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    1. Naturvetenskap och teknik
    2. Matematik och naturvetenskap
    3. Matematik
    4. Matematisk statistik

    Bayesian Methods for Structural Dynamics and Civil Engineering

    AvKa-Veng Yuen

    Inbunden, Engelska, 2010

    2 196 kr

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    E-bok

    1 688 kr

    Beskrivning

    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 fieldIncludes identification problems for both dynamic and static systemsAddresses challenging civil engineering problems such as modal/model updatingPresents methods applicable to mechanical and aerospace engineeringGives engineers and engineering students a concrete sense of implementationCovers real-world case studies in civil engineering and beyond, such as:structural health monitoringseismic attenuationfinite-element model updatinghydraulic jumpartificial neural network for damage detectionair quality prediction Includes other insightful daily-life examplesCompanion website with MATLAB code downloads for independent practiceWritten 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 www.wiley.com/go/yuen

    Produktinformation

    • Utgivningsdatum:2010-04-28
    • Mått:173 x 246 x 23 mm
    • Vikt:703 g
    • Format:Inbunden
    • Språk:Engelska
    • Antal sidor:320
    • Förlag:John Wiley & Sons Inc
    • ISBN:9780470824542

    Utforska kategorier

    • Matematisk statistik inom Naturvetenskap och teknik
    • Byggnadsteknik inom Naturvetenskap och teknik

    Mer om författaren

    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.

    Innehållsförteckning

    • Contents PrefaceNomenclature1 Introduction1.1 Thomas Bayes and Bayesian Methods in Engineering1.2 Purpose of Model Updating1.3 Source of Uncertainty and Bayesian Updating1.4 Organization of the Book2 Basic Concepts and Bayesian Probabilistic Framework2.1 Conditional Probability and Basic Concepts2.2 Bayesian Model Updating with Input-output Measurements2.3 Deterministic versus Probabilistic Methods2.4 Regression Problems2.5 Numerical Representation of the Updated PDF2.6 Application to Temperature Effects on Structural Behavior2.7 Application to Noise Parameters Selection for Kalman Filter2.8 Application to Prediction of Particulate Matter Concentration3 Bayesian Spectral Density Approach3.1 Modal and Model Updating of Dynamical Systems3.2 Random Vibration Analysis3.3 Bayesian Spectral Density Approach3.4 Numerical Verifications3.5 Optimal Sensor Placement3.6 Updating of a Nonlinear Oscillator3.7 Application to Structural Behavior under Typhoons3.8 Application to Hydraulic Jump4 Bayesian Time-domain Approach4.1 Introduction4.2 Exact Bayesian Formulation and its Computational Difficulties4.3 Random Vibration Analysis of Nonstationary Response4.4 Bayesian Updating with Approximated PDF Expansion4.5 Numerical Verification4.6 Application to Model Updating with Unmeasured Earthquake Ground Motion4.7 Concluding Remarks4.8 Comparison of Spectral Density Approach and Time-domain Approach4.9 Extended Readings5 Model Updating Using Eigenvalue-Eigenvector Measurements5.1 Introduction5.2 Formulation5.3 Linear Optimization Problems5.4 Iterative Algorithm5.5 Uncertainty Estimation5.6 Applications to Structural Health Monitoring5.7 Concluding Remarks6 Bayesian Model Class Selection6.1 Introduction6.2 Bayesian Model Class Selection6.3 Model Class Selection for Regression Problems6.4 Application to Modal Updating6.5 Application to Seismic Attenuation Empirical Relationship6.6 Prior Distributions - Revisited6.7 Final RemarksA Relationship between the Hessian and Covariance Matrix for Gaussian Random VariablesB Contours of Marginal PDFs for Gaussian Random VariablesC Conditional PDF for PredictionC.1 Two Random VariablesC.2 General CasesReferencesIndex