Reliability and Statistics in Geotechnical Engineering

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Risk and reliability analysis is an area of growing importance ingeotechnical engineering, where many variables have to beconsidered. Statistics, reliability modeling and engineeringjudgement are employed together to develop risk and decisionanalyses for civil engineering systems. The resulting engineeringmodels are used to make probabilistic predictions, which areapplied to geotechnical problems. Reliability & Statistics in Geotechnical Engineeringcomprehensively covers the subject of risk and reliability in bothpractical and research terms * Includes extensive use of case studies * Presents topics not covered elsewhere--spatial variability andstochastic properties of geological materials * No comparable texts available Practicing engineers will find this an essential resource as willgraduates in geotechnical engineering programmes.


Gregory B. Baecher is the author of Reliability and Statistics in Geotechnical Engineering, published by Wiley.

John T. Christian is the author of Reliability and Statistics in Geotechnical Engineering, published by Wiley.

Probabilistic reasoning, statistical methods, and measures of engineering judgment are combined to develop a quantified approach for analyzing and managing risks in civil engineering systems and the applied earth sciences. The resulting risk analysis approach described in this book reflects an emerging trend in geotechnical engineering, natural hazards mitigation, infrastructure protection, and other civil engineering fields to directly and quantitatively deal with uncertainty. Reliability and Statistics in Geotechnical Engineering offers a much needed state-of-the-art reference for risk analysis in geotechnical engineering and geology.

Integrating theory and practical applications, this book:

  • Discusses the nature and philosophy of uncertainty in geological and geotechnical engineering.
  • Addresses fundamentals and limits of probabilistic and statistical methods in the geological and geotechnical context.
  • Develops statistical approaches to site characaterization decisions and for analyzing field and laboratory data.
  • Explains traditional and emerging risk analysis methodologies and provides guidance for their use.
  • Presents many applications of statistics, reliability, and risk techniques to practical problems.

Emphasizing both theoretical underpinnings and practical applications, this comprehensive text constitutes an invaluable reference for practising geotechnical engineers, geologists, university students, and civil engineers in general practice.


Part I.

1 Introduction uncertainty and risk in geotechnical engineering.

1.1 Offshore platforms.

1.2 Pit mine slopes.

1.3 Balancing risk and reliability in a geotechnical design.

1.4 Historical development of reliability methods in civil engineering.

1.5 Some terminological and philosophical issues.

1.6 The organization of this book.

1.7 A comment on notation and nomenclature.

2 Uncertainty.

2.1 Randomness, uncertainty, and the world.

2.2 Modeling uncertainties in risk and reliability analysis.

2.3 Probability.

3 Probability.

3.1 Histograms and frequency diagrams.

3.2 Summary statistics.

3.3 Probability theory.

3.4 Random variables.

3.5 Random process models.

3.6 Fitting mathematical pdf models to data.

3.7 Covariance among variables.

4 Inference.

4.1 Frequentist theory.

4.2 Bayesian theory.

4.3 Prior probabilities.

4.4 Inferences from sampling.

4.5 Regression analysis.

4.6 Hypothesis tests.

4.7 Choice among models.

5 Risk, decisions and judgment.

5.1 Risk.

5.2 Optimizing decisions.

5.3 Non-optimizing decisions.

5.4 Engineering judgment.

Part II.

6 Site characterization.

6.1 Developments in site characterization.

6.2 Analytical approaches to site characterization.

6.3 Modeling site characterization activities.

6.4 Some pitfalls of intuitive data evaluation.

6.5 Organization of Part II.

7 Classification and mapping.

7.1 Mapping discrete variables.

7.2 Classification.

7.3 Discriminant analysis.

7.4 Mapping.

7.5 Carrying out a discriminant or logistic analysis.

8 Soil variability.

8.1 Soil properties.

8.2 Index tests and classification of soils.

8.3 Consolidation properties.

8.4 Permeability.

8.5 Strength properties.

8.6 Distributional properties.

8.7 Measurement error.

9 Spatial variability within homogeneous deposits.

9.1 Trends and variations about trends.

9.2 Residual variations.

9.3 Estimating autocorrelation and autocovariance.

9.4 Variograms and geostatistics.

Appendix: algorithm for maximizing log-likelihood of autocovariance.

10 Random field theory.

10.1 Stationary processes.

10.2 Mathematical properties of autocovariance functions.

10.3 Multivariate (vector) random fields.

10.4 Gaussian random fields.

10.5 Functions of random fields.

11 Spatial sampling.

11.1 Concepts of sampling.

11.2 Common spatial sampling plans.

11.3 Interpolating random fields.

11.4 Sampling for autocorrelation.

12 Search theory.

12.1 Brief history of search theory.

12.2 Logic of a search process.

12.3 Single stage search.

12.4 Grid search.

12.5 Inferring target characteristics.

12.6 Optimal search.

12.7 Sequential search.

Part III.

13 Reliability analysis and error propagation.

13.1 Loads, resistances and reliability.

13.2 Results for different distributions of the performance function.

13.3 Steps and approximations in reliability analysis.

13.4 Error propagation statistical moments of the performance function.

13.5 Solution techniques for practical cases.

13.6 A simple conceptual model of practical significance.

14 First order second moment (FOSM) methods.

14.1 The James Bay dikes.

14.2 Uncertainty in geotechnical parameters.

14.3 FOSM calculations.

14.4 Extrapolations and consequences.

14.5 Con...

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Reliability and Statistics in Geotechnical Engineering
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