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

Finite Element Model Updating Using Computational Intelligence Techniques

Applications to Structural Dynamics

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Published: 10th June 2010
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FEM updating allows FEMs to be tuned better to reflect measured data. It can be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. This book applies both strategies to the field of structural mechanics, using vibration data. Computational intelligence techniques including: multi-layer perceptron neural networks; particle swarm and GA-based optimization methods; simulated annealing; response surface methods; and expectation maximization algorithms, are proposed to facilitate the updating process. Based on these methods, the most appropriate updated FEM is selected, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements through the formulations of prior distributions. Case studies, demonstrating the principles test the viability of the approaches, and. by critically analysing the state of the art in FEM updating, this book identifies new research directions.

From the reviews: "This book introduces the concepts of computational intelligence for finite-element-model updating. ... This book opens new research directions in the field of computational intelligence applied in mathematical models that use finite-element updating method. I would warmly recommend this book for the under-graduated and graduated students, researchers and all the people interested in the fields of computational intelligence and the finite element method." (Razvan Raducanu, Zentralblatt MATH, Vol. 1197, 2010)

Introduction to Finite-element-model Updatingp. 1
Introductionp. 1
Finite-element Modelingp. 2
Vibration Analysisp. 5
Domains Used for Finite-element-model Updatingp. 6
Modal-domain Data (MDD)p. 6
Frequency-domain Datap. 9
Finite-element-model Updating Methodsp. 10
Computational Intelligence Methodsp. 17
Outline of the Bookp. 18
Referencesp. 18
Finite-element-model Updating Using Nelder-Mead Simplex and Newton Broyden-Fletcher-Goldfarb-Shanno Methodsp. 25
Introductionp. 25
Introduction to Structural Dynamicsp. 26
Expansion/Reduction Methodsp. 28
Model Expansion and Reduction Proceduresp. 28
Model Reductionp. 28
Model Expansionp. 31
Methods for Comparing Datap. 33
Direct Comparisonp. 33
Frequency-response Functions Assurance Criterion (FRFAC)p. 34
The Model Assurance Criterion (MAC)p. 35
The Coordinate Modal Assurance Criterion (COMAC)p. 36
Optimization Methodsp. 36
Nelder-Mead Simplex Methodp. 36
Quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) Algorithmp. 38
Example 1: Simple Beamp. 40
Example 2: Unsymmetrical H-shaped Structurep. 41
Conclusionp. 44
Further Workp. 44
Referencesp. 44
Finite-element-model Updating Using Genetic Algorithmp. 49
Introductionp. 49
Mathematical Backgroundp. 51
Genetic Algorithmp. 53
Initializationp. 56
Crossoverp. 56
Mutationp. 56
Selectionp. 57
Terminationp. 57
Nelder-Mead Simplex Optimization Methodp. 58
Example 1: Simple Beamp. 59
Example 2: Unsymmetrical H-shaped Structurep. 61
Conclusionp. 63
Future Workp. 63
Referencesp. 63
Finite-element-model Updating Using Particle-swarm Optimizationp. 67
Introductionp. 67
Mathematical Backgroundp. 69
Particle-swarm Optimizationp. 71
Genetic Algorithm (GA)p. 75
Example 1: A Simple Beamp. 76
Example 2: Unsymmetrical H-shaped Structurep. 78
Conclusionp. 81
Future Workp. 81
Referencesp. 82
Finite-element-model Updating Using Simulated Annealingp. 85
Introductionp. 85
Mathematical Backgroundp. 87
Simulated Annealing (SA)p. 87
Simulated-annealing Parametersp. 90
Transition Probabilitiesp. 91
Monte Carlo Methodp. 91
Markov Chain Monte Carlo (MCMC)p. 91
Acceptance Probability Function: Metropolis Algorithmp. 92
Cooling Schedulep. 92
Particle-swarm-optimization Methodp. 94
Example 1: Simple Beamp. 95
Example 2: Unsymmetrical H-shaped Structurep. 97
Conclusionp. 98
Future Workp. 98
Referencesp. 99
Finite-element-model Updating Using the Response-surface Methodp. 103
Introductionp. 103
Mathematical Backgroundp. 105
Response-surface Method (RSM)p. 105
Neural Networksp. 109
Multi-layer Perceptron (MLP)p. 110
Training the Multi-layer Perceptronp. 111
Back-propagation Methodp. 113
Scaled Conjugate Gradient Methodp. 114
Evolutionary Optimizationp. 115
Example 1: Simple Beamp. 117
Example 2: Unsymmetrical H-shaped Structurep. 119
Conclusionp. 121
Future Workp. 121
Referencesp. 122
Finite-element-model Updating Using a Hybrid Optimization Methodp. 127
Introductionp. 127
Introduction to Structural Dynamicsp. 128
Hybrid Particle-swarm Optimization and the Nelder-Mead Simplexp. 129
Example 1: Simple Beamp. 135
Example 2: Unsymmetrical H-shaped Structurep. 136
Conclusionp. 138
Future Workp. 138
Referencesp. 139
Finite-element-model Updating Using a Multi-criteria Methodp. 143
Introductionp. 143
Mathematical Foundationp. 144
Frequency-response Function Method (FRFM)p. 145
Modal Property Method (MPM)p. 147
Multi-criteria Methodp. 151
Optimizationp. 153
Example 1: Simple Beamp. 154
Example 2: Unsymmetrical H-shaped Structurep. 155
Conclusionp. 157
Future Workp. 157
Referencesp. 157
Finite-element-model Updating Using Artificial Neural Networksp. 161
Introductionp. 161
Bayesian Neural Networksp. 164
Stochastic Dynamics Modelp. 167
Metropolis Algorithmp. 170
Hybrid Monte Carlop. 170
Finite-element Updating Using Neural Networks and Control Theoryp. 172
Example 1: Simple Beamp. 174
Example 2: Unsymmetrical H-shaped Structurep. 176
Conclusionp. 177
Future Workp. 178
Referencesp. 178
Finite-element-model Updating Using a Bayesian Approachp. 183
Introductionp. 183
Mathematical Foundationp. 185
Dynamicsp. 185
Bayesian Methodp. 186
Markov Chain Monte Carlo Methodp. 189
MCMC: Genetic Programming and Metropolis Algorithmp. 191
Example 1: Simple Beamp. 194
Example 2: Unsymmetrical H-shaped Structurep. 196
Conclusionp. 198
Future Workp. 198
Referencesp. 199
Finite-element-model Updating Applied in Damage Detectionp. 203
Introductionp. 203
Data Used for Damage Detectionp. 205
Time Domainp. 205
Frequency Domainp. 206
Modal Domainp. 207
Time-Frequency Domainp. 207
Model Identification Methodsp. 208
Neural Networksp. 208
Support Vector Machinesp. 209
Fuzzy Logicp. 209
Rough Setsp. 210
Finite-element-model Updating Approachp. 211
Example 1: Suspended Beamp. 213
Example 2: Freely Suspended H-shaped Structurep. 215
Conclusionp. 219
Future Workp. 219
Referencesp. 219
Conclusions and Emerging State-of-the-artp. 225
Introductionp. 225
Overview of the Previous Chaptersp. 226
Outstanding Issuesp. 227
Model Selectionp. 227
Objective Functionp. 228
Data Used for Finite-element-model Updatingp. 229
Local Versus Global Optimally Updated Modelp. 229
Online Finite-element-model Updatingp. 229
The Issue of Dampingp. 230
Dealing with Nonlinearityp. 230
Nonuniquenessp. 230
Parameter Selectionp. 231
Referencesp. 231
Finite-element Modelingp. 233
Introductionp. 233
Discretization and Shape Functionsp. 233
Estimation of Mass and Stiffness Matricesp. 235
Multi-degree-of-freedom Mass-spring Systemp. 237
Dampingp. 238
Eigenvalues and Eigenvectorsp. 239
Frequency-response Functionsp. 240
Modal Property Extractionp. 242
Referencesp. 242
Introduction to Vibration Analysisp. 243
Introductionp. 243
Excitation and Response Measurementsp. 243
Amplifiersp. 244
Filterp. 244
Data-logging Systemp. 245
Signal Processingp. 245
Referencesp. 245
Bibliographyp. 247
Indexp. 249
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781849963220
ISBN-10: 1849963223
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 250
Published: 10th June 2010
Publisher: Springer London Ltd
Country of Publication: GB
Dimensions (cm): 23.5 x 15.5  x 1.91
Weight (kg): 0.62