ASCE GSP 282-2017 Keynote Lectures.pdf

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1、Edited byD. V. Griffiths, Ph.D., P.E., D.GE Gordon A. Fenton, Ph.D., P.Eng Jinsong Huang, Ph.D. Limin Zhang, Ph.D.Geo-Risk 2017Keynote LecturesSelected Papers from the Proceedings of Geo-Risk 2017GSP 282GEOTECHNICAL SPECIAL PUBLICATION NO. 282 GEO-RISK 2017 KEYNOTE LECTURES SELECTED PAPERS FROM SESS

2、IONS OF GEO-RISK 2017 June 47, 2017 Denver, Colorado SPONSORED BY Geo-Institute of the American Society of Civil Engineers EDITED BY D. V. Griffiths, Ph.D., P.E., D.GE Gordon A. Fenton, Ph.D., P.Eng. Jinsong Huang, Ph.D. Limin Zhang, Ph.D. Published by the American Society of Civil Engineers Publish

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9、ght 2017 by the American Society of Civil Engineers. All Rights Reserved. ISBN 978-0-7844-8069-4 (PDF) Manufactured in the United States of America. Preface Interest and use of probabilistic methods and risk assessment tools in geotechnical engineering has grown rapidly in recent years. The natural

10、variability of soil and rock properties, combined with a frequent lack of high quality site data, makes a probabilistic approach to geotechnical design a logical and scientific way of managing both technical and economic risk. The burgeoning field of geotechnical risk assessment is evidenced by nume

11、rous publications, textbooks, dedicated journals and sessions at general geotechnical conferences. Risk assessments are increasingly becoming a requirement in many large engineering construction projects. Probabilistic methods are also recognized in design codes as a way of delivering reasonable loa

12、d and resistance factors (LRFD) to target allowable risk levels in geotechnical design. This Geotechnical Special Publication (GSP), coming out of the Geo-Risk 2017 specialty conference held in Denver, Colorado from June 4-7, 2017, presents eight outstanding contributions from the keynote speakers.

13、Four of the contributions are from practitioners and the other four are from academics, but they are all motivated by a desire to promote the use of risk assessment and probabilistic methodologies in geotechnical engineering practice. Honor Lectures are presented by Greg Baecher (Suzanne Lacasse Lec

14、turer) on Bayesian thinking in geotechnical engineering and Gordon Fenton (Wilson Tang Lecturer) on future directions in reliability based design. The reliability-based design theme is continued by Dennis Becker who includes discussion of risk management, and Brian Simpson, who focuses on aspects of

15、 Eurocode 7 and the rapidly growing importance of robustness in engineering design. The evolution and importance of risk assessment tools in dam safety is covered in lectures by John France and Jennifer Williams, and Steven Vick. The challenges of liquefaction modeling and the associated risks of pr

16、oblems due to instability and deformations are covered in lectures by Hsein Juang and Armin Stuedlein. These contributions to the use of risk assessment methodologies in geotechnical practice are very timely, and will provide a valuable and lasting reference for practitioners and academics alike. Al

17、l the papers in this GSP went through a rigorous review process. The contributions of the reviewers are much appreciated. The Editors D.V. Griffiths, Ph.D., P.E., D.GE, F.ASCE, Colorado School of Mines, Golden, CO, USA Gordon A. Fenton, Ph.D., P.Eng., FEIC, FCAE, M.ASCE, Dalhousie University, Halifa

18、x, Canada Jinsong Huang, Ph.D., M.ASCE, University of Newcastle, NSW, Australia Limin Zhang, Ph.D., F.ASCE, Hong Kong University of Science and Technology, PR China *HR5LVN*63 LLL$6 Jack Montgomery, Auburn University Sponsorships and Exhibits Chair: Armin Stuedlein, Oregon State University The Edito

19、rs greatly appreciate the work of Ms. Helen Cook, Ms. Leanne Shroeder, Ms. Brandi Steeves, and Mr. Drew Caracciolo of the ASCE Geo-Institute for their administration of many important conference organizational issues, including management of the on-line paper submissions, the conference web site and

