Finite Element Model Updating Using Computational Intelligence Techniques
Applications to Structural Dynamics
(Sprache: Englisch)
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...
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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.
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.
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.
Inhaltsverzeichnis zu „Finite Element Model Updating Using Computational Intelligence Techniques “
- Introduction to Finite Element Model Updating- Finite Element Model Updating Using Neural Networks
- Maximum Likelihood Approach to Finite Element Model Updating
- Finite Element Model Updating Using Evolutionary Optimization Techniques: Genetic Algorithms and Particle Swarm Optimization
- Finite Element Model Updating Using Simulated Annealing
- Finite Element Model Updating Using Response Surface Method
- Bayesian Approach to Finite Element Model Updating
- Model Selection in Finite Element Model Updating
- Expectation Maximization Algorithm for Finite Element Model Updating
- Damage Detection Using Finite Element Model Updating
Autoren-Porträt von 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.
Bibliographische Angaben
- Autor: Tshilidzi Marwala
- 2010, 2010, 250 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, London
- ISBN-10: 1849963223
- ISBN-13: 9781849963220
- Erscheinungsdatum: 10.06.2010
Sprache:
Englisch
Rezension zu „Finite Element Model Updating Using Computational Intelligence Techniques “
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)
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