161.5 143.5 128.8 13.2 11.4 9.8 12Confident = 60%27 2.98 13.35 15.9 14.7 13.4 1.2 1.1 0.8 149 20.65 23.98 31.1 28.2 26.1 2.7 2.4 2.1 356 72.10 37.40 63.4 56.7 50.5 6.1 5.5 4.8 561 71.64 45.26 76.7 68.6 61.2 7.2 6.3 5.6 665 106.04 53.63 116.6 99.4 88.0 11.0 9.1 7.9 1069 106.27 64.39 140.0 119.2 105.6 12.7 10.8 9.3 1177 147.68 81.81 263.9 209.8 177.9 27.8 19.5 15.8 1879 132.70 87.71 237.1 199.3 168.7 23.1 18.0 14.6 1882 75.28 94.01 162.1 144.7 130.6 13.8 12.1 10.7 1288 107.14 108.46 235.8 204.6 177.8 21.9 17.8 14.8 2090 134.62 113.92 316.4 258.9 223.4 37.7 25.3 19.9 1997 21.21 121.11 157.3 143.5 131.6 12.8 11.4 10.2 12Confident 50%27 2.98 13.35 15.7 14.7 13.7 1.1 1.1 0.7 149 20.65 23.98 30.4 28.2 26.4 2.5 2.4 2.1 356 72.10 37.40 62.3 56.7 51.9 6.0 5.5 5.0 561 71.64 45.26 75.4 68.6 62.9 7.1 6.3 5.8 665 106.04 53.63 111.8 99.4 89.4 10.5 9.1 8.1 1069 106.27 64.39 134.1 119.2 107.3 12.2 10.8 9.5 11‘77 147.68 81.81 247.9 209.8 181.8 25.0 19.5 16.2 1879 132.70 87.71 230.8 199.3 175.4 22.1 18.0 15.3 1882 75.28 94.01 159.3 144.7 132.4 13.5 12.1 10.9 1288 107.14 108.46 230.8 204.6 183.8 21.2 17.8 15.4 2090 134.62 113.92 307.9 258.9 227.8 35.5 25.3 20.5 1997 21.21 121.11 153.3 143.5 133.1 12.4 11.4 10.3 12Appendix C. Predicted Blow Count with Different Confidence 130Table C.3: Predicted Blow Counts Based on Confident Interval for UBC Pile 6Depth ea Rsr L (kips) Blow Counts (blows/foot) Measured(feet) (bars) (kips) J Lower I Reg. Line Upper Lower Reg. line Upper Blow CountConfident = 90%27 2.95 38.60 47.3 40.2 38.6 3.3 2.7 2.6 348 20.97 57.40 76.5 64.5 57.4 6.8 5.7 5.0 560 74.83 95.57 180.3 140.5 116.6 23.0 16.1 12.9 2162 82.02 103.09 202.1 158.6 128.9 25.0 20.5 14.1 1966 110.82 121.56 311.7 225.1 178.8 30.0 21.3 17.6 2080 109.50 190.75 476.9 353.2 276.5 53.0 31.4 22.9 2989- 125.03 231.36 680.5 482.0 367.2 134.5 49.9 30.5 43102 21.41 273.06 369.0 306.8 273.1 32.4 25.0- 21.7- 25Confident = 80%27 2.95 38.60 45.0 40.2 38.6 3.1 2.7 2.6 348 20.97 57.40 73.6 64.5 57.4 6.5 5.7 5.0 560 74.83 95.57 167.7 140.5 121.0 21.8 16.1 13.4 2162 82.02 103.09 190.9 158.6 135.7 23.8 20.5 15.0 1966 110.82 121.56 289.4 225.1 187.0 27.5 21.3 18.2 2080 109.50 190.75 443.6 353.2 289.0 45.9 31.4 24.1 2989 125.03 231.36 625.3 482.0 385.6 98.9 49.9 33.0 43102 21.41 273.06 350.1 306.8 273.1 30.0 25.0 21.7 25Confident 70%27 2.95 38.60 44.4 40.2 38.6 3.1 2.7 2.6 348 20.97 57.40 71.8 64.5 57.4 6.3 5.7 5.3 560 74.83 95.57 162.0 140.5 124.0 21.2 16.1 13.8 2162 82.02 103.09 185.5 158.6 139.3 23.1 20.5 15.5 1966 110.82 121.56 270.1 225.1 193.0 25.4 21.3 18.7 2080 109.50 190.75 423.9 353.2 298.1 42.2 31.4 25.0 2989 125.03 231.36 593.3 482.0 398.9 83.9 49.9 34.9 43102 21.41 273.06 341.3 306.8 278.6 28.9 25.0 22.2 25Confident = 60%27 2.95 38.60 43.3 40.2 38.6 3.0 2.7 2.6 348 20.97 57.40 70.0 64.5 57.4 6.2 5.7 5.3 560 74.83 95.57 156.7 140.5 127.4 20.7 16.1 14.3 2162 82.02 103.09 177.7 158.6 141.2 22.4 20.5 18.7 