E-raamat: Bayesian Methods for Structural Dynamics and Civil Engineering [Wiley Online]

Yuen (Department of Civil and Environmental Engineering, the University of Macau) introduces some recently developed Bayesian probabilistic methods and applications used in civil engineering and structural dynamics. The methods are developed for the identification of dynamical systems, but some of them are also applicable to static systems. The book deals with two levels of system identification problems: parametric identification with a specified model class, and the selection of model class. Civil engineering applications are described, including air quality prediction, finite-element model updating, hydraulic jump, and seismic attenuation relationships. The book begins with a literature review of applications in engineering and an introduction to basic concepts of conditional probabilities and the Bayes Theorem. Subsequent chapters introduce Bayesian methods for updating the mathematical models of dynamical systems, and discuss the problem of model updating with eigenvalue-eigenvector measurements. A final chapter covers Bayesian model class selection. About 20 pages of mathematical appendices are provided. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com) 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 problemsThis 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