Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering
- 4h 14m
- Ilyes Boulkaibet, Sondipon Adhikari, Tshilidzi Marwala
- John Wiley & Sons (UK)
Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering
Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering.
The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering.
Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering.
- Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students.
- Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations.
The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.
About the Authors
Tshilidzi Marwala is a Professor of Mechanical and Electrical Engineering as well as Deputy Vice-Chancellor at the University of Johannesburg. He holds a Bachelor of Science in Mechanical Engineering from Case Western Reserve University, a Master of Mechanical Engineering from the University of Pretoria, a PhD in Engineering from Cambridge University and was a post-doctoral researcher at Imperial College (London). He is a Fellow of TWAS and a distinguished member of the ACM. His research interests are multi-disciplinary and include the applications of computational intelligence to engineering, computer science, finance, social science and medicine. He has supervised 45 Masters and 19 PhD students and has published 8 books and over 260 papers. He is an associate editor of the International Journal of Systems Science.
Dr. Ilyes Boulkaibet is currently a researcher at the University of Johannesburg. He received a PhD from the University of Johannesburg, a second MSc from Stellenbosch University, an MSc from the University of Constantine 1 Algeria, and a Bachelor of Engineering from University of Constantine 1 Algeria. Dr. Ilyes Boulkaibet has published papers in international journals and has participated in numerous conferences including the International Modal Analysis Conference. Dr. Boulkaibet’s research areas are multidisciplinary in nature and include uncertainty quantification in computational mechanics, dynamics of complex systems, inverse problems for linear and non-linear dynamics and control systems.
Professor Adhikari is the chair of Aerospace Engineering in the College of Engineering of Swansea University. He received his MSc from the Indian Institute of Science and a PhD from the University of Cambridge. He was a lecturer at the Bristol University and a Junior Research Fellow in Fitzwilliam College, Cambridge. He has been a visiting Professor at the University of Johannesburg, Carleton University and the Los Alamos National Laboratory . Professor Adhikari's research areas are multidisciplinary in nature and include uncertainty quantification in computational mechanics, bio- and nano-mechanics (nanotubes, graphene, cell mechanics, nano-bio sensors), dynamics of complex systems, inverse problems for linear and non-linear dynamics and vibration energy harvesting.
In this Book
Introduction to Finite Element Model Updating
Model Selection in Finite Element Model Updating
Bayesian Statistics in Structural Dynamics
Metropolis-Hastings and Slice Sampling for Finite Element Updating
Dynamically Weighted Importance Sampling for Finite Element Updating
Adaptive Metropolis–Hastings for Finite Element Updating
Hybrid Monte Carlo Technique for Finite Element Model Updating
Shadow Hybrid Monte Carlo Technique for Finite Element Model Updating
Separable Shadow Hybrid Monte Carlo in Finite Element Updating
Evolutionary Approach to Finite Element Model Updating
Adaptive Markov Chain Monte Carlo Method for Finite Element Model Updating
Conclusions and Further Work