1966 110.82 121.56 264.3 225.1 199.3 24.9 21.3 19.2 2080 109.50 190.75 405.9 353.2 307.7 39.1 31.4 26.0 2989 125.03 231.36 564.3 482.0 413.1 72.8 49.9 37.0 43102 21.41 273.06 337.1 306.8 284.4 28.4 25.0 22.8 25Appendix C. Predicted Blow Count with Different Confidence 131Table C.3: Predicted Blow Counts Based on Confident Interval for UBC Pile 6 ContinuousDepth qea R8 (kips) Blow Counts (blows/foot) Measured(feet) (bars) (kips) Lower Reg. Line Upper Lower Reg. Line Upper Blow CountConfident = 50%27 2.95 38.60 42.9 40.2 38.6 3.0 2.7 2.6 348 20.97 57.40 69.2 64.5 60.4 6.1 5.7 5.3 560 74.83 95.57 154.1 140.5 129.2 20.5 16.1 14.5 2162 82.02 103.09 174.7 158.6 145.2 22.1 20.5 18.7 1966 110.82 121.56 253.3 225.1 202.6 23.8 21.3 19.4 2080 109.50 190.75 397.4 353.2 317.9 37.7 31.4 27.1 2989 125.03 231.36 550.9 482.0 428.4 68.3 49.9 39.5 43102 21.41 273.06 329.0 306.8 287.4 27.4 25.0 23.1 25Table C.4: Predicted Blow Counts Based on Confident Interval for Tilbury H PileDepth ea Rsr (kips) Blow Counts (blows/foot) Measured(feet) (bars) (kips) Lower Reg. Line Upper Lower Reg. Line Upper Blow CountConfident = 90%34 44.49 65.53 77.1 69.0 65.5 7.7 6.8 6.4 540 57.07 81.18 97.8 87.3 81.1 9.4 8.2 7.6 ‘760 162.35 159.68 238.3 207.4 185.7 27.9 20.5 16.8 2074 141.70 224.54 320.8 280.7 249.5 60.9 36.2 25.3 4275 150.74 228.36 331.0 289.1 259.5 69.6 39.7 27.8 3779 164.17 247.76 369.8 321.8 288.1 126.2 56.7 35.8 4484 122.43 270.43 370.5 325.8 294.0 113.2 55.5 36.2 46Confident = 70%34 44.49 65.53 73.6 69.0 65.5 7.3 6.8 6.4 540 57.07 81.18 93.3 87.3 82.0 8.9 8.2 7.7 760 162.35 159.68 224.9 207.4 192.4 24.2 20.5 17.8 2074 141.70 224.54 303.4 280.7 261.1 48.2 36.2 28.7 4275 150.74 228.36 312.8 289.1 268.7 53.9 39.7 31.0 3779 164.17 247.76 349.0 321.8 298.5 86.5 56.7 41.8 4484 122.43 270.43 351.2 325.8 303.9 81.3 55.5 42.1 46Appendix DInput and Output Data Sample of Application132Appendix D. Input and Output Data Sample of Application 133GRLWEAP: WAVE EQUATION ANALYSIS OF PILE FQ.JNDATIONS1987 VERSION 1.00EngLish UnitsTILBURY P!LE3 BLOW CJNT PREDICTION -- HAMMER MCCEL OF: 0 30-13 MADE BY: DELMAGELEMENT WEIGHT STIFFNESS COEFF. OF D-NL. CAP DAMPO(kips) (k/in) RESTITUTION ft (k/ft/s)1 2.2002 2.200 157707.9 1.000 .01003 2.200 157707.9 1.000 .0100IMP. BLK 1.200 96454.2 .900 .0100 -CAP/RAM 2.020 33390.0 .800 .0100 11.3HAMMER OPTIONS:HAMMER NO. FUEL SETTG. STROKE OPT. HAMMER TYPE DAMPNG-HAMR13 4 1 1 2HAMMER PERFORMANCE DATARAM WEIGHT RAM LENGTH MAX STROKE STROKE EFFICIENCY(kips) (in) (ft) (It)6.60 118.10 10.00 5.70 .720MAX PRESS. ACT PRESS. TIME DELAY COMP/EXPN V START INJ.(psi) (psi) Cs) (1n3)1254.0 914.0 .00050 1.350/1.250 .0THE HAMMER DATA INCLUDES ESTIMATED (NON-MEASURED) QUANTITIES VHAMMER CUSHION AREA E-MCCULUS THICKNESS STIFFNESS(fn2) (ksi) (in) (kips/in)238.50 280.0 2.000 33390.0INSITU RESEARCH 05/02/92 GRLWEAP TILBURY PILE3 BLOW CJNT PREDICTIONPILE PROFILE:L b Top Area E-Mod Spec Wt Wave S EA/c(ft) (ir2) (ksi) (Lb/ft3) (ft/s) (k/ft/s).00 146 30000. 