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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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    2. Chen, Edward & Bao, Han & Dinh, Nam, 2024. "Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    3. Abaei, Mohammad Mahdi & Leira, Bernt Johan & Sævik, Svein & BahooToroody, Ahmad, 2024. "Integrating physics-based simulations with gaussian processes for enhanced safety assessment of offshore installations," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    4. Buchwald, J. & Kolditz, O. & Nagel, T., 2024. "Design-of-Experiment (DoE) based history matching for probabilistic integrity analysis—A case study of the FE-experiment at Mont Terri," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. D'Angela, Danilo & Magliulo, Gennaro, 2025. "Methodological guidance and quantitative measures regarding seismic capacity and safety of freestanding and inelastic anchored nonstructural elements housed in ordinary and critical facilities," Reliability Engineering and System Safety, Elsevier, vol. 260(C).

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