Reliability analysis using artificial neural networks

A probabilistic analysis approach is developed by extending the Monte Carlo simulation. The Multilayer perceptron with backpropagation learning algorithm is applied in reliability analysis as the substitute of finite element solver. The reliability of a tunnel is analyzed as an example. Through Mont...

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Bibliographic Details
Published in:2010 Sixth International Conference on Natural Computation Vol. 4; pp. 1783 - 1787
Main Authors: Changqing Qi, Jimin Wu
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2010
Subjects:
Artificial neural networks
backpropagation learning algorithm
Finite element methods
Geology
Monte Carlo methods
Monte Carlo simulation
multilayer perceptron
Reliability
reliability analysis
stochastic finite element method
Stochastic processes
Training
ISBN:1424459583, 9781424459582
ISSN:2157-9555
Online Access:Get full text
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Summary:A probabilistic analysis approach is developed by extending the Monte Carlo simulation. The Multilayer perceptron with backpropagation learning algorithm is applied in reliability analysis as the substitute of finite element solver. The reliability of a tunnel is analyzed as an example. Through Monte Carlo simulations, the input and output samples of the network are obtained. As comparing to the responses obtained by Monte Carlo simulations with finite element solver, the network performs high accuracy and fast training speed. The results show that the proposed approach is a promising tool for stochastic analysis inasmuch as the error with respect to finite element solver is negligible.
ISBN:1424459583
9781424459582
ISSN:2157-9555
DOI:10.1109/ICNC.2010.5584442