A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure SystemsSource: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2021:;Volume ( 007 ):;issue: 001::page 04020060-1DOI: 10.1061/AJRUA6.0001108Publisher: ASCE
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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contributor author | Mrinal Kanti Sen | |
contributor author | Subhrajit Dutta | |
contributor author | Jahir Iqbal Laskar | |
date accessioned | 2022-01-31T23:58:27Z | |
date available | 2022-01-31T23:58:27Z | |
date issued | 3/1/2021 | |
identifier other | AJRUA6.0001108.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4270672 | |
description 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. | |
publisher | ASCE | |
title | A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems | |
type | Journal Paper | |
journal volume | 7 | |
journal issue | 1 | |
journal title | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering | |
identifier doi | 10.1061/AJRUA6.0001108 | |
journal fristpage | 04020060-1 | |
journal lastpage | 04020060-18 | |
page | 18 | |
tree | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2021:;Volume ( 007 ):;issue: 001 | |
contenttype | Fulltext |