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.

Establishes Geotechnical Reliability as Fundamentally Distinct from Structural Reliability Reliability-based design is relatively well established in structural design. Its use is less mature in geotechnical design, but there is a steady progression towards reliability-based design as seen in the inclusion of a new Annex D on "Reliability of Geotechnical Structures" in the third edition of ISO 2394. Reliability-based design can be viewed as a simplified form of risk-based design where different consequences of failure are implicitly covered by the adoption of different target reliability indices. Explicit risk management methodologies are required for large geotechnical systems where soil and loading conditions are too varied to be conveniently slotted into a few reliability classes (typically three) and an associated simple discrete tier of target reliability indices. Provides Realistic Practical Guidance Risk and Reliability in Geotechnical Engineering makes these reliability and risk methodologies more accessible to practitioners and researchers by presenting soil statistics which are necessary inputs, by explaining how calculations can be carried out using simple tools, and by presenting illustrative or actual examples showcasing the benefits and limitations of these methodologies. With contributions from a broad international group of authors, this text: Presents probabilistic models suited for soil parameters Provides easy-to-use Excel-based methods for reliability analysis Connects reliability analysis to design codes (including LRFD and Eurocode 7) Maximizes value of information using Bayesian updating Contains efficient reliability analysis methods Accessible To a Wide Audience Risk and Reliability in Geotechnical Engineering presents all the "need-to-know" information for a non-specialist to calculate and interpret the reliability index and risk of geotechnical structures in a realistic and robust way. It suits engineers, researchers, and students who are interested in the practical outcomes of reliability and risk analyses without going into the intricacies of the underlying mathematical theories.

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.

NEW PROBABILISTIC APPROACHES FOR REALISTIC RISK ASSESSMENT IN GEOTECHNICAL ENGINEERING. This text presents a thorough examination of the theories and methodologies available for risk assessment in geotechnical engineering, spanning the full range from established single-variable and "first order" methods to the most recent, advanced numerical developments. In response to the growing application of LRFD methodologies in geotechnical design, coupled with increased demand for risk assessments from clients ranging from regulatory agencies to insurance companies, authors Fenton and Griffiths have introduced an innovative reliability-based risk assessment method, the Random Finite Element Method (RFEM). The authors have spent more than fifteen years developing this statistically based method for modeling the real spatial variability of soils and rocks. As demonstrated in the book, RFEM performs better in real-world applications than traditional risk assessment tools that do not properly account for the spatial variability of geomaterials. This text is divided into two parts: Part One, Theory, explains the theory underlying risk assessment methods in geotechnical engineering. This part's seven chapters feature more than 100 worked examples, enabling you to develop a detailed understanding of the methods. Part Two, Practice, demonstrates how to use advanced probabilistic tools for several classical geotechnical engineering applications. Working with the RFEM, the authors show how to assess risk in problems familiar to all geotechnical engineers. All the programs used for the geotechnical applications discussed in Part Two may be downloaded from the authors' Web site at www.engmath.dal.ca/rfem/ at no charge, enabling you to duplicate the authors' results and experiment with your own data. In short, you get all the theory and practical guidance you need to apply the most advanced probabilistic approaches for managing uncertainty in geotechnical design.

Reliability-based design is the only engineering methodology currently available which can ensure self-consistency in both physical and probabilistic terms. It is also uniquely compatible with the theoretical basis underlying other disciplines such as structural design. It is especially relevant as geotechnical design becomes subject to incre

Singh, Jain, and Tyagi present the key concepts of risk and reliability that apply to a wide array of problems in civil and environmental engineering.

This updated edition retains its introduction to applied fundamental statistics, probability, reliability, and decision theory as these pertain to problems in Civil Engineering. The new edition adds an expanded treatment of systems reliability, Bayesian methods, and spatial variabililty, along with additional example problems throughout. The book provides readers with the tools needed to determine the probability of failure, and when multiplied by the consequences of failure, illustrates how to assess the risk of civil engineering problems. Presenting methods for quantifying uncertainty that exists in engineering analysis and design, with an emphasis on fostering more accurate analysis and design, the text is ideal for students and practitioners of a range of civil engineering disciplines. Expands on the class-tested pedagogy from the first edition with more material and more examples; Broadens understanding with simulations coded both in Matlab and in R; Features new chapters on spatial variability and Bayesian methods; Emphasizes techniques for estimating the influence of uncertainty on the probability of failure