492.000 16806.8 26.086.00 14.6 30000. 492.000 16806.8 26.0 VWave TraveL Time- 2L/c - = 10.234 meAppendix D. Input and Output Data Sample of Application 134No. Weight(kips)1 .2522 .2525 .252-6 .2527 .2528 .2529 .25210 .25211 .25212 .25213 .25214 .25215 252 -16 .25217 .252ToePiLe and SoiL ModeL for Rut =Stiffri C-Sk T-SLk C0R SoiL-S(k/in) (It) (ft) (kips)7205. .010 .000 .850 .07205. .000 .000 1.000 .07205. .000 .000 1.000 .17205. .000 .000 1.000 1.37205. .000 .000 1.000 3.17205. .000 .000 1.000 4.97205. .000 .000 1.000 6.77205. .000 .000 1.000 8.67205. .000 .000 1.000 10.27205. .000 .000 1.000 12.07205. .000 .000 1.000 13.77205. .000 .000 1.000 15.57205. .000 .000 1.000 17.37205. .000 .000 1.000 19.17205. .000 .000 1.000 20.8127.9261.0 kipsSoit-D Quake I. b Top Area(sift) (in) (ft) (in2)000 .100 5.06 14.6.000 .100 10.12 14.6.000 .100 25.29 14.6.050 .100 30.35 14.6.100 .100 35.41 14.6.100 .100 40.47 14.6.100 .100 45.53 14.6.100 .100 50.59 14.6.050 .100 55.65 14.6.050 .100 60.71 14.6.050 .100 65.76 14.6.050 ;100 7082 14.6.050 .100 75.88 14.6.050 .100 80.94 14.6.050 .100 86.00 14.6.150 .106(kips)1 .0, 0 405.2, 52 .0, 0 407.0, 53 .0, 0 408.5, 54 .0, 0 410.2, 55 .0, 0 411.6, 66 .0, 0 413.5, 67 .0, 0 414.7, 78 .0, 0 415.1, 79 .0, 0 412.7, 710 .0, 0 406.3, 711 .0, 0 395.8, 812 .0, 0 388.6, 813 .0, 0 378.6, 814 .0, 0 367.0, 915 .0, 0 350.3, 916 .0, 0 324.4, 917 .0, 0 334.0, 10Rut BL Ct Stroke (ft)kips bpf down up261.0 29.5 5.7 4.8.00, 0 27.79, 5 13.69, 5 1.008, 16.00, 0 27.91, 5 13.71, 5 .973, 16.00, 0 28.02, 5 13.68, 5 .937, 15.00, 0 28.13, 5 13.61, 6 .903, 17.00, 0 28.23, 6 13.57, 6 .868, 17..00, 0 28.36, 6 13.42, 6 .832, 17.00, 0 28.44, 7 13.14, 7 .797, 17.00, 0 28.47, 7 12.83, 7 .763, 18.00, 0 28.31, 7 12., 7 .729, 18.00, 0 27.86, 7 12.01, 8 .696, 18.00, 0 27.15, 8 11.67, 8 .664, 18.00, 0 26.65, 8 11.30, 8 .634, 19.00, 0 25.96, 8 10.88, 9 .605, 19.00, 0 25.17, 9 10.53, 9 .578, 19.00, 0 24.02, 9 10.35, 9 .553, 19.00, 0 22.25, 9 10.00, 9 .531, 19.00, 0 22.91, 10 8.66, 10 .510, 19miii Str i,t max Str f,t ENTHRU BL Rtksi ksi kip-ft b/mm.00( 1, 0) 28.47( 8, 7) 21.5 50.9ECHO PRINT OF INPUT DATAPrLE OPTIONS: -N/UNIFORM AUTO S.C. SPLICES DAMPNG-P D-P VALUE(k/f tls)0 0 0 1 .521SOIL OPTIONS:X SKIN FR % END 80 DIS. NO. S DAMPING51 49 1 SMITH1ANALYSIS/OUTPUT OPTIONS:!TERATNS DTCR/DT() RES STRESS lOUT AUTO SGMNT OUTPT INCR MAX T(rns)0 160 0 10 0 2 0Rut= 261.0, Rtoe= 127.9 kips, Time hnc.= .119 insNo. miii F, t max F, t miii Str, t max Str, t max V, t max 0, t(kips) (ksi) (ksi) (ft/s) (in)
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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 |
FileFormat | 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 |
GraduationDate | 1992-11 |
Campus |
UBCV |
Scholarly Level | Graduate |
AggregatedSourceRepository | DSpace |
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