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

Author

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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)

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.

Suggested Citation

  • Mrinal Kanti Sen & Subhrajit Dutta & Golam Kabir, 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1026-:d:483563
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    1. Kattreeya Chanpariyavatevong & Warit Wipulanusat & Thanapong Champahom & Sajjakaj Jomnonkwao & Dissakoon Chonsalasin & Vatanavongs Ratanavaraha, 2021. "Predicting Airline Customer Loyalty by Integrating Structural Equation Modeling and Bayesian Networks," Sustainability, MDPI, vol. 13(13), pages 1-21, June.
    2. Maria Iglesias-Mendoza & Akilu Yunusa-Kaltungo & Sara Hadleigh-Dunn & Ashraf Labib, 2021. "Learning How to Learn from Disasters through a Comparative Dichotomy Analysis: Grenfell Tower and Hurricane Katrina Case Studies," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    3. Himadri Sen Gupta & Omar M. Nofal & Andrés D. González & Charles D. Nicholson & John W. van de Lindt, 2022. "Optimal Selection of Short- and Long-Term Mitigation Strategies for Buildings within Communities under Flooding Hazard," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    4. S. M. Amin Hosseini & Rama Ghalambordezfooly & Albert de la Fuente, 2022. "Sustainability Model to Select Optimal Site Location for Temporary Housing Units: Combining GIS and the MIVES–Knapsack Model," Sustainability, MDPI, vol. 14(8), pages 1-23, April.

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