Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network

Mrinal Kanti Sen, Subhrajit Dutta and Golam Kabir
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Mrinal Kanti Sen: Department of Civil Engineering, National Institute of Technology Silchar, Assam 788010, India
Subhrajit Dutta: Department of Civil Engineering, National Institute of Technology Silchar, Assam 788010, India
Golam Kabir: Industrial Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada

Sustainability, 2021, vol. 13, issue 3, 1-24

Abstract: Resilience is the capability of a system to resist any hazard and revive to a desirable performance. The consequences of such hazards require the development of resilient infrastructure to ensure community safety and sustainability. However, resilience-based housing infrastructure design is a challenging task due to a lack of appropriate post-disaster datasets and the non-availability of resilience models for housing infrastructure. Hence, it is necessary to build a resilience model for housing infrastructure based on a realistic dataset. In this work, a Bayesian belief network (BBN) model was developed for housing infrastructure resilience. The proposed model was tested in a real community in Northeast India and the reliability, recovery, and resilience of housing infrastructure against flood hazards for that community were quantified. The required data for resilience quantification were collected by conducting a field survey and from public reports and documents. Lastly, a sensitivity analysis was performed to observe the critical parameters of the proposed BBN model, which can be used to inform designers, policymakers, and stakeholders in making resilience-based decisions.

Keywords: resilience; housing infrastructure; Bayesian belief network; flood hazard and sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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