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The correlation tools provided by you can thus easily implement most of the existing error localization algorithms.No mechanism is however implemented to automatically translate the results of this localization into a set of parameters to be updated.Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering.Key features: Acknowledgements x Nomenclature xi 1 Introduction to Finite Element Model Updating 1 1.1 Introduction 1 1.2 Finite Element Modelling 2 1.3 Vibration Analysis 4 1.3.1 Modal Domain Data 4 1.3.2 Frequency Domain Data 5 1.4 Finite Element Model Updating 5 1.5 Finite Element Model Updating and Bounded Rationality 6 1.6 Finite Element Model Updating Methods 7 1.6.1 Direct Methods 8 1.6.2 Iterative Methods 10 1.6.3 Artificial Intelligence Methods 11 1.6.4 Uncertainty Quantification Methods 11 1.7 Bayesian Approach versus Maximum Likelihood Method 14 1.8 Outline of the Book 15 References 17 2 Model Selection in Finite Element Model Updating 24 2.1 Introduction 24 2.2 Model Selection in Finite Element Modelling 25 2.2.1 Akaike Information Criterion 25 2.2.2 Bayesian Information Criterion 25 2.2.3 Bayes Factor 26 2.2.4 Deviance Information Criterion 26 2.2.5 Particle Swarm Optimisation for Model Selection 27 2.2.6 Regularisation 28 2.2.7 Cross-Validation 28 2.2.8 Nested Sampling for Model Selection 30 2.3 Simulated Annealing 32 2.4 Asymmetrical H-Shaped Structure 35 2.4.1 Regularisation 35 2.4.2 Cross-Validation 36 2.4.3 Bayes Factor and Nested Sampling 36 2.5 Conclusion 37 References 37 3 Bayesian Statistics in Structural Dynamics 42 3.1 Introduction 42 3.2 Bayes’ Rule 45 3.3 Maximum Likelihood Method 46 3.4 Maximum a Posteriori Parameter Estimates 46 3.5 Laplace’s Method 47 3.6 Prior, Likelihood and Posterior Function of a Simple Dynamic Example 47 3.6.1 Likelihood Function 49 3.6.2 Prior Function 49 3.6.3 Posterior Function 50 3.6.4 Gaussian Approximation 50 3.7 The Posterior Approximation 52 3.7.1 Objective Function 52 3.7.2 Optimisation Approach 52 3.7.3 Case Example 55 3.8 Sampling Approaches for Estimating Posterior Distribution 55 3.8.1 Monte Carlo Method 55 3.8.2 Markov Chain Monte Carlo Method 56 3.8.3 Simulated Annealing 57 3.8.4 Gibbs Sampling 58 3.9 Comparison between Approaches 58 3.9.1 Numerical Example 58 3.10 Conclusions 60 References 61 4 Metropolis–Hastings and Slice Sampling for Finite Element Updating 65 4.1 Introduction 65 4.2 Likelihood, Prior and the Posterior Functions 66 4.3 The Metropolis–Hastings Algorithm 69 4.4 The Slice Sampling Algorithm 71 4.5 Statistical Measures 72 4.6 Application 1: Cantilevered Beam 74 4.7 Application 2: Asymmetrical H-Shaped Structure 78 4.8 Conclusions 81 References 81 5 Dynamically Weighted Importance Sampling for Finite Element Updating 84 5.1 Introduction 84 5.2 Bayesian Modelling Approach 85 5.3 Metropolis–Hastings (M-H) Algorithm 87 5.4 Importance Sampling 88 5.5 Dynamically Weighted Importance Sampling 89 5.5.1 Markov Chain 90 5.5.2 Adaptive Pruned-Enriched Population Control Scheme 90 5.5.3 Monte Carlo Dynamically Weighted Importance Sampling 92 5.6 Application 1: Cantilevered Beam 93 5.7 Application 2: H-Shaped Structure 97 5.8 Conclusions 101 References 101 6 Adaptive Metropolis–Hastings for Finite Element Updating 104 6.1 Introduction 104 6.2 Adaptive Metropolis–Hastings Algorithm 105 6.3 Application 1: Cantilevered Beam 