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This report provides a tutorial introduction to practical techniques of statistical data analysis for geotechnical engineering--the intended audience is the practicing geotechnical engineer with little or no background in statistics. The approach summarizes soil parameters by two numbers: a best estimate and a measure of uncertainty. Uncertainties in soil parameter estimates of four types are considered--(a) variability in the soil deposit; (b) random measurement error; (c) measurement bias; and (d) limited numbers of tests. The first two cause data scatter. The second two cause systematic error. A procedure is developed to assess each source individually and then to combine them into a statistical soil profile. The statistical profile shows the best estimate of soil properties with depth and uncertainty envelopes about that profile. The statistical design profile is the first step in error analysis, as described in the accompanying report. AD-A186795. Keywords: Embankment dams; Risk assessment.
Risk and reliability analysis is an area of growing importance in geotechnical engineering, where many variables have to be considered. Statistics, reliability modeling and engineering judgement are employed together to develop risk and decision analyses for civil engineering systems. The resulting engineering models are used to make probabilistic predictions, which are applied to geotechnical problems. Reliability & Statistics in Geotechnical Engineering comprehensively covers the subject of risk and reliability in both practical and research terms * Includes extensive use of case studies * Presents topics not covered elsewhere--spatial variability and stochastic properties of geological materials * No comparable texts available Practicing engineers will find this an essential resource as will graduates in geotechnical engineering programmes.
Geotechnical and Geophysical Site Characterization collects the papers presented at the Third International Conference on Site Characterization (ISC 3) that took place in Taipei from April 1-4, 2008. The subjects covered include new developments in mechanical in-situ testing and interpretation techniques, statistical analysis of test data, geo
This report provides an introduction to practical techniques of error analysis for geotechnical engineering. The intended audience is the practicing geotechnical engineer with little background in probability or statistics. An analysis procedure is presented by which errors entering geotechnical calculations can be quantified and their effect on predictive accuracy evaluated. The approach involves three phases: (a) identification of potential sources of error; (b) assessment of the magnitude of each source; and (c) determination of the influence of each source on calculations. A companion report, provides related introductory material on the statistical analysis of geotechnical site-characterization data. Keywords: Engineering design; Geotechnical data; Sensitivity analysis.
Learn to use probabilistic techniques to solve problems in geotechnical engineering. The book reviews the statistical theories needed to develop the methodologies and interpret the results. Next, the authors explore probabilistic methods of analysis, such as the first order second moment method, the point estimate method, and random set theory. Examples and case histories guide you step by step in applying the techniques to particular problems.
A statistical analysis of geotechnical data from Ralston Valley, Nevada, is conducted with the objective of seeking relationships whereby near-surface-soil compressibility may be predicted within acceptable limits. Such relationships can be utilized in place of much of the extensive and costly boring, core sampling, and laboratory analysis that is required for design of such strategic structures as those for the MX Missile System. Possible predictors of compressibility which are investigated include site descriptors obtainable from maps, seismic surveys, bag samples from borings, and undisturbed core samples. Study findings suggest that site characteristics from maps, particle gradation variance, and soil porosity offer promise as predictors of compressibility at depths of ten feet.
This textbook systematically introduces the theories, methods, and algorithms for geotechnical reliability analysis. There are a lot of illustrative examples in the textbook such that readers can easily grasp the concepts and theories related to geotechnical reliability analysis. A unique feature of the textbook is that computer codes are also provided through carefully designed examples such that the methods and the algorithms described in the textbook can be easily understood. In addition, the computer codes are flexible and can be conveniently extended to analyze different types of realistic problems with little additional efforts.
This book presents a one-stop reference to the empirical correlations used extensively in geotechnical engineering. Empirical correlations play a key role in geotechnical engineering designs and analysis. Laboratory and in situ testing of soils can add significant cost to a civil engineering project. By using appropriate empirical correlations, it is possible to derive many design parameters, thus limiting our reliance on these soil tests. The authors have decades of experience in geotechnical engineering, as professional engineers or researchers. The objective of this book is to present a critical evaluation of a wide range of empirical correlations reported in the literature, along with typical values of soil parameters, in the light of their experience and knowledge. This book will be a one-stop-shop for the practising professionals, geotechnical researchers and academics looking for specific correlations for estimating certain geotechnical parameters. The empirical correlations in the forms of equations and charts and typical values are collated from extensive literature review, and from the authors' database.
The purpose of this study is to improve site investigation in geotechnical engineering via the evaluation and development of statistical approaches for characterizing the spatial variability of soil properties and the development of site investigation simulation software for educational use. : This study consists of four components: statistical characteristics, data measurement, simulation, and educational training. Statistical measures of spatial variability of soil properties were examined for three different geographical areas where soil formation processes differ to assess the influence on the spatial variability of soils. Statistical measures of spatial variability were also calculated for a case history where blasting was used as a method of soil improvement to evaluate the effects of man-made changes to soil structure.