ASCE GSP 294-2018 INSTALLATION TESTING AND ANALYSIS OF DEEP FOUNDATIONS.pdf

1、GEOTECHNICAL SPECIAL PUBLICATION NO. 294 IFCEE 2018 INSTALLATION, TESTING, AND ANALYSIS OF DEEP FOUNDATIONS SELECTED PAPERS FROM SESSIONS OF THE INTERNATIONAL FOUNDATION CONGRESS AND EQUIPMENT EXPO 2018 March 510, 2018 Orlando, Florida SPONSORED BY International Association of Foundation Drilling De

2、ep Foundations Institute Pile Driving Contractors Association The Geo-Institute of the American Society of Civil Engineers EDITED BY Muhannad T. Suleiman, Ph.D. Anne Lemnitzer, Ph.D. Armin W. Stuedlein, Ph.D., P.E. Published by the American Society of Civil Engineers Published by American Society of

3、 Civil Engineers 1801 Alexander Bell Drive Reston, Virginia, 20191-4382 www.asce.org/publications | ascelibrary.org Any statements expressed in these materials are those of the individual authors and do not necessarily represent the views of ASCE, which takes no responsibility for any statement made

4、 herein. No reference made in this publication to any specific method, product, process, or service constitutes or implies an endorsement, recommendation, or warranty thereof by ASCE. The materials are for general information only and do not represent a standard of ASCE, nor are they intended as a r

5、eference in purchase specifications, contracts, regulations, statutes, or any other legal document. ASCE makes no representation or warranty of any kind, whether express or implied, concerning the accuracy, completeness, suitability, or utility of any information, apparatus, product, or process disc

6、ussed in this publication, and assumes no liability therefor. The information contained in these materials should not be used without first securing competent advice with respect to its suitability for any general or specific application. Anyone utilizing such information assumes all liability arisi

7、ng from such use, including but not limited to infringement of any patent or patents. ASCE and American Society of Civil EngineersRegistered in U.S. Patent and Trademark Office. Photocopies and permissions. Permission to photocopy or reproduce material from ASCE publications can be requested by send

8、ing an e-mail to permissionsasce.org or by locating a title in ASCEs Civil Engineering Database (http:/cedb.asce.org) or ASCE Library (http:/ascelibrary.org) and using the “Permissions” link. Errata: Errata, if any, can be found at https:/doi.org/10.1061/9780784481578 Copyright 2018 by the American

9、Society of Civil Engineers. All Rights Reserved. ISBN 978-0-7844-8157-8 (PDF) Manufactured in the United States of America. Preface This is the first volume of six Geotechnical Special Publications (GSPs) and one Geotechnical Practice Publication (GPP) containing papers from the 2018 International F

10、oundations Congress and Equipment Expo (IFCEE18) held in Orlando, Florida on March 510, 2018. The IFCEE conference series combines a technical conference and equipment show dedicated to the design and construction of foundation systems, using the latest geo-engineering and geo-construction technolog

11、ies and practices. The IFCEE conference series is a one of a kind event that attracts attendees from around the world for the worlds largest equipment exposition dedicated solely to the deep foundations industry. This Congress combined the 2018 annual meetings of ASCEs Geo-Institute, the Internation

12、al Association of Foundation Drilling (ADSC), the Pile Driving Contractors Association (PDCA) and the Deep Foundations Institute (DFI). This event was the third Congress in the IFCEE conference series, following the successful 2009 and 2015 meetings, in which these leading geotechnical and geotechni

13、cal-related organizations joined together for a single and singular annual congress. IFCEE18 provided an international forum to discuss technological advances, case histories, and present challenges related to geotechnical and foundation engineering. The congress was attended by a wide range of geo-

14、professionals including engineers, contractors, academicians, equipment manufacturers, geo-technologists, researchers, and service, material and tooling suppliers. This publication culminates two years of effort by the technical planning committee whose focus has been to continue the success of the

15、previous meetings in the IFCEE conference series. Many individuals are responsible for the content of this volume, all of whom served in the efforts to maintain the standard set by previous proceedings. An international call for papers and a rigorous peer review process yielded 280 accepted technica

16、l papers, that were presented in 47 sessions, in addition to invited keynote presentations. Papers were reviewed in accordance with ASCE GSP standards. Accordingly, each paper was subjected to technical review by two or more independent peer reviewers. Publication requires concurrence by at least tw

17、o peer reviewers. The Editors would like to express their appreciation for having been provided the opportunity to be a part of this Congress organization, their sincere thanks to the numerous session chairs and reviewers, and we hope that these proceedings will be of use to the geotechnical enginee

18、ring community for many years to come. The Editors, Muhannad T. Suleiman, Ph.D., A.M.ASCE, M.DFI, Lehigh University Anne Lemnitzer, Ph.D., A.M.ASCE, M.DFI, University of California, Irvine Armin W. Stuedlein, Ph.D., P.E., M.ASCE, M.DFI, Oregon State University ,) Elizabeth M. Smith, P.E., G.E., D.GE

