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A non-probabilistic time-variant method for structural reliability analysis

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Listed:
  • Tengda Xin
  • Jiguang Zhao
  • Cunyan Cui
  • Yongsheng Duan

Abstract

Time-variant reliability problems commonly occur in practical engineering due to the deterioration in material properties, external disturbance and other uncertain factors. Considering the non-probabilistic method can effectively deal with the uncertainties in reliability analysis. Based on the stress–strength interference method and interval method, a time-variant stress–strength interference interval model is established by considering the stress and strength as time-variant intervals. And then, the stress and strength intervals are converted into the normalized intervals to define the non-probabilistic time-variant reliability index η according to the different relationships between the limit state function and the normalized intervals. The structural state at any time can be described by the non-probabilistic time-variant reliability index η ∈ [ 0 , + ∞ ) . In addition, a strength power exponential degradation model is given as an example to clearly verify the non-probabilistic time-variant method, and the analysis results are compared with the interval method, the uniform distribution stress–strength interference method and the normal distribution stress–strength interference method, which confirm that the non-probabilistic time-variant method is feasible and valid to analyze the structural time-variant reliability without the probability density functions of the parameters.

Suggested Citation

  • Tengda Xin & Jiguang Zhao & Cunyan Cui & Yongsheng Duan, 2020. "A non-probabilistic time-variant method for structural reliability analysis," Journal of Risk and Reliability, , vol. 234(5), pages 664-675, October.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:5:p:664-675
    DOI: 10.1177/1748006X20928196
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    References listed on IDEAS

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    4. Peng, Yizhen & Wang, Yu & Zi, YanYang & Tsui, Kwok-Leung & Zhang, Chuhua, 2017. "Dynamic reliability assessment and prediction for repairable systems with interval-censored data," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 301-309.
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