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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.
Mrinal Kanti Sen; Subhrajit Dutta; Amir H. Gandomi; Chandrasekhar Putcha. Case Study for Quantifying Flood Resilience of Interdependent Building–Roadway Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2021, 7, 04021005 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Amir H. Gandomi, Chandrasekhar Putcha. Case Study for Quantifying Flood Resilience of Interdependent Building–Roadway Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2021; 7 (2):04021005.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Amir H. Gandomi; Chandrasekhar Putcha. 2021. "Case Study for Quantifying Flood Resilience of Interdependent Building–Roadway Infrastructure Systems." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7, no. 2: 04021005.
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.
Mrinal Kanti Sen; Subhrajit Dutta; Jahir Iqbal Laskar. A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2021, 7, 04020060 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Jahir Iqbal Laskar. A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2021; 7 (1):04020060.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Jahir Iqbal Laskar. 2021. "A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7, no. 1: 04020060.
Resilience is the capability of a system to resist any hazard and revive to a desirable performance. The consequences of such hazards require the development of resilient infrastructure to ensure community safety and sustainability. However, resilience-based housing infrastructure design is a challenging task due to a lack of appropriate post-disaster datasets and the non-availability of resilience models for housing infrastructure. Hence, it is necessary to build a resilience model for housing infrastructure based on a realistic dataset. In this work, a Bayesian belief network (BBN) model was developed for housing infrastructure resilience. The proposed model was tested in a real community in Northeast India and the reliability, recovery, and resilience of housing infrastructure against flood hazards for that community were quantified. The required data for resilience quantification were collected by conducting a field survey and from public reports and documents. Lastly, a sensitivity analysis was performed to observe the critical parameters of the proposed BBN model, which can be used to inform designers, policymakers, and stakeholders in making resilience-based decisions.
Mrinal Kanti Sen; Subhrajit Dutta; Golam Kabir. Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network. Sustainability 2021, 13, 1026 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Golam Kabir. Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network. Sustainability. 2021; 13 (3):1026.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Golam Kabir. 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network." Sustainability 13, no. 3: 1026.
Housing infrastructure is a basic human need and thus should be resilient against natural disasters. Therefore, an effective resilience-based framework for housing infrastructure is required to enhance the endurance capacity of housing and for effective recovery. Involving an expert opinion is also required for the representation of the nonlinear, complex relationship between the flood resilience parameters for housing infrastructure. In this work, initially, a hierarchical flood resilience model for housing infrastructure is developed using the decision-making trial and evaluation laboratory (DEMATEL) and the interpretive structure modeling (ISM) methods. The relationships among various resilience parameters related to flood hazards are obtained using integrated DEMATEL and ISM following the data received from expert opinions. Next, a flood resilience-based decision-making framework is developed integrating DEMATEL, ISM, and Bayesian Network (BN) methods. A field survey collects all the postdisaster data to feed in the BN model, capturing the interrelationship among the resilience parameters. Finally, the developed framework is implemented for a community in northeast India for flood resilience assessment and risk-informed decision making. It has been observed from the result that the resilience of the housing infrastructure for most of the surveyed places are extremely low, which means houses of those areas should be immediately strengthened. The evaluated resilience values provide the vulnerable scenarios of housing infrastructure of that community against flood hazards, which help the public authority of that community. Additionally, the identification of the most sensitive parameter/s among all considered parameters helps in decision making for future hazards.
Mrinal Kanti Sen; Subhrajit Dutta; Golam Kabir; Nikil N. Pujari; Shamim Ahmed Laskar. An integrated approach for modelling and quantifying housing infrastructure resilience against flood hazard. Journal of Cleaner Production 2020, 288, 125526 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Golam Kabir, Nikil N. Pujari, Shamim Ahmed Laskar. An integrated approach for modelling and quantifying housing infrastructure resilience against flood hazard. Journal of Cleaner Production. 2020; 288 ():125526.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Golam Kabir; Nikil N. Pujari; Shamim Ahmed Laskar. 2020. "An integrated approach for modelling and quantifying housing infrastructure resilience against flood hazard." Journal of Cleaner Production 288, no. : 125526.
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.
Mrinal Kanti Sen; Subhrajit Dutta. An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2020, 6, 04020045 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta. An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2020; 6 (4):04020045.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta. 2020. "An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 6, no. 4: 04020045.
Housing infrastructure is the basic need of living, and due to disaster, many houses got damaged. Therefore, assessing resilience for housing infrastructure against a flood hazard is an important task for any community as it gives the real scenario of the capability to resist and recover from the disaster after the occurrence of the hazard. The process of resilience quantification requires a different type of information from different sources, and due to which uncertainty and incomplete data may get involved. There is significantly limited literature available focusing on housing infrastructure resilience; however, the available literature has not incorporated such uncertainty and incomplete information. Therefore, in this work, a resilience assessment framework for housing infrastructure is proposed using a combination approach of Best Worst Method and a Hierarchical Evidential Reasoning based on the Dempster-Shafer theory against flood hazard. The proposed framework is then implemented in Barak valley North-East India to quantify that valley’s resilience and evaluate the model. Initially, different resilience attributes are selected, and based on experts’ opinion, the Best Worst Method rates the criteria to find the weightage. After finding the weightage, flood resilience is evaluated by using the Dempster-Shafer rule of combination. Lastly, sensitivity analysis is also performed to investigate the sensitivity of all the attributes of the proposed hierarchical housing infrastructure resilience model. The proposed flood resilience assessment model generates satisfactory results which indicate the condition state of resilience along with the unassigned degree of belief or uncertainty.
Mrinal Kanti Sen; Subhrajit Dutta; Golam Kabir. Development of flood resilience framework for housing infrastructure system: Integration of best-worst method with evidence theory. Journal of Cleaner Production 2020, 290, 125197 .
AMA StyleMrinal Kanti Sen, Subhrajit Dutta, Golam Kabir. Development of flood resilience framework for housing infrastructure system: Integration of best-worst method with evidence theory. Journal of Cleaner Production. 2020; 290 ():125197.
Chicago/Turabian StyleMrinal Kanti Sen; Subhrajit Dutta; Golam Kabir. 2020. "Development of flood resilience framework for housing infrastructure system: Integration of best-worst method with evidence theory." Journal of Cleaner Production 290, no. : 125197.