19、, Terracon Consultants, Inc.; James W. Niehoff, P.E., M.ASCE, GEI Consultants, Inc. Field Testing: Axial/Lateral I Gerald Verbeek, M.ASCE, Verbeek Management Services; John P. Turner, Ph.D., P.E., D.GE, M.ASCE, Dan Brown and Associates, PC; Murad Y. Abu-Farsakh, Ph.D., P.E., M.ASCE, Louisiana State

20、University ,) Thomas W. Pennington, P.E., M.ASCE, Jacobs Associates Ground Improvement Jason DeJong, Ph.D., University of California, Davis; Kenichi Soga, Ph.D., FREng, FICE, M.ASCE, University of California, Berkeley Geosynthetic/Fiber Reinforcement Ben A. Leshchinsky, Ph.D., A.M.ASCE, Oregon State

21、 University Ground Improvement: Treatment Case Studies Christian B. Woods, P.E., D.GE, G.E., M.ASCE, Densification, Inc. Liquefaction and Densification Menzer Pehlivan, Ph.D., P.E., M.ASCE, CH2M HILL Retaining and Cutoff Wall Design and Construction Kenneth L. Fishman, Ph.D., P.E., M.ASCE, McMahon N

22、asser Massoudi, Ph.D., P.E., M.ASCE, Bechtel Corp. Stone Columns/Piers/Grouting I Kord J. Wissmann, Ph.D., P.E., D.GE, M.ASCE, Geopier Foundation Company; Jie Han, Ph.D., P.E., F.ASCE, The University of Kansas ,) John S. McCartney, Ph.D., P.E., M.ASCE, University of California, San Diego Bridges: Fo

23、undation Design and Construction Sam Sternberg, III, P.E., M.ASCE, Thompson Engineering Characterizing the Behavior of Soils Cumaraswamy (Vipu) Vipulanandan, Ph.D., P.E., M.ASCE, University of Houston; Yazen Khasawneh, Ph.D., P.E., M.ASCE, NTH Consultants, Ltd. Liquefaction: Analysis and Design C. Y

24、oga Chandran, Ph.D., G.E., P.E., M.ASCE, CH2M HILL QA/QC for Deep Foundations Anna Sellountou, Ph.D., A.M.ASCE, Pile Dynamics, Inc. Rock Mechanics Ingrid Tomac, Ph.D., A.M.ASCE, University of California, San Diego; Ehsan Ghazanfari, Ph.D., P.E., M.ASCE, University of Vermont Site Characterization Xi

25、ong (Bill) Yu, Ph.D., P.E., F.ASCE, Case Western University Other Topics in Geotechnical Engineering Constitutive Modeling Usama S. El Shamy, Ph.D., P.E., M.ASCE, Southern Methodist University; Seung Jae Lee, Ph.D., Aff.M.ASCE, Florida International University Pavements and Subgrades Boo Hyun Nam, P

26、h.D., A.M.ASCE, University of Central Florida Shallow Foundations Xiong Zhang, Ph.D., P.E., A.M.ASCE, Missouri University of Science and Technology Slopes, Dams, Embankments Timothy D. Stark, Ph.D., P.E., D.GE, F.ASCE, University of Illinois at Urbana-Champaign; Binod Tiwari, Ph.E., P.E., M.ASCE, Ca

27、lifornia State University, Fullerton; Beena Ajmera, Ph.D., A.M.ASCE, California State University, Fullerton ,) Rifat Bulut, Ph.D., M.ASCE, Oklahoma State University Selected Other Topics in Geotechnical Engineering Matteo Montesi, P.E., M.ASCE, WSP USA; Curt R. Basnett, P.E., M.ASCE. CH2M HILL; Morg

28、an Race, Ph.D., P.E., M.ASCE, Braun Intertec; Kam Weng Ng, Ph.D., P.E., M.ASCE, University of Wyoming; Lori A. Simpson, G.E., P.E., M.ASCE, Langan Treadwell Rollo Case Histories, Lessons Learned and General Practice ACIP Piles: Case Histories and Lessons Learned W. Morgan NeSmith, P.E., M.ASCE, Berk

29、el Phoon and Kulhawy 2008; Haldar and Babu 2008; Misra and Roberts 2006, 2009; Zhang et al. 2005). Service limit state design of drilled shaft foundation usually involves vertical settlement. Several methods can be used to determine the vertical shaft head displacement, among which the load transfer

30、, or t-z method, is one of the more rigorous methods (Brown 2010, ONeil and Reese 1999, Misra and Roberts 2009). Application of the t-z method requires load transfer models to predict mobilization of resistance along the shaft and at the tip of the shaft. The load transfer models are in the format o

31、f normalized unit resistance versus normalized displacement. Currently there are no side or tip load transfer functions for drilled shafts in shales (cohesive intermediate geo materials). In order for load transfer models to be utilized in probabilistic analysis or computational calculations, not on

32、ly the availability of the functional models but also the quantified variability and uncertainty of the models are needed. The variability of the load transfer models proposed by ONeil and Reese (1999) are not quantified, and, to the best of our knowledge, there are no others available in literature

33、. Furthermore, for probabilistic analyses with the use of random number ,) the SDility of load tnts, the smaleing correctpproach is ret Square vergression anighted least the data exng and Tangession weige equal to 1The advantfficient of vaimulate ranare incorpy is to finvariability variability ign.

