Reliability Analysis Using Artificial Neural Network Based Adaptive Parameter Differential Evolution Algorithm

N. T. Bui, T. T. Nguyen, V. T. Nguyen, N. L. Tao, H. Hasegawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reliability analysis is one of the methods to consider the safety and stability of an engineering system. It is very important to determine whether a system is safe or not. We need to solve the complex nonlinear and implicit the limit state functions to obtain the reliability index. Traditional reliability analysis methods, First-Order Reliability Method (FORM), Second-Order Reliability Method (SORM), and Monte Carlo simulation (MCS), are not effective and have many limitations. In this paper, at the first step, an artificial neural network was used to model the limit state function. After that, the elite opposition-based learning differential evolution algorithm was selected to solve nonlinear equality constrained optimization problem to find the reliability index and the failure probability of problems in terms of random variables. The proposed method and some reference methods were applied to analyze the test problems in the literature to compare their effectiveness.

Original languageEnglish
Title of host publicationProceedings of the 2020 3rd International Conference on Robot Systems and Applications, ICRSA 2020
PublisherAssociation for Computing Machinery
Pages88-93
Number of pages6
ISBN (Electronic)9781450387644
DOIs
Publication statusPublished - 2020 Jun 14
Event3rd International Conference on Robot Systems and Applications, ICRSA 2020 - Chengdu, China
Duration: 2020 Jun 142020 Jun 16

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Robot Systems and Applications, ICRSA 2020
CountryChina
CityChengdu
Period20/6/1420/6/16

Keywords

  • Reliability analysis
  • differential evolution
  • global search
  • neural network
  • optimization algorithm

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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