20、 sponsorship. *HR5LVN*63 LY$6 they require Bayesian thinking. Bayesian thinking is that of judgment and belief. It leads to remarkably strong inferences from even sparse data. Most geotechnical engineers are in-tuitive Bayesians whether they know it or not, and have much to gain from a more formal u

21、nder-standing of the logic behind these straightforward and relatively simple methods. BAYESIAN THINKING Most geotechnical engineers are intuitive Bayesians. Practical examples of Bayesian thinking in site characterization, dam safety, data analysis, and reliability are common in practice; and the e

22、mblematic observational approach of Terzaghi is a pure Bayesian concept although in a quali-tative form (Lacasse 2016). The statistics course one took in college most likely introduced a peculiar and narrow form of statistics, generally known as Relative Frequentist theory or Sampling Theory statist

23、ics. In the way normal statistics courses are taught, one is led to believe that this is all there is to sta-tistics. That is not the case. As one of the reviewers of this paper said, its not your fault if you havent thought about Bayesian methods until now; and its not too late. This traditional fr

24、equentist form of statistical thinking is not particularly useful except in narrowly defined problems of the sort one finds in big science, like medical trials, or in sociolog-ical surveys like the US Census. It is tailored to problems for which data have been acquired through a carefully planned an

25、d randomized set of trials. It is tailored to aleatory uncertainties, that is, uncertainty dealing with variations in nature. This almost never describes the problems a normal person faces, and especially not geotechnical engineers. Most geotechnical uncertainties are epistemic: they deal with limit

26、ed knowledge, with uncertainties in the mind not variations in nature. Two concepts of probability. The reason that college statistics courses deal with this peculiar form of statistics and not something more useful in daily life has to do with intellectual battles in the history of probability, and

27、 in how the pedagogy of statistical teaching evolved in the early 20thC. Even though concepts of uncertainty, inference, and induction arose in antiquity, what we *HR5LVN*63 $6 but the probability that you as a unique individual are sick if a diagnostic test comes back positive is not a matter of fr

28、equencies, it is a matter of one unique individual, namely, you. You are either sick or well. Probability in this case is a matter of the degree of belief about which of those two conditions you think ob-tains. Vick (2002) interprets this theory of degrees-of-belief as a formalization of “engineerin

29、g judgment.” Scope of this paper. This paper focusses on inferences which at first glance seem difficult or impossible to makeand indeed they are, using frequentist thinking. But they are easy when viewed through the lens of Bayesian thinking. Bayesian methods have been used across the spec-trum of

30、geotechnical applications since the 1970s, as reflected in the early work of Tang (Lacasse et al. 2013), Wu (2011), Einstein (Einstein et al. 1978), Marr (2011), and many others. These methods have revolutionized many fields of engineering and continue to do so (McGrayne 2012). “Clippy” the annoying

31、 Microsoft self-help wizard was a Bayesian app. Spam filtering of your email inbox is, too. The Enigma Code of the German Kriegsmarine was broken using Bayesian methods at Bletchley Park. And the wreckage of Air France flight 447 was found using a Bayesian search algorithm. Recent reviews of the use

32、 of Bayesian methods in geotechnical en-gineering have been provided by Yu Wang (2016), Zhang (2016), and Juang and Zhang (2017). For reasons of space and to avoid complicating the message, advanced topics in Bayesian methods such as belief nets and Markov-chain Monte-Carlo are not discussed here. L

33、EARNING FROM EXPERIENCE The application of statistics to practical problems is of two sorts. On the one hand, we use statis-tics to describe the variability of data using summaries such as measures of central tendency and spread, or frequency distributions such as histograms or probability density f

34、unctions. On the other hand, we use statistics to infer probabilities over properties of a population that we have not observed and based on a limited sample that we have observed. It is this latter meaning of statistics that we deal with here. It is the inductive use of statistics, which in the 19t