34、fer of side easurementsd shafts eml data were odel paramele 1. ed for moderession Anats “collectiv(i.e., load te collectiveansfer measue respective deviation (SDs one best fin, average vload transfeis small becaransfer for all SD would ply reflected. presented bysus Weightealyses weresquare regrehib

35、it consta, 2004), whhts that arenull, where nullage of usingriation (COVdom side aorated. Suchd functionaand uncertafor probabiand tip refrom a relabedded in fit using foueters and noling load tralyses: Regreely” as well ransfer meae set of availrements frommodel from). On the ott load transfevalues

36、 of beer model. Frouse little scatll data were rerevent the vaTherefore, ththe variabilitd Least Squae performed:ssion (WLSnt conditionich is comme inversely pis the valuethis type o). nd tip loada methodl models tointy, to simlistic analyssistance wetively largeshales in thr different frmalized axn

37、sfer relatiossion analyseas on each shsurements folable measureall shaft segthe collecther hand, a rfr model for st fit paramm this approter exists witpresented byriability ande variabilityy of the modre Regressionon-weigh). NLS real standardonly exhibiroportional of the predf weights istransfer mis

38、 not currreflect the ulate the moes of indivre developedatabase ofe central Uunctional foial displacemnships. s were perfoaft segmentr a single ments produments of allive approacegression anthe shaft segeters for all ach, a small hin the data othe average uncertainty of the respeels parameten: Two t

39、ypted least sqgression is bdeviation ted in manyto the magniction for a gthe convenodels ently load dels, idual d by full-nited rms; ent. rmed s load shaft ces a tests. h are alysis ment shaft SD is f one value of the ctive rs. es of uare ased (SD) data itude iven ience ,) infinity slope at origin;

40、high RMSE. WLS null = 0.84 nullnull.nullnull0.5 null = 0.48nullnull.nullnull0.4 Exponential NLS null=1exp(4.057z) 0.19 null = 1nullnullnull(0.78null) 0.16 Fits data well when displacement is large; traces the upper bound well, but high RMSE. WLS null=1exp(9.67z) 1.41 null=1 nullnullnull(2.55null) 0.

41、82 Logarithm NLS null = 0.15ln(z) + 0.79 0.19 null = 0.13ln(w) +0.52 = 0.15 Overpredicts side resistance side resistance data, and underpredicts tip resistance, relatively high RMSE. WLS null= 0.09ln(z) + 0.67 0.48 null = 0.08nullnull(null) + 0.44 0.46 Hyperbolic NLS null=null1.07null+0.130.17 null=

42、null1.1null +0.720.14 Well reflects data and their variability, low RMSE. WLS null=null1.07null+0.130.62 null=null1.12null +0.690.76 Power function: the model trends and the 68.2% confidence bounds for new observations ( null|nullbounds) are imposed with the field test data in Fig. 1. The analyses p

43、roduced the fitting parameters n as the powers of 0.22 and 0.34, which is less than 1.0. The initial slope of the power function is infinite, which may pose numerical problems in modeling load transfer. The model trends fit the data well at normalized displacements of less than about 4% (NLS) and 2%

44、 (WLS). However, the model trends overestimate the mobilized unit side resistance at greater normalized displacements, which exposes the major disadvantages of the function. The null|nullbounds from NLS regression analyses are “parallel” to the model trend, and the vertical distance from a bound to

45、the model trend is constant and equal to the models RMSE. In contrast, the bounds from WLS regression deviate from the model trend starting from the point of origin, showing a ,) however, he displaces the observster for the MSE of 0.4ounds encoS and WLigure 2), aithout a bouderestimateis shaft died

46、to that frion fitting: s normalizedk the load the data ponds obtainesing 68% ofuld not be tregressionthe model oment exceeded load trawhole range8 (comparempass mosS regressiond both annd and is pthe variabilameter (Figom NLS of displacemetransfer datints are clud from WLSthe data pohe case. reflect

47、s theverpredictss 4% of thnsfer data, of displaced to an RMt data and n analyses alyses yielroblematicity of data w. 1c and d). 0.20. nts. a well whestered; thenanalysis apints, the bodata well wfuture outce shaft diamwhere the mments. HowMSE of 0.19 overpredicgave the mded exact . The hen WLS n the

48、 , the pear unds hen omes eter. odel ever, from t the odel same ,) the ms. normalizedoad transfereasured vaervations fo). The functin Table 2ility of theD. WhenWLS regreNLS regree resistancedrilled shaerbolic funr side and tissues such odels also of predictiors have phyand b paramof the colleside an

49、d 0.1hese valuesitting with % of , the odel unit q-w lue), r tip ional . The data the ssion ssion , the fts in ction p are as an give ns is sical eter ctive 4 for give ,) that of the collective approach is represented by its conditional SD, while that of the individual approach is represented by the COVs of the fitting parameters. ,) theted and covacorrelatednullnull_nullnullnull_nullnuller a and varelation coege sets of che random pair of ran) to obtain ased to genual Measurtransfer damodels fro(Eqs. 1 ands points