A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 1
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.
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Data Availability Statement
The sample data collection sheet and the data used for analysis, along with the associated computer programs, are available on request from the corresponding author.
Acknowledgments
The second author (SD) acknowledges the support extended by the District Disaster Management Authority (DDMA), Cachar, Assam by providing the information of flood-vulnerable places of Barak Valley region in Northeast India. SD acknowledges Siddhartha Ghosh, Professor, Department of Civil Engineering, IIT Bombay for encouraging discussion related to infrastructure resilience during the initial stages of this work. The first and third authors acknowledge the student scholarships received from the Ministry of Human Resource and Development (Government of India), and also acknowledge all the experts from the different fields for giving their valuable time to select the influencing parameters.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 1 • March 2021
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© 2020 American Society of Civil Engineers.
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Received: Jun 16, 2020
Accepted: Sep 16, 2020
Published online: Dec 29, 2020
Published in print: Mar 1, 2021
Discussion open until: May 29, 2021
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