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

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.

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

“…The concept emphasized the connection between resilience and systems. Since the 1990s, scholars have extended the study of resilience to various domains such as urban systems [35][36][37], complex system design [38,39], supply chains [40,41], manufacturing industries [42,43], and infrastructure resilience [44][45][46]. The concept of resilience has gradually gained recognition and importance in many fields.…”

Predicting Airline Customer Loyalty by Integrating Structural Equation Modeling and Bayesian Networks

“…Perceived value, directly and indirectly, impacts customer loyalty through customer satisfaction, while perceived service quality and customer expectation influence customer loyalty only indirectly. The BN comprises two elements, represented as 𝐵〈𝐺, 𝛩〉, where 𝐺 stands for the DAG consisting of nodes and edges [74]. Joint probability distribution is represented by 𝛩.…”

“…As a result, since the high state has the highest proportion, high consumer loyalty is likely to happen. The mean value and standard deviation are presented at the bottom of the The BN comprises two elements, represented as BG, Θ, where G stands for the DAG consisting of nodes and edges [74]. Joint probability distribution is represented by Θ.…”

“…This approach calculates variance reduction of the expected real value of a query node R (i.e., customer loyalty) due to a finding caused by varying an input variable node P, such as customer satisfaction. Thus, variance of the real value of R given evidence P, V(R|q) is calculated using the following equation [74].…”

Learning How to Learn from Disasters through a Comparative Dichotomy Analysis: Grenfell Tower and Hurricane Katrina Case Studies

“…Sustainability is a very broad topic that covers virtually every aspect of human endeavours, which sometimes makes it difficult to have a single definition. Although not limited to these, it covers the management of the natural environment, built environment, well-being, raw materials, energy, people and knowledge [1][2][3]. Therefore, sustainability can be defined as a multifaceted endeavour aimed at enhancing the quality of life for people without necessarily compromising the integrity of their environments and the ability of future generations to meet their own needs [4].…”