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Informationen zum Autor Ka-Veng Yuen is an Associate Professor of Civil and Environmental Engineering at the University of Macau. His research interests include random vibrations, system identification, structural health monitoring, modal/model identification, reliability analysis of engineering systems, structural control, model class selection, air quality prediction, non-destructive testing and probabilistic methods. He has been working on Bayesian statistical inference and its application since 1997. Yuen has published over sixty research papers in international conferences and top journals in the field. He is an editorial board member of the International Journal of Reliability and Safety , and is also a member of the ASCE Probabilistic Methods Committee, the Subcommittee on Computational Stochastic Mechanics, and the Subcommittee on System Identification and Structural Control of the International Association for Structural Safety and Reliability (IASSAR), as well as the Committee of Financial Analysis and Computation, Chinese Association of New Cross Technology in Mathematics, Mechanics and Physics. Yuen holds an M.S. from Hong Kong University of Science and Technology and a Ph.D. from Caltech, both in Civil Engineering. Klappentext Bayesian Methods for Structural Dynamics introduces recently developed Bayesian methods and applications to several areas of engineering. Readers are provided a through grounding in the theory, and shown concrete examples to promote easier understanding. The first two chapters give a general introduction and literature review of the applications of Bayesian methods in different disciplines of engineering, while giving simple examples of static systems to illustrate the concepts. Yuen goes on to introduce time-domain approaches for unmeasured input, which can be applied to multi-degree-of-freedom linear systems subjected to stationary or non-stationary input, as demonstrated with earthquake ground motion. The author presents the Bayesian spectral density approach in the fourth chapter, using hydraulic jump to demonstrate the methodology and providing comparisons of applicability between time-domain and frequency-domain approaches. In chapter five, Yuen addresses the problem of model parameter identification through eigenvalue-eigenvector measurements, with applications to finite-element model updating and structural health monitoring. Chapter 6 considers the problem of selection of model class for system identification, and introduces Markov Chain Monte Carlo simulation and Metropolis-Hastings algorithm. Model class selection is then illustrated by problems in air-quality prediction, artificial neural networks, and seismic attenuation. Zusammenfassung 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. Inhaltsverzeichnis Contents Preface Nomenclature 1 Introduction 1.1 Thomas Bayes and Bayesian Methods in Engineering 1.2 Purpose of Model Updating 1.3 Source of Uncertainty and Bayesian Updating 1.4 Organization of the Book 2 Basic Concepts and Bayesian Probabilistic Framework 2.1 Conditional Probability and Basic Concepts 2.2 Bayesian Model Updating with Input-output Measurements 2.3 Deterministic versus Probabilistic Methods 2.4 Regression Problems 2.5 Numerical Representation of the Updated PDF 2.6 Application to Temperature Effects on Structural Behavior 2.7 Application to Noise Parameters Selection for Kalman Filter 2.8 Application to Prediction of Particulate Matter Concentration 3 Bayesian Spectral Density...
