期刊
STRUCTURES
卷 34, 期 -, 页码 3703-3715出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.istruc.2021.09.069
关键词
Structural engineering; Machine learning algorithms; Reliability; Stochastic processes; Numerical simulation
资金
- UK India Education and Research Initiative (UKIERI) [DST UKIERI-2018-19-011]
- Newton Programme Vietnam partnership [429715093]
- UK Department of Business, Energy and Industrial Strategy (BEIS)
This study proposes a novel framework using a Bayesian neural network data-driven model to compute the dynamic reliability and uncertainty quantification of structures under time-varying excitation, significantly reducing time complexity. The effectiveness and correctness of the proposed method are validated through three case studies, recommending an 11-year maintenance routine for prestressed bridge structures in marine and chemically aggressive environments.
This study proposes a novel framework computing the dynamic reliability and associated uncertainty quantification of structures under time-varying excitation with significantly reduced time complexity. For this purpose, the deep neural network's power and the Bayesian theory's probabilistic ability are leveraged, forming a Bayesian neural network data-driven model (BNN). The BNN-based surrogate model can yield a probability distribution of outputs of interest, e.g., a limit state function and its derived statistics such as median value, confidence interval rather than only a deterministic quantity. The effectiveness and correctness of the proposed method are reaffirmed via three case studies involving examples from the literature and a 3D numeral model of a prestressed reinforced concrete bridge structure, showing a reduction in time complexity up to three orders of magnitude compared to the Monte Carlo method only using finite element models. As a result, an 11-year maintenance routine is recommended for a marine and chemically aggressive environment to ensure the high reliability of prestressed bridge structures when accounting for uncertainty estimation.
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