product iamge

Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Download
Search on Amazon

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process.

Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include:

• multi-layer perceptron neural networks for real-time FEM updating;

• particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models;

• simulated annealing to put the methodologies into a sound statistical basis; and

• response surface methods and expectation maximization algorithms to demonstrate how FEM updating can be performed in a cost-effective manner; and to help manage computational complexity.

Based on these methods, the most appropriate updated FEM is selected using the Bayesian approach, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements systematically through the formulations of prior distributions. Throughout the text, case studies, specifically designed to demonstrate the special principles are included. These serve to test the viability of the new approaches in FEM updating.

Finite Element Model Updating Using Computational Intelligence Techniques analyses the state of the art in FEM updating critically and based on these findings, identifies new research directions, making it of interest to researchers in strucural dynamics and practising engineers using FEMs. Graduate students of mechanical, aerospace and civil engineering will also find the text instructive.

Author(s): Tshilidzi Marwala
Edition: 1st Edition.
Publisher: Springer
Year: 2010

Language: English
Pages: 254
Tags: Математика;Вычислительная математика;Метод конечных элементов;

Finite-element-model
Updating Using
Computional Intelligence
Techniques......Page 1
ISBN 1849963223......Page 4
Foreword......Page 5
Preface......Page 7
Acknowledgements......Page 8
Contents......Page 9
1.1 Introduction......Page 14
1.2 Finite-element Modeling......Page 15
1.3 Vibration Analysis......Page 18
1.4.1 Modal-domain Data (MDD)......Page 19
1.4.2 Frequency-domain Data......Page 22
1.5 Finite-element-model Updating Methods......Page 23
1.6 Computational Intelligence Methods......Page 30
References......Page 31
2.1 Introduction......Page 38
2.2 Introduction to Structural Dynamics......Page 39
2.3.2 Model Reduction......Page 41
2.3.3 Model Expansion......Page 44
2.4.1 Direct Comparison......Page 46
2.4.2 Frequency-response Functions Assurance Criterion (FRFAC)......Page 47
2.4.3 The Model Assurance Criterion (MAC)......Page 48
2.5.1 Nelder–Mead Simplex Method......Page 49
2.5.2 Quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) Algorithm......Page 51
2.6 Example 1: Simple Beam......Page 53
2.7 Example 2: Unsymmetrical H-shaped Structure......Page 54
References......Page 57
3.1 Introduction......Page 61
3.2 Mathematical Background......Page 63
3.3 Genetic Algorithm......Page 65
3.3.3 Mutation......Page 68
3.3.5 Termination......Page 69
3.4 Nelder–Mead Simplex Optimization Method......Page 70
3.5 Example 1: Simple Beam......Page 71
3.6 Example 2: Unsymmetrical H-shaped Structure......Page 73
References......Page 75
4.1 Introduction......Page 79
4.2 Mathematical Background......Page 81
4.3 Particle-swarm Optimization......Page 83
4.4 Genetic Algorithm (GA)......Page 87
4.5 Example 1: A Simple Beam......Page 88
4.6 Example 2: Unsymmetrical H-shaped Structure......Page 90
4.8 Future Work......Page 93
References......Page 94
5.1 Introduction......Page 97
5.3 Simulated Annealing (SA)......Page 99
5.3.1 Simulated-annealing Parameters......Page 102
5.3.4 Markov Chain Monte Carlo (MCMC)......Page 103
5.3.6 Cooling Schedule......Page 104
5.4 Particle-swarm-optimization Method......Page 106
5.5 Example 1: Simple Beam......Page 107
5.6 Example 2: Unsymmetrical H-shaped Structure......Page 109
5.8 Future Work......Page 110
References......Page 111
6.1 Introduction......Page 115
6.3 Response-surface Method (RSM)......Page 117
6.4 Neural Networks......Page 121
6.4.1 Multi-layer Perceptron (MLP)......Page 122
6.4.2 Training the Multi-layer Perceptron......Page 123
6.4.3 Back-propagation Method......Page 125
6.4.4 Scaled-conjugate-gradient Method......Page 126
6.5 Evolutionary Optimization......Page 127
6.6 Example 1: Simple Beam......Page 129
6.7 Example 2: Unsymmetrical H-shaped Structure......Page 131
6.9 Future Work......Page 133
References......Page 134
7.1 Introduction......Page 138
7.2 Introduction to Structural Dynamics......Page 139
7.3 Hybrid Particle-swarm Optimization and the Nelder–Mead Simplex......Page 140
7.4 Example 1: Simple Beam......Page 146
7.5 Example 2: Unsymmetrical H-shaped Structure......Page 147
7.7 Future Work......Page 149
References......Page 150
8.1 Introduction......Page 153
8.2 Mathematical Foundation......Page 154
8.2.1 Frequency-response Function Method (FRFM)......Page 155
8.2.2 Modal Property Method (MPM)......Page 157
8.2.3 Multi-criteria Method (MCM)......Page 161
8.3 Optimization......Page 163
8.4 Example 1: Simple Beam......Page 164
8.5 Example 2: Unsymmetrical H-shaped Structure......Page 165
References......Page 167
9.1 Introduction......Page 171
9.2 Bayesian Neural Networks......Page 174
9.2.1 Stochastic Dynamics Model......Page 177
9.2.3 Hybrid Monte Carlo......Page 180
9.3 Finite-element-model Updating Using Neural Networks and Control Theory......Page 182
9.4 Example 1: A Simple Beam......Page 184
9.5 Example 2: Unsymmetrical H-shaped Structure......Page 186
9.6 Conclusion......Page 187
References......Page 188
10.1 Introduction......Page 192
10.2.1 Dynamics......Page 194
10.2.2 Bayesian Method......Page 195
10.2.3 Markov Chain Monte Carlo Method......Page 198
10.2.4 MCMC: Genetic Programming and Metropolis Algorithm......Page 200
10.3 Example 1: Simple Beam......Page 203
10.4 Example 2: Unsymmetrical H-shaped Structure......Page 205
10.6 Future Work......Page 207
References......Page 208
11.1 Introduction......Page 211
11.2.1 Time Domain......Page 213
11.2.2 Frequency Domain......Page 214
11.2.4 Time–Frequency Domain......Page 215
11.3.1 Neural Networks......Page 216
11.3.3 Fuzzy Logic......Page 217
11.3.4 Rough Sets......Page 218
11.4 Finite-element-updating Approach......Page 219
11.5 Example 1: Suspended Beam......Page 221
11.6 Example 2: Freely Suspended H-shaped Structure......Page 223
References......Page 227
12.1 Introduction......Page 232
12.2 Overview of the Previous Chapters......Page 233
12.3.1 Model Selection......Page 234
12.3.2 Objective Function......Page 235
12.3.5 Online Finite-element-model Updating......Page 236
12.3.8 Nonuniqueness......Page 237
References......Page 238
A.2 Discretization and Shape Functions......Page 239
A.3 Estimation of Mass and Stiffness Matrices......Page 241
A.4 Multi-degree-of-freedom Mass–Spring System......Page 243
A.5 Damping......Page 244
A.6 Eigenvalues and Eigenvectors......Page 245
A.7 Frequency-response Functions......Page 246
References......Page 248
B.2 Excitation and Response Measurements......Page 249
B.4 Filter......Page 250
References......Page 251
Biography......Page 252
Index......Page 253