Uncertainties about analytical models, fluctuations in loads, and variability of material properties contribute to the small but real probability of structure failures. This advanced engineering text describes methods developed to deal with stochastic aspects of structural behavior, providing a framework for evaluating, comparing, and combining stochastic effects. Starting with the general problem of consistent evaluation of the reliability of structures, the text proceeds to examination of the second-moment reliability index methods that describe failure in terms of one or more limit states. It presents first-order reliability methods for computation of failure probabilities for individual limit states and for systems; and it illustrates identification of the design parameters most affecting reliability. Additional subjects include a self-contained presentation of extreme-value theory and stochastic processes; stationary, evolutionary, and nonlinear aspects of stochastic response of structures; a stochastic approach to material fatigue damage and crack propagation; and stochastic models for several natural and manufactured loads.

A quarter of the century has elapsed since I gave my first course in structural reliability to graduate students at the University of Waterloo in Canada. Since that time on I have given many courses and seminars to students, researchers, designers, and site engineers interested in reliability. I also participated in and was responsible for numerous projects where reliability solutions were required. During that period, the scope of structural reliability gradually enlarged to become a substantial part of the general reliability theory. First, it is apparent that bearing structures should not be isolated objectives of interest, and, consequently, that constntCted facilities should be studied. Second, a new engineering branch has emerged -reliability engineering. These two facts have highlighted new aspects and asked for new approaches to the theory and applications. I always state in my lectures that the reliability theory is nothing more than mathematized engineering judgment. In fact, thanks mainly to probability and statistics, and also to computers, the empirical knowledge gained by Humankind's construction experience could have been transposed into a pattern of logic thinking, able to produce conclusions and to forecast the behavior of engineering entities. This manner of thinking has developed into an intricate network linked by certain rules, which, in a way, can be considered a type of reliability grammar. We can discern many grammatical concepts in the general structure of the reliability theory.

This book addresses probabilistic methods for the evaluation of structural reliability, including the theoretical basis of these methods. Partial safety factor codes under current practice are briefly introduced and discussed. A probabilistic code format for obtaining a formal reliability evaluation system that catches the most essential features of the nature of the uncertainties and their interplay is then gradually developed. The concepts presented are illustrated by numerous examples throughout the text. The modular approach of the book allows the reader to navigate through the different stages of the methods.

Structural Design for Fire Safety, 2nd edition Andrew H. Buchanan, University of Canterbury, New Zealand Anthony K. Abu, University of Canterbury, New Zealand A practical and informative guide to structural fire engineering This book presents a comprehensive overview of structural fire engineering. An update on the first edition, the book describes new developments in the past ten years, including advanced calculation methods and computer programs. Further additions include: calculation methods for membrane action in floor slabs exposed to fires; a chapter on composite steel-concrete construction; and case studies of structural collapses. The book begins with an introduction to fire safety in buildings, from fire growth and development to the devastating effects of severe fires on large building structures. Methods of calculating fire severity and fire resistance are then described in detail, together with both simple and advanced methods for assessing and designing for structural fire safety in buildings constructed from structural steel, reinforced concrete, or structural timber. Structural Design for Fire Safety, 2nd edition bridges the information gap between fire safety engineers, structural engineers and building officials, and it will be useful for many others including architects, code writers, building designers, and firefighters. Key features: • Updated references to current research, as well as new end-of-chapter questions and worked examples. •Authors experienced in teaching, researching, and applying structural fire engineering in real buildings. • A focus on basic principles rather than specific building code requirements, for an international audience. An essential guide for structural engineers who wish to improve their understanding of buildings exposed to severe fires and an ideal textbook for introductory or advanced courses in structural fire engineering.

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Bayesian methods are a powerful tool in many areas of science and engineering, especially statistical physics, medical sciences, electrical engineering, and information sciences. They are also ideal for civil engineering applications, given the numerous types of modeling and parametric uncertainty in civil engineering problems. For example, earthquake ground motion cannot be predetermined at the structural design stage. Complete wind pressure profiles are difficult to measure under operating conditions. Material properties can be difficult to determine to a very precise level – especially concrete, rock, and soil. For air quality prediction, it is difficult to measure the hourly/daily pollutants generated by cars and factories within the area of concern. It is also difficult to obtain the updated air quality information of the surrounding cities. Furthermore, the meteorological conditions of the day for prediction are also uncertain. These are just some of the civil engineering examples to which Bayesian probabilistic methods are applicable. Familiarizes readers with the latest developments in the field Includes identification problems for both dynamic and static systems Addresses challenging civil engineering problems such as modal/model updating Presents methods applicable to mechanical and aerospace engineering Gives engineers and engineering students a concrete sense of implementation Covers real-world case studies in civil engineering and beyond, such as: structural health monitoring seismic attenuation finite-element model updating hydraulic jump artificial neural network for damage detection air quality prediction Includes other insightful daily-life examples Companion website with MATLAB code downloads for independent practice Written by a leading expert in the use of Bayesian methods for civil engineering problems This book is ideal for researchers and graduate students in civil and mechanical engineering or applied probability and statistics. Practicing engineers interested in the application of statistical methods to solve engineering problems will also find this to be a valuable text. MATLAB code and lecture materials for instructors available at http://www.wiley.com/go/yuen