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A Decision-Making Framework Integrating Fluid and Solid Systems to Assess Resilience of Coastal Communities Experiencing Extreme Storm Events

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  • Fahad, Md Golam Rabbani
  • Nazari, Rouzbeh
  • Motamedi, M.H.
  • Karimi, Maryam

Abstract

The precise assessment of flood damage and structural resiliency is of the utmost importance for coastal communities, mitigating risk from repeated extreme storm events. However, most flood resiliency studies have been criticized for lack of accuracy and failing to depict the relationships among the hydrodynamics, structural characteristics, and community preparedness. This work intends to present an inclusive approach for quantifying community-scale flood damage and structural resiliency. Large-scale coastal flooding has been simulated and validated with semi-coupled storm surge and 2D inundation models. The depth and momentum components of flood flow were integrated with a newly developed multidimensional flood-damage assessment model, which includes the traditional depth–damage relationship as well as building height, age, configuration, and construction material to calculate the resiliency of structures as a function of recovery time, community preparedness, and level of flood-induced damage. The study region experienced flood depths ranging from 4.91 m to 8.06 m from various hurricane categories with 28.69%, 45.62%, and 92.13% damage to properties. The flood damage and resiliency results were presented via geospatial analytics at property level (i.e., microscale) and aggregated census block group (i.e., macroscale) levels, indicating the developed framework's universal applicability and scalability.

Suggested Citation

  • Fahad, Md Golam Rabbani & Nazari, Rouzbeh & Motamedi, M.H. & Karimi, Maryam, 2022. "A Decision-Making Framework Integrating Fluid and Solid Systems to Assess Resilience of Coastal Communities Experiencing Extreme Storm Events," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000643
    DOI: 10.1016/j.ress.2022.108388
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    1. Jayaraman, Deepan & Ramu, Palaniappan, 2023. "L-moments and Bayesian inference for probabilistic risk assessment with scarce samples that include extremes," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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