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Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network

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Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network sustainability Article Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network Mrinal Kanti Sen 1 , Subhrajit Dutta 1 and Golam Kabir 2,* 1 Department of Civil Engineering, National Institute of Technology Silchar, Assam 788010, India; mksen88@gmail.com (M.K.S.); subhrajit.nits@gmail.com (S.D.) 2 Industrial Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada * Correspondence: golam.kabir@uregina.ca Abstract: 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. Citation: Sen, M.K.; Dutta, S.; Kabir, G. Flood Resilience of Housing Keywords: resilience; housing infrastructure; Bayesian belief network; flood hazard and sensitiv- Infrastructure Modeling and ity analysis Quantification Using a Bayesian Belief Network. Sustainability 2021, 13, 1026. https://doi.org/10.3390/ su13031026 1. Introduction Academic Editor: Resilience is defined as the capability of a system to sustain against any hazard and Abdollah Shafieezadeh return to its desired performance level after the occurrence of the hazard [1]. Hosseini et al. Received: 14 December 2020 and Meerow et al. reviewed the definition of resilience in different disciplines [2,3], and its Accepted: 18 January 2021 meaning has been discussed and evaluated in the existing literature [3,4]. The reliability and Published: 20 January 2021 recovery of infrastructure are the two key dependent parameters of infrastructure resilience; furthermore, these two key parameters depend on four additional parameters: robustness, Publisher’s Note: MDPI stays neutral redundancy, rapidity, and resourcefulness, as shown in Figure1[ 1,5–7]. Robustness refers with regard to jurisdictional claims in to the sustainability of a system against the effects of the disaster, redundancy refers to published maps and institutional affil- the duplication of any critical components or functions of a system that are intended to iations. increase the reliability of the system, rapidity refers to the length of time required to return to its desired position after the occurrence of the hazard, and resourcefulness refers to the availability of resources for recovery. Reliability depends on the robustness and redundancy of the infrastructure, whereas the recovery process depends on rapidity and resourcefulness. Copyright: © 2021 by the authors. Therefore, determining the reliability of infrastructure involves considering parameters Licensee MDPI, Basel, Switzerland. based on robustness and redundancy, and similarly, determining the recoverability of This article is an open access article infrastructure involves considering parameters based on rapidity and resourcefulness. distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Sustainability 2021, 13, 1026. https://doi.org/10.3390/su13031026 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, x FORSustainability PEER REVIEW 2021, 13 , x FOR PEER REVIEW 2 of 24 2 of 24 Sustainability 2021, 13 , 1026 2 of 24 FigureFigure 1. 1.ResilienceResilience flowchartFigure flowchart 1. Resilienceof of the the dependency dependency flowchart parameters. of parameters. the dependency parameters. FigureFigure 2 represents2 representsFigure the the generalized 2 generalizedrepresents perfor the performance generalizedmance of ofa perforsystem/infrastructure a system/infrastructuremance of a system/infrastructure over over its its over its serviceservice life life[7,8], [7 ,where8service], where A liferepresents A [7,8], represents where the theinitial A represents initial condition condition the of initial a of system a condition system (which (which of isa systemgenerally is generally (which is generally consideredconsidered to be to 100% beconsidered 100% performance); performance); to be 100% AB performance); ABand and DE DErepresent represent AB and the theDEgradual gradualrepresent degradation degradation the gradual in indegradation in systemsystem performance performancesystem due due performanceto to operational operational due cond conditions; to itions;operational BCBCrepresents represents conditions; a suddena BCsudden represents drop drop in system ina sudden drop in 1 systemperformance performance duesystem todue a disaster,toperformance a disaster, which duewhich isalso to ais known disaster,also known as which loss; as CT isloss;1 alsorepresents CT known1 represents the as robustnessloss; the CT represents the 1 2 robustnessof a system; of a Tsystem;1Trobustness2 represents T1T2 represents of the a system; time requiredthe T timeT represents forrequired the recovery thefor timethe of recovery required the system; offor the CDthe system; representsrecovery of the system; CD therepresents recovery the profileCD recovery represents of the system.profile the of Figurerecovery the 2system. shows profile that,Figure of initially,the 2 system.shows the system/infrastructurethat,Figure initially, 2 shows the that, initially, the system/infrastructureperformance degradessystem/infrastructure performance with time degrades due performance to natural with time causes. degrades due Then,to winaturalth due time tocauses. thedue occurrenceto Then, natural due causes. of a Then, due to thedisaster, occurrence the performance toof thea disaster, occurrence level the sharply performanceof a disaster, declines. levelthe The performance losssharply that declines. is shown level sharply inThe the loss figure declines. that mainly is The loss that is showndepends in the on figure theshown impactmainly in ofdependsthe the figure disaster on mainly the and impact depends the robustnessof theon thedisaster impact of the and system/infrastructure,of thethe robustnessdisaster and of the robustness of the whichsystem/infrastructure, means thatthe ifsystem/infrastructure, the which resistance means ability that whichif of the the resistancemeans system that is ability veryif the high, ofresistance the then system theability lossesis very of the due system is very high,to then the disasterthe losseshigh, will due bethen to very the the disaster low.losses The due will losses to be the very candisaster low. be estimated The will losses be very usingcan low. be the estimatedThe Hazus losses technicalusingcan be estimated using the manualHazus technical createdthe by manual Hazus the Federal createdtechnical Emergency by manual the Federal Management created Emergency by the Agency Federal Management [9 ].Emergency This manual Agency Management provides [9]. Agency [9]. Thisseveral manual methodologies providesThis severalmanual for multihazard methodologiesprovides several loss for estimation. methodologies multihazard After lossfor the multihazardestimation. loss, the system Afterloss estimation. triesthe to After the loss, the system tries to recover to its baseline performance by following a recovery profile, loss,recover the system to its tries baseline to recover performance to its baseline by following performance a recovery by following profile, which a recovery is uncertain profile, and which is uncertain and dependent on the type of infrastructure system. There are three whichdependent is uncertain on the and type dependent of infrastructure on the type system. of infrastructure There are three system. types ofThere recovery are three profiles: types of recovery profiles: linear, non-linear, and stepped. The restoration of roads and typeslinear, of recovery non-linear, profiles: and stepped. linear, non-linear, The restoration and ofstepped. roads and The bridges, restoration for example, of roads typically and bridges, for example, typically follows a stepped recovery pattern. bridges,follows for aexample, stepped recoverytypically pattern.follows a stepped recovery pattern. Figure 2. System Performance over its service Life. FigureFigure 2. System 2. System Performance Performance over over its service its service Life. Life. Sustainability 2021, 13, x FOR PEER REVIEW 3 of 24 1.1. Socio-Physical Infrastructure Over the past decade, resilience quantification for communities has been an active area of research for both scientists and engineers. Engineering resilience is relatively new and currently developing, and valuable resources are available for the development of Sustainability 2021, 13, 1026 new engineering practices, codes, and regulations [10]. A community is defined as a group3 of 24 of people living in a
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