35、hC. was called inverse reasoning. *HR5LVN*63 $6 Peck 1969). Bayesian methods, however, allow a quantitative updating of model uncertainty, parameter values, and predictions based on quantitative measurements of performance. So, anyone using an observa-tional approach is, de facto, applying Bayesian

36、methods. Staged construction example. Ladd (Baecher and Ladd 1997; Noiray 1982) performed a relia-bility assessment of the staged loading of a limestone aggregate storage embankment on soft Gulf of Mexico Clay at a cement plant in Alabama (Figure 2) using the SHANSEP method (Ladd and Foott 1974). Th

37、e embankment was loaded in stages to allow consolidation and an in-crease in the undrained strength of the clay stratum. It was incrementally raised to a final height of 55 ft (17 m). Extensive in situ and laboratory testing allowed a formal observation approach to be used, in which uncertainty anal

38、ysis of the preliminary design was combined with field moni-toring to update both soil engineering parameters and site conditions (maximum past pressures). Bayesian methods were used to modify initial calculations, and thus to update predictions of per-formance for later stages of raising the embank

39、ment. The site abuts a ship channel and a gantry crane unloads limestone ore from barges moored at a relieving platform. The ore is placed in a reserve storage area adjacent to the chan-nel. As the site is underlain by thick deposits of medium to soft plastic deltaic clay, concrete pile foundations

40、had been used to support earlier facilities at the plant. Although the clay underlying the site was too weak to support the planned stockpile, the cost of a pile supported mat founda-tion for the storage area was prohibitive. To allow construction, a foundation stabilization scheme was conceived in

41、which lime-stone ore would be placed in stages, leading to consolidation and strengthening of the clay, has-*HR5LVN*63 $6 (b) investigate stability for various loading condi-tions; and (c) predict vertical and lateral deformations as a function of fill height and consolida-tion. To do so, the uncert

42、ainty in soil property estimates was divided into two parts: that caused by data scatter, and that caused by systematic error. The data scatter was due to real spatial v-ariability in the soil deposit, plus that due to random measurement noise. The systematic error was due to statistical estimation

43、error in the average soil properties, plus that due to measurement or model biases. The data scatter (aleatory) and systematic (epistemic) uncertainties were combined by adding the variances of the (nearly) independent contributions, in which null is the respective soil engineering property (Baecher

44、 and Christian 2008): null(null) nullnullnullnullnull nullnullnullnullnullnullnull(null)+nullnullnullnullnullnullnullnullnullnullnull(null) (5a)null(null) nullnullnullnullnullnullnullnull(null)+nullnullnullnullnullnullnull(null)+nullnullnullnullnullnullnullnullnullnullnullnull(null)+nullnullnullnull

45、null(null) (5b)The variance components are those due to spatial variation, random error, statistical estimation error, and model or measurement bias, respectively. Application of SHANSEP. An expected maximum vertical past pressureprofile was obtained by linear regression of the measurements. The obs

46、erved data scatter about the expected value profile was assumed to reflect spatial variability of the clay plus random measurement error in the test data. The data scatter causes statistical estimation error in the location of the expected value because of the limited number of tests. *HR5LVN*63 $6

47、the east boundary was free to move vertically. The base case parameters consisted of initial in situ stresses, undrained shear strength properties, stress-strain relationships, and pore pressure parameters. The outputs of FEM analyses consisted of undrained deformations at each node and stresses at

48、the center of each element of the grid for fill heights Hf =6.2, 9.2, and 10.8 m (20, 30, and 35 ft). The displacement magnitude was less than expected considering that the stability fac-tors of safety were close to 1.0. Moreover, the analyses predicted almost no yielding within the foundation clay.

49、 Bayes Rule (Eq. (1) was used to update initial estimates of soil engineering properties in light of the field monitoring results, given that the pile was raised to 35 ft (10.8 m) without in-cident. This involved a reverse solution to the stability model. The updated soil parameters were re-entered into the analysis to