A resource allocation framework for predisaster resilience management of interdependent infrastructure networks. Issue 2 (28th December 2020)

• Record Type: Journal Article
• Authors: Sun, Jingran; Balakrishnan, Srijith; Zhang, Zhanmin
• Is Part Of: Built environment project and asset management. Volume 11:Issue 2(2021)
• Page Start: 284
• Page End: 303
• Publication Date: 2020-12-28
• Subjects: Infrastructure interdependencies -- Infrastructure resilience -- Resource allocation -- Agent-based modeling -- Simulation-based optimization -- Critical infrastructure -- Infrastructure management; Construction industry -- Management -- Periodicals; Construction projects -- Management -- Periodicals; Public works -- Management -- Periodicals; Sustainable buildings -- Periodicals; 624.068
• DOI: 10.1108/BEPAM-06-2020-0109
• Languages: English
• ISSNs: 2044-124X

Abstract

Abstract : Purpose: Resource allocation is essential to infrastructure management. The purpose of this study is to develop a methodological framework for resource allocation that takes interdependencies among infrastructure systems into consideration to minimize the overall impact of infrastructure network disruptions due to extreme events. Design/methodology/approach: Taking advantage of agent