Bayesian Methods for Structural Dynamics and Civil Engineering
Published on 22. February 2010
312 pages
978-0-470-82455-9 (ISBN)
Description
Bayesian methods are a powerful tool in many areas of science andengineering, especially statistical physics, medical sciences,electrical engineering, and information sciences. They are alsoideal for civil engineering applications, given the numerous typesof modeling and parametric uncertainty in civil engineeringproblems. For example, earthquake ground motion cannot bepredetermined at the structural design stage. Complete windpressure profiles are difficult to measure under operatingconditions. Material properties can be difficult to determine to avery precise level - especially concrete, rock, and soil. Forair quality prediction, it is difficult to measure the hourly/dailypollutants generated by cars and factories within the area ofconcern. It is also difficult to obtain the updated air qualityinformation of the surrounding cities. Furthermore, themeteorological conditions of the day for prediction are alsouncertain. These are just some of the civil engineering examples towhich Bayesian probabilistic methods are applicable.
* Familiarizes readers with the latest developments in thefield
* Includes identification problems for both dynamic and staticsystems
* Addresses challenging civil engineering problems such asmodal/model updating
* Presents methods applicable to mechanical and aerospaceengineering
* Gives engineers and engineering students a concrete sense ofimplementation
* 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 independentpractice
* Written by a leading expert in the use of Bayesian methods forcivil engineering problems
This book is ideal for researchers and graduate students incivil and mechanical engineering or applied probability andstatistics. Practicing engineers interested in the application ofstatistical methods to solve engineering problems will also findthis to be a valuable text.
MATLAB code and lecture materials for instructors available athttp://www.wiley.com/go/yuen
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Ka-Veng Yuen
Bayesian Methods for Structural Dynamics
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09/2010
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Person
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.
Content
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 StructuralBehavior
2.7 Application to Noise Parameters Selection for KalmanFilter
2.8 Application to Prediction of Particulate MatterConcentration
3 Bayesian Spectral Density Approach
3.1 Modal and Model Updating of Dynamical Systems
3.2 Random Vibration Analysis
3.3 Bayesian Spectral Density Approach
3.4 Numerical Verifications
3.5 Optimal Sensor Placement
3.6 Updating of a Nonlinear Oscillator
3.7 Application to Structural Behavior under Typhoons
3.8 Application to Hydraulic Jump
4 Bayesian Time-domain Approach
4.1 Introduction
4.2 Exact Bayesian Formulation and its ComputationalDifficulties
4.3 Random Vibration Analysis of Nonstationary Response
4.4 Bayesian Updating with Approximated PDF Expansion
4.5 Numerical Verification
4.6 Application to Model Updating with Unmeasured EarthquakeGround Motion
4.7 Concluding Remarks
4.8 Comparison of Spectral Density Approach and Time-domainApproach
4.9 Extended Readings
5 Model Updating Using Eigenvalue-EigenvectorMeasurements
5.1 Introduction
5.2 Formulation
5.3 Linear Optimization Problems
5.4 Iterative Algorithm
5.5 Uncertainty Estimation
5.6 Applications to Structural Health Monitoring
5.7 Concluding Remarks
6 Bayesian Model Class Selection
6.1 Introduction
6.2 Bayesian Model Class Selection
6.3 Model Class Selection for Regression Problems
6.4 Application to Modal Updating
6.5 Application to Seismic Attenuation EmpiricalRelationship
6.6 Prior Distributions - Revisited
6.7 Final Remarks
A Relationship between the Hessian and Covariance Matrix forGaussian Random Variables
B Contours of Marginal PDFs for Gaussian RandomVariables
C Conditional PDF for Prediction
C.1 Two Random Variables
C.2 General Cases
References
Index
