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dc.contributor.authorICASP14
dc.contributor.authorLi, Hui
dc.contributor.author
dc.contributor.authorChen, Guohai
dc.contributor.authorYang, Dixiong
dc.date.accessioned2023-08-03T10:42:15Z
dc.date.available2023-08-03T10:42:15Z
dc.date.issued2023
dc.identifier.citationLi, Hui, Chen, Guohai, Yang, Dixiong, Direct probability integral method-based system reliability sensitivity analysis and design optimization, 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14), Dublin, Ireland, 2023.
dc.descriptionPUBLISHED
dc.description.abstractSystem reliability-based design optimization (SRBDO) can take an optimal trade-off between economy and safety in structural design with multiple failure modes, in which the evaluation of system reliability and its sensitivity is a challenging problem. Due to the complexity introduced by multiple failure modes, a unified and efficient method is required. In this study, the direct probability integral method (DPIM) is proposed to evaluate system reliability and its sensitivity efficiently, and a decoupled approach is established to attack time-invariant and time-variant SRBDO problems. Firstly, the decoupled framework based on first-order and second-order sensitivity is established for SRBDO problems. Furthermore, the first-order and second-order system reliability sensitivity with respect to distribution parameters of random variables are derived from the probability density integral equation, which significantly improves the computational efficiency. Finally, numerical example verifies the high accuracy and efficiency of DPIM for system reliability and its sensitivity analysis. It is shown that the proposed decoupled approach can address the intractable SRBDO problems efficiently.
dc.language.isoen
dc.relation.ispartofseries14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)
dc.rightsY
dc.titleDirect probability integral method-based system reliability sensitivity analysis and design optimization
dc.title.alternative14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)
dc.typeConference Paper
dc.type.supercollectionscholarly_publications
dc.type.supercollectionrefereed_publications
dc.rights.ecaccessrightsopenAccess
dc.identifier.urihttp://hdl.handle.net/2262/103214


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  • ICASP14
    14th International Conference on Application of Statistics and Probability in Civil Engineering

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