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Abstract In the aftermath of Hurricane Ike in 2008 in the United States, the “Ike Dike” was proposed as a coastal barrier system, featuring floodgates, to protect the Houston‐Galveston area (HGA) from future storm surges. Given its substantial costs, the feasibility and effectiveness of the Ike Dike have been subjects of investigation. In this study, we evaluated these aspects under both present and future climate conditions by simulating storm surges using a set of models. Delft3D Flexible Mesh Suite was utilized to simulate hydrodynamic and wave motions driven by hurricanes, with wind and pressure fields spatialized by the Holland model. The models were validated against data from Hurricane Ike and were used to simulate synthetic hurricane tracks downscaled from several general circulation models and based on different sea level rise projections, both with and without the Ike Dike. Flood maps for each simulation were generated, and probabilistic flood depths for specific annual exceedance probabilities were predicted using annual maxima flood maps. Building damage curves were applied to residential properties in the HGA to calculate flood damage for each exceedance probability, resulting in estimates of expected annual damage as a measure of quantified flood risk. Our findings indicate that the Ike Dike significantly mitigates storm surge risk in the HGA, demonstrating its feasibility and effectiveness. We also found that the flood risk estimates are sensitive to hurricane intensity, the choice of damage curve, and the properties included in the analysis, suggesting that careful consideration is needed in future studies.
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Bayesian Multimodal Models for Risk Analyses of Low-Probability High-Consequence Events
This paper reviews a set of Bayesian model updating methodologies for quantification of uncertainty in multi-modal 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.
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- Award ID(s):
- 2101091
- PAR ID:
- 10532501
- Editor(s):
- Gaw, N; Pardalos, PM; Gahrooei, MR
- Publisher / Repository:
- Springer Optimization and Its Applications from Springer
- Date Published:
- Journal Name:
- Multimodal and Tensor Data Analytics for Industrial Systems Improvement. Springer Optimization and Its Applications
- Edition / Version:
- Springer Optimization and Its Applications
- Volume:
- 211
- ISSN:
- 978-3031530913
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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