Bayesian support vector machine for optimal reliability design of modular systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number108840
Journal / PublicationReliability Engineering and System Safety
Online published17 Sep 2022
Publication statusPublished - Dec 2022


In a modular system, uncertainties will spread among coupled modules and cause system failure. To cope with this issue, the reliability-based design optimization (RBDO) of modular systems came into being. However, the solution of this design task is a nested triple-loop process, making the computational burden unaffordable for real-world systems. Thus, this paper endeavors to effectively mitigate this computational effort. The individual module feasible approach is first proposed to tackle the coupling effects of modules, whereby, the original optimization problem is converted into a conventional one. Then, the Bayesian-inference-based support vector machine is utilized to build the alternative model for the actual probabilistic constraint function, in the augmented reliability space. The alternative model is constructed using small number of model evaluations, which possesses enough precision everywhere in the augmented confidence region. Finally, the optimal decision scheme is obtained by solving the formulated conventional RBDO using the alternative model. The performance of the proposed method is investigated using several examples.

Research Area(s)

  • Augmented reliability space, Modular system, Reliability-based design optimization, Support vector machine, Uncertainty