Bayesian Methods for Structural Dynamics and Civil Engineering
Bayesian Methods for Structural Dynamics and Civil Engineering
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DOI：
10.1002/9780470824566
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发表时间：
2010-04
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通讯作者：
K. Yuen
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文献类型：
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作者：
K. Yuen
目录前言1引言1.1工程中的托马斯贝叶斯和贝叶斯方法1.2模型更新的目的1.3不确定性和贝叶斯更新的来源1.4本书的组织2基本概念和贝叶斯概率框架2.1条件概率和基本概念2.2贝叶斯模型更新与输入-输出测量2.3确定性方法与概率方法2.4回归问题2.5更新PDF的数值表示2.6温度对结构行为的影响2.7卡尔曼滤波器噪声参数选择的应用2.8颗粒物浓度的预测3贝叶斯谱密度方法3.1动力系统的模态和模型更新3.2随机振动分析3.3贝叶斯谱密度方法3.4数值验证3.5传感器的最佳布置3.6非线性振子的更新3.7台风下结构行为的应用3.8水跃的应用4贝叶斯时间-域方法4.1引言4.2精确贝叶斯公式及其计算困难4.3非平稳响应的随机振动分析4.4近似PDF展开的贝叶斯更新4.5数值验证4.6在未测地震动模型更新中的应用4.7结论4.8谱密度法和时间-域方法4.9扩展阅读5使用特征值的模型更新-特征向量测量5.1介绍5.2公式5.3线性最优化问题5.4迭代算法5.5不确定性估计5.6结构健康监测的应用5.7结论6贝叶斯模型类别选择6.1介绍6.2贝叶斯模型类选择6.3回归问题的模型类选择6.4模态更新应用6.5地震衰减经验关系应用6.6先验分布-6.7最后的评论A高斯随机变量的海森矩阵和协方差矩阵之间的关系B高斯随机变量的边缘PDF的轮廓C预测的条件PDF C.1两个随机变量C.2一般案例参考索引
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 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 Computational Difficulties 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 Earthquake Ground Motion 4.7 Concluding Remarks 4.8 Comparison of Spectral Density Approach and Time-domain Approach 4.9 Extended Readings 5 Model Updating Using Eigenvalue-Eigenvector Measurements 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 Empirical Relationship 6.6 Prior Distributions - Revisited 6.7 Final Remarks A Relationship between the Hessian and Covariance Matrix for Gaussian Random Variables B Contours of Marginal PDFs for Gaussian Random Variables C Conditional PDF for Prediction C.1 Two Random Variables C.2 General Cases References Index