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Bayesian Multimodal Models for Risk Analyses of Low-Probability High-Consequence Events

In: Multimodal and Tensor Data Analytics for Industrial Systems Improvement

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  • Arda Vanli

    (Florida A&M University—Florida State University College of Engineering)

Abstract

This paper reviews a set of Bayesian model updating methodologies for quantification of uncertainty in multimodal models for estimating failure probabilities in rare hazard events. Specifically, a two-stage Bayesian regression model is proposed to fuse an analytical capacity model with experimentally observed capacity data to predict failure probability of residential building roof systems under severe wind loading. The ultimate goals are to construct fragility models accounting for uncertainties due to model inadequacy (epistemic uncertainty) and lack of experimental data (aleatory uncertainty) in estimating failure (exceedance) probabilities and number of damaged buildings in building portfolios. The proposed approach is illustrated on a case study involving a sample residential building portfolio under scenario hurricanes to compare the exceedance probability and aggregate expected loss to determine the most cost-effective wind mitigation options.

Suggested Citation

  • Arda Vanli, 2024. "Bayesian Multimodal Models for Risk Analyses of Low-Probability High-Consequence Events," Springer Optimization and Its Applications, in: Nathan Gaw & Panos M. Pardalos & Mostafa Reisi Gahrooei (ed.), Multimodal and Tensor Data Analytics for Industrial Systems Improvement, pages 373-394, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-53092-0_18
    DOI: 10.1007/978-3-031-53092-0_18
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