An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 4
Abstract
Infrastructure resilience is defined as the ability of a system to withstand and recover from the effects of natural or man-made hazards. For any community, quantifying its sociophysical infrastructure resilience during and after any disruptive event is important for planners, designers, and decision-makers. However, a global approach for resilience quantification becomes challenging due to the fact that infrastructure systems’ performance varies from location to location and the recovery process is also complex and region-specific. In this work, an integrated Geographic Information System (GIS)-Bayesian Belief Network (BBN) framework is developed to model and quantify the resilience (vulnerability and recovery) of network infrastructure systems against flood hazards. To this end, a simple case study is demonstrated for quantifying flood resilience of a roadway network in a community in northeast India. Data collection is done using a GIS platform and a probabilistic graphical model (BBN model) is used to model uncertainties in resilience quantification based on the available data and judgments. The main contributions of the proposed resilience model are: (1) the model can provide more accurate and realistic estimates based on beliefs; (2) the model can be updated as and when more data is available; and (3) sensitivity analysis of the validated road network resilience model to facilitate risk-informed decision-making against future flood disaster.
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Data Availability Statement
Some or all the data and computer programs used for resilience quantification in this work is available with the corresponding author and can be obtained on request. Some computer code(s) and a sample calculation are provided in the manuscript for reference.
Acknowledgments
The first author (MKS) acknowledges the students’ scholarship received from ministry of human resource and development, Government of India. The second author (SD) acknowledge Siddhartha Ghosh, Professor, Department of Civil Engineering, IIT Bombay for encouraging discussion 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 6 • Issue 4 • December 2020
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© 2020 American Society of Civil Engineers.
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Received: Mar 17, 2020
Accepted: Jun 12, 2020
Published online: Sep 24, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 24, 2021
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