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Time-Dependent Reliability Modeling and Analysis Method for Mechanics Based on Convex Process

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  • Lei Wang
  • Xiaojun Wang
  • Ruixing Wang
  • Xiao Chen

Abstract

The objective of the present study is to evaluate the time-dependent reliability for dynamic mechanics with insufficient time-varying uncertainty information. In this paper, the nonprobabilistic convex process model, which contains autocorrelation and cross-correlation, is firstly employed for the quantitative assessment of the time-variant uncertainty in structural performance characteristics. By combination of the set-theory method and the regularization treatment, the time-varying properties of structural limit state are determined and a standard convex process with autocorrelation for describing the limit state is formulated. By virtue of the classical first-passage method in random process theory, a new nonprobabilistic measure index of time-dependent reliability is proposed and its solution strategy is mathematically conducted. Furthermore, the Monte-Carlo simulation method is also discussed to illustrate the feasibility and accuracy of the developed approach. Three engineering cases clearly demonstrate that the proposed method may provide a reasonable and more efficient way to estimate structural safety than Monte-Carlo simulations throughout a product life-cycle.

Suggested Citation

  • Lei Wang & Xiaojun Wang & Ruixing Wang & Xiao Chen, 2015. "Time-Dependent Reliability Modeling and Analysis Method for Mechanics Based on Convex Process," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-16, June.
  • Handle: RePEc:hin:jnlmpe:914893
    DOI: 10.1155/2015/914893
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    Cited by:

    1. Hong, Linxiong & Li, Huacong & Fu, Jiangfeng & Li, Jia & Peng, Kai, 2022. "Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Jian Wang & Xiang Gao & Zhili Sun, 2021. "An Importance Sampling Framework for Time-Variant Reliability Analysis Involving Stochastic Processes," Sustainability, MDPI, vol. 13(14), pages 1-16, July.

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