108 6.4 Application 2: Asymmetrical H-Shaped Beam 111 6.5 Application 3: Aircraft GARTEUR Structure 113 6.6 Conclusion 119 References 119 7 Hybrid Monte Carlo Technique for Finite Element Model Updating 122 7.1 Introduction 122 7.2 Hybrid Monte Carlo Method 123 7.3 Properties of the HMC Method 124 7.3.1 Time Reversibility 124 7.3.2 Volume Preservation 124 7.3.3 Energy Conservation 125 7.4 The Molecular Dynamics Algorithm 125 7.5 Improving the HMC 127 7.5.1 Choosing an Efficient Time Step 127 7.5.2 Suppressing the Random Walk in the Momentum 128 7.5.3 Gradient Computation 128 7.6 Application 1: Cantilever Beam 129 7.7 Application 2: Asymmetrical H-Shaped Structure 132 7.8 Conclusion 135 References 135 8 Shadow Hybrid Monte Carlo Technique for Finite Element Model Updating 138 8.1 Introduction 138 8.2 Effect of Time Step in the Hybrid Monte Carlo Method 139 8.3 The Shadow Hybrid Monte Carlo Method 139 8.4 The Shadow Hamiltonian 142 8.5 Application: GARTEUR SM-AG19 Structure 143 8.6 Conclusion 152 References 153 9 Separable Shadow Hybrid Monte Carlo in Finite Element Updating 155 9.1 Introduction 155 9.2 Separable Shadow Hybrid Monte Carlo 155 9.3 Theoretical Justifications of the S2HMC Method 158 9.4 Application 1: Asymmetrical H-Shaped Structure 160 9.5 Application 2: GARTEUR SM-AG19 Structure 165 9.6 Conclusions 171 References 172 10 Evolutionary Approach to Finite Element Model Updating 174 10.1 Introduction 174 10.2 The Bayesian Formulation 175 10.3 The Evolutionary MCMC Algorithm 177 10.3.1 Mutation 178 10.3.2 Crossover 179 10.3.3 Exchange 181 10.4 Metropolis–Hastings Method 181 10.5 Application: Asymmetrical H-Shaped Structure 182 10.6 Conclusion 185 References 186 11 Adaptive Markov Chain Monte Carlo Method for Finite Element Model Updating 189 11.1 Introduction 189 11.2 Bayesian Theory 191 11.3 Adaptive Hybrid Monte Carlo 192 11.4 Application 1: A Linear System with Three Degrees of Freedom 195 11.4.1 Updating the Stiffness Parameters 196 11.5 Application 2: Asymmetrical H-Shaped Structure 198 11.5.1 H-Shaped Structure Simulation 198 11.6 Conclusion 202 References 203 12 Conclusions and Further Work 206 12.1 Introduction 206 12.2 Further Work 208 12.2.1 Reversible Jump Monte Carlo 208 12.2.2 Multiple-Try Metropolis–Hastings 208 12.2.3 Dynamic Programming 209 12.2.4 Sequential Monte Carlo 209 References 209 Appendix A: Experimental Examples 211 Appendix B: Markov Chain Monte Carlo 219 Appendix C: Gaussian Distribution 222 Index 226Tshilidzi Marwala is a Professor of Mechanical and Electrical Engineering as well as Deputy Vice-Chancellor at the University of Johannesburg.

model updating-79model updating-24model updating-67model updating-4

In this paper, an FE model updating method is proposed to assist industrial engineers in achieving a reliable model that can accurately represent the dynamic characteristics of machine-tool spindle systems.

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering.

The book covers probabilistic finite element model updating, achieved using Bayesian statistics.

The proposed method was applied to update an existing coupled model of a machine-tool spindle system.

The experimental results show that the identified joint stiffness parameters are acceptable and the dynamic behavior of the spindle mounted in the machine tool column is predicted reliably.




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