A Bayesian Network Modeling Approach for Time-Varying Flood Resilience Assessment of Housing Infrastructure System
Publication: Natural Hazards Review
Volume 23, Issue 2
Abstract
Natural hazard causes severe types of damage to infrastructure systems at regular intervals, and the occurrence of such events is inevitable. The concept of resilience is adopted to make infrastructure systems more reliable, adaptable, and recoverable against natural disasters. Resilience is defined as the ability of an infrastructure to resist the impact of a disaster and bounce back to its desirable performance level after the disaster. Recovery of infrastructure, which forms a part of resiliency, is a time-dependent process in nature. In this work, a dynamic Bayesian network (BN) model is developed for the resilience assessment of housing infrastructure against flood hazards. The proposed resilience model is then implemented in the testbed of Barak Valley in North-East India. An extensive field survey is performed to collect relevant indicator data for resilience assessment and validation. To assess the recovery process, the housing infrastructure resiliency of the Barak Valley testbed is evaluated and compared between multiple flood event time periods. Lastly, the most critical indicators of the proposed dynamic BN model are identified by performing sensitivity analysis. This study will help the planner, designers, policymakers, and stakeholders to provide resilience-based decisions on flood resiliency of housing infrastructure systems.
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Data Availability Statement
All the data and computer programs used for resilience quantification in this work are available with the corresponding author and can be obtained on request. Sample calculations are provided in the manuscript for reference.
Acknowledgments
The first author (MKS) acknowledges the students’ scholarship received from the ministry of human resource and development, Government of India. Both the authors also gratefully acknowledge Golam Kabir, Assistant Professor, Faculty of Engineering and Applied Sciences, University of Regina, Canada for the valuable discussion on sensitivity analysis and model validation. The second author (SD) gratefully acknowledges the support extended by District Disaster Management Authority, Silchar during this work.
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© 2022 American Society of Civil Engineers.
History
Received: Aug 25, 2021
Accepted: Nov 16, 2021
Published online: Feb 7, 2022
Published in print: May 1, 2022
Discussion open until: Jul 7, 2022
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