Finite Element Model Updating Using Computational Intelligence Techniques

1. Front Matter

   Pages i-xv

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2. Introduction to Finite-element-model Updating

   Pages 1-24

3. Finite-element-model Updating Using Nelder–Mead Simplex and Newton Broyden–Fletcher–Goldfarb–Shanno Methods

   Pages 25-47

4. Finite-element-model Updating Using Genetic Algorithm

   Pages 49-66

5. Finite-element-model Updating Using Particle-swarm Optimization

   Pages 67-84

6. Finite-element-model Updating Using Simulated Annealing

   Pages 85-102

7. Finite-element-model Updating Using the Response-surface Method

   Pages 103-125

8. Finite-element-model Updating Using a Hybrid Optimization Method

   Pages 127-141

9. Finite-element-model Updating Using a Multi-criteria Method

   Pages 143-160

10. Finite-element-model Updating Using Artificial Neural Networks

    Pages 161-181

11. Finite-element-model Updating Using a Bayesian Approach

    Pages 183-201

12. Finite-element-model Updating Applied in Damage Detection

    Pages 203-223

13. Conclusions and Emerging State-of-the-art

    Pages 225-231

14. Back Matter

    Pages 233-250

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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.

• STATISTICA
• algorithm
• algorithms
• design
• finite element method
• genetic algorithms
• linear optimization
• mechanical engineering
• model
• optimization
• simulation
• vibration

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.”­­­ (Răzvan Răducanu, Zentralblatt MATH, Vol. 1197, 2010)

• Faculty of Engineering and the Built Environment, University of Johannesburg, Auckland Park, South Africa

  Tshilidzi Marwala

Tshilidzi Marwala is the Executive Dean of the Faculty of Engineering and the Built Environment at the University of Johannesburg. He was previously the Adhominem Professor of Electrical Engineering as well as the Carl and Emily Fuchs Chair of Systems and Control Engineering at the University of the Witwatersrand. He is a Fellow of the Royal Society of Arts as well as the Royal Statistical Society. He holds a PhD in Engineering from the University of Cambridge and a PLD from Harvard University in the USA. He was a post-doctoral research associate at Imperial College working in the general area of computational intelligence. He has been a visiting fellow at Harvard University and Cambridge University. His research interests include the application of computational intelligence to mechanical. civil, aerospace and biomedical engineering. Professor Marwala has made fundamental contributions to engineering including the development of the concept of pseudo-modal energies and the development of Bayesian framework for solving engineering problems such as finite element model updating. He has supervised 40 masters and PhD students many of whom have proceeded to distinguish themselves at universities such as Harvard, Oxford and Cambridge. He has published over 200 papers in journals such as the American Institute of Aeronautics and Astronautics Journal, proceedings and book chapters. He has published two books: Computational Intelligence for Modelling Complex Systems published by Research India Publications as well as Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques published by the IGI Global Publications (New York). His work has appeared in prestigious publications such as New Scientist. He is a senior member of the IEEE.

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