Analysis of structural reliability from time response using fully probabilistic

  • Fang Yongfeng*
  • , Chen Jianjun
  • , Kong Fah Tee
  • , Cao Hongjun
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The probability density evolution of structural dynamic prediction models under several times random loads are proposed in this paper. Firstly, the probability density of several times stochastic loads can be obtained from the probability density of single time stochastic load. The prediction models of structural reliability from time responses under several times stochastic loads with and without strength degeneration over time are derived using the stress-strength interference theory and probability density evolution method. The differential equation in the models can be solved using the forward finite difference method. The probability density functions of the structural strength redundancy at any time can be obtained. In the end, structural dynamic reliability can be calculated using integral method. The efficiency of the proposed method is demonstrated numerically through a speed reducer. The results have shown that the proposed method is practicable, feasible and gives reasonable accurate prediction.

Original languageEnglish
Pages (from-to)277-281
Number of pages5
JournalJournal of the Brazilian Society of Mechanical Sciences and Engineering
Volume36
Issue number2
DOIs
StatePublished - Feb 2014
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgments The work described in this paper was supported in part by a research grant from the National Natural Science Foundation of China (51175398) and a Foundation from Ministry of Education of China (JY10000904012).

Keywords

  • Probability density evolution
  • Random loads
  • Reliability
  • Structure

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • General Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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