LEADER 05474nam 2200673Ia 450 001 9910139364103321 005 20200520144314.0 010 $a1-282-54782-8 010 $a9786612547829 010 $a0-470-82456-5 010 $a0-470-82455-7 035 $a(CKB)2520000000006782 035 $a(EBL)496045 035 $a(OCoLC)609858682 035 $a(SSID)ssj0000356980 035 $a(PQKBManifestationID)11262119 035 $a(PQKBTitleCode)TC0000356980 035 $a(PQKBWorkID)10352454 035 $a(PQKB)11339490 035 $a(MiAaPQ)EBC496045 035 $a(Au-PeEL)EBL496045 035 $a(CaPaEBR)ebr10371959 035 $a(CaONFJC)MIL254782 035 $a(PPN)169614271 035 $a(EXLCZ)992520000000006782 100 $a20091104d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian methods for structural dynamics and civil engineering$b[electronic resource] /$fKa-Veng Yuen 210 $aSingapore ;$aHoboken, N.J. $cJohn Wiley & Sons Asia$dc2010 215 $a1 online resource (312 p.) 300 $aDescription based upon print version of record. 311 $a0-470-82454-9 320 $aIncludes bibliographical references and index. 327 $aBAYESIAN METHODS FOR STRUCTURAL DYNAMICS AND CIVIL ENGINEERING; Contents; Preface; Acknowledgements; 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; 2.1.1 Bayes' Theorem for Discrete Events; 2.1.2 Bayes' Theorem for Continuous-valued Parameters by Discrete Events; 2.1.3 Bayes' Theorem for Discrete Events by Continuous-valued Parameters 327 $a2.1.4 Bayes' Theorem between Continuous-valued Parameters2.1.5 Bayesian Inference; 2.1.6 Examples of Bayesian Inference; 2.2 Bayesian Model Updating with Input-output Measurements; 2.2.1 Input-output Measurements; 2.2.2 Bayesian Parametric Identification; 2.2.3 Model Identifiability; 2.3 Deterministic versus Probabilistic Methods; 2.4 Regression Problems; 2.4.1 Linear Regression Problems; 2.4.2 Nonlinear Regression Problems; 2.5 Numerical Representation of the Updated PDF; 2.5.1 General Form of Reliability Integrals; 2.5.2 Monte Carlo Simulation 327 $a2.5.3 Adaptive Markov Chain Monte Carlo Simulation2.5.4 Illustrative Example; 2.6 Application to Temperature Effects on Structural Behavior; 2.6.1 Problem Description; 2.6.2 Thermal Effects on Modal Frequencies of Buildings; 2.6.3 Bayesian Regression Analysis; 2.6.4 Analysis of the Measurements; 2.6.5 Concluding Remarks; 2.7 Application to Noise Parameters Selection for the Kalman Filter; 2.7.1 Problem Description; 2.7.2 Kalman Filter; 2.7.3 Illustrative Examples; 2.8 Application to Prediction of Particulate Matter Concentration; 2.8.1 Introduction 327 $a2.8.2 Extended-Kalman-filter based Time-varying Statistical Models2.8.3 Analysis with Monitoring Data; 2.8.4 Conclusion; 3 Bayesian Spectral Density Approach; 3.1 Modal and Model Updating of Dynamical Systems; 3.2 Random Vibration Analysis; 3.2.1 Single-degree-of-freedom Systems; 3.2.2 Multi-degree-of-freedom Systems; 3.3 Bayesian Spectral Density Approach; 3.3.1 Formulation for Single-channel Output Measurements; 3.3.2 Formulation for Multiple-channel Output Measurements; 3.3.3 Selection of the Frequency Index Set; 3.3.4 Nonlinear Systems; 3.4 Numerical Verifications 327 $a3.4.1 Aliasing and Leakage3.4.2 Identification with the Spectral Density Approach; 3.4.3 Identification with Small Amount of Data; 3.4.4 Concluding Remarks; 3.5 Optimal Sensor Placement; 3.5.1 Information Entropy with Globally Identifiable Case; 3.5.2 Optimal Sensor Configuration; 3.5.3 Robust Information Entropy; 3.5.4 Discrete Optimization Algorithm for Suboptimal Solution; 3.6 Updating of a Nonlinear Oscillator; 3.7 Application to Structural Behavior under Typhoons; 3.7.1 Problem Description; 3.7.2 Meteorological Information of the Two Typhoons; 3.7.3 Analysis of Monitoring Data 327 $a3.7.4 Concluding Remarks 330 $aBayesian 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 conc 606 $aEngineering$xStatistical methods 606 $aStructural engineering$xMathematics 606 $aBayesian statistical decision theory 615 0$aEngineering$xStatistical methods. 615 0$aStructural engineering$xMathematics. 615 0$aBayesian statistical decision theory. 676 $a624.101/519542 700 $aYuen$b Ka-Veng$0943267 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139364103321 996 $aBayesian methods for structural dynamics and civil engineering$92128855 997 $aUNINA