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Full Description
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.
Contents
to Finite-element-model Updating.- Finite-element-model Updating Using Nelder-Mead Simplex and Newton Broyden-Fletcher-Goldfarb-Shanno Methods.- Finite-element-model Updating Using Genetic Algorithm.- Finite-element-model Updating Using Particle-swarm Optimization.- Finite-element-model Updating Using Simulated Annealing.- Finite-element-model Updating Using the Response-surface Method.- Finite-element-model Updating Using a Hybrid Optimization Method.- Finite-element-model Updating Using a Multi-criteria Method.- Finite-element-model Updating Using Artificial Neural Networks.- Finite-element-model Updating Using a Bayesian Approach.- Finite-element-model Updating Applied in Damage Detection.- Conclusions and Emerging State-of-the-art.
