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

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  • Qin, Zhiyuan
  • Naser, M.Z.

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.

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  • Qin, Zhiyuan & Naser, M.Z., 2023. "Machine learning and model driven bayesian uncertainty quantification in suspended nonstructural systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s095183202300306x
    DOI: 10.1016/j.ress.2023.109392
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    References listed on IDEAS

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