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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Veröffentlicht in:2010 Sixth International Conference on Natural Computation Jg. 4; S. 1783 - 1787
Hauptverfasser: Changqing Qi, Jimin Wu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2010
Schlagworte:
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
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Abstract 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.
AbstractList 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.
Author Jimin Wu
Changqing Qi
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  fullname: Jimin Wu
  organization: Coll. of Earth Sci. & Eng., Hohai Univ., Nanjing, China
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Snippet A probabilistic analysis approach is developed by extending the Monte Carlo simulation. The Multilayer perceptron with backpropagation learning algorithm is...
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SubjectTerms 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
Title Reliability analysis using artificial neural networks
URI https://ieeexplore.ieee.org/document/5584442
Volume 4
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