Case Study for Quantifying Flood Resilience of Interdependent Building–Roadway Infrastructure Systems
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 2
Abstract
Resilience is defined as the ability of a system to withstand and recover to a desired level of performance after the occurrence of a hazard. Community resilience has a significant socioeconomic implication for any disaster. Therefore, attempting to quantify resilience after a disaster is of utmost importance, particularly for planners, designers, and decision makers. Modern society depends on various infrastructure system networks to ensure functionality, and these infrastructure systems perform on their own and also perform interdependently with other infrastructure networks during natural hazards. For quantifying resilience, the interdependency between infrastructure systems plays a significant role; for instance, in the event of building damage, the state of damage to the roadways network is also crucial for the recovery process and ultimately in resilience. As a result, large-scale disruption of any infrastructure network increases significantly because of interdependency. In this work, an integrated geographic information system (GIS) and Bayesian belief network (BBN) framework is developed to study the resilience and effects in functionality due to interdependency among building and roadways infrastructure systems in a community. GIS is used for data collection, and BBN is adopted for computing the posterior probabilities of resilience. The framework is then implemented in a study area of Barak Valley in North-East India, and resilience is evaluated for the considered building-roadways network. Sensitivity analysis of system resilience to the critical components is performed to facilitate decision making under uncertainty. Finally, some general recommendations are given for improving flood resilience for future disasters.
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Data Availability Statement
All data and computer programs used, generated, or analyzed during the study are included in the published paper and Supplemental Materials.
Acknowledgments
The authors highly acknowledge the reviewers for their valuable comments that have enhanced the quality of the manuscript. The first author (MKS) acknowledges the students’ scholarship received from the ministry of human resource and development, Government of India. The second author (SD) acknowledges Siddhartha Ghosh, Professor, Department of Civil Engineering, IIT Bombay, for some encouraging discussions related to infrastructure resilience during the initial stages of this work. Both the authors 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.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 2 • June 2021
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© 2021 American Society of Civil Engineers.
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Received: Dec 23, 2019
Accepted: Nov 4, 2020
Published online: Jan 31, 2021
Published in print: Jun 1, 2021
Discussion open until: Jun 30, 2021
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