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Citation |
Sen MK, Dutta S, Laskar JI. ASCE ASME J. Risk Uncertain. Eng. Syst. A Civ. Eng. 2021; 7(1): 04020060. |
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Copyright |
(Copyright © 2021, American Society of Civil Engineers) |
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DOI |
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PMID |
unavailable |
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Abstract |
Resilience is defined as the capacity of a system to withstand a natural hazard and to regain desirable performance after the occurrence of such disasters. Natural hazards, such as floods, earthquakes, hurricanes, and tsunamis, have devastating effects on infrastructure systems. Such high-consequence events create the need for building resilient infrastructure for sustainable development. However, resilience-based infrastructure design is a challenging task, primarily due to factors such as lack of appropriate data for quantifying infrastructure resilience, and robustness of resilience models. Hence, there is a definite need to build resilience models based on realistic data and to validate such models. This paper developed a hierarchical Bayesian network (BN) model for flood resilience of housing infrastructure, and used the variable elimination (VE) method to quantify flood resilience. A study area in Barak Valley of Northeast India was selected because frequent high consequence flood events have occurred in this region. Relevant data were collected by performing an extensive field survey in various places of the valley, and were used to quantify two major factors--reliability and recovery--on which housing infrastructure resilience quantification depends. The main advantages of the proposed resilience model are that (1) it gives a realistic scenario of the infrastructure system robustness and its restoration after damage, (2) the proposed BN-based data-driven resilience model can be updated as and when more data are available, and (3) it helps planners, designers, policymakers, and stakeholders to make resilience-based decisions for sustainable communities. Language: en |
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Keywords |
Bayesian network; Flood hazard; Housing infrastructure; Resilience; Sustainable development; Variable elimination method |