A loading contribution degree analysis-based strategy for time-variant reliability analysis of structures under multiple loading stochastic processes

Yang Zhang, Jun Xu and Paolo Gardoni

Reliability Engineering and System Safety, 2024, vol. 243, issue C

Abstract: This paper proposes a new single-loop method based on the loading contribution degree analysis (LCDA) and decoupling strategy for analyzing the time-variant reliability of structures considering multiple loading stochastic processes and degradation processes. In this method, the loading stochastic processes are first discretized using the expansion optimal linear estimation method. Then, the LCDA is carried out to obtain a total weighted loading stochastic process used to accurately reflects the structural response, thus constructing a single loading stochastic process analysis problem. Subsequently, a decoupling strategy is applied to decouple the degradation processes and the total weighted loading stochastic process to obtain the equivalent extreme-value limit state function (EEV-LSF) of the structure. Finally, an efficient probability distribution reconstruction tool is applied to derive the probability density function of EEV-LSF. Notably, the proposed LCDA can avoid the calculation of the response time series of a structure under the input loading stochastic processes, providing a novel perspective to simplify the calculation of structural extreme-value response. Additionally, the proposed method requires only one round of deterministic analysis to obtain the structural time-variant failure probability at the specified time, constructing an efficient single-loop analysis method. Three numerical examples are used to validate the proposed method.

Keywords: Time-variant reliability; Multiple loading stochastic processes; Loading contribution degree analysis; Decoupling strategy (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007470

DOI: 10.1016/j.ress.2023.109833

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