 Bayesian Multimodal Models for Risk Analyses of Low-Probability High-Consequence EventsArda Vanli ()
A chapter in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, 2024, pp 373-394 from Springer Abstract: 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. Date: 2024 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-53092-0_18 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-53092-0_18 Access Statistics for this chapter More chapters in Springer Optimization and Its Applications from Springer |
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