Machine learning and model driven bayesian uncertainty quantification in suspended nonstructural systems

Zhiyuan Qin and M.Z. Naser

Reliability Engineering and System Safety, 2023, vol. 237, issue C

Abstract: This paper presents a novel framework for the uncertainty quantification of inverse problems often encountered in suspended nonstructural systems. This framework adopts machine learning- and model-driven stochastic Gaussian process model calibration to quantify the uncertainty via a new blackbox variational inference that accounts for geometric complexity through Bayesian inference. The soundness of the proposed framework is validated by examining one of the largest full-scale shaking table tests of suspended nonstructural systems and accompanying simulated (numerical) data. Our findings indicate that the proposed framework is computationally sound and scalable and yields optimal generalizability.

Keywords: Inverse problems; Machine learning; Gaussian process; Blackbox variational inference; Geometric complexity; Suspended nonstructural systems (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:237:y:2023:i:c:s095183202300306x

DOI: 10.1016/j.ress.2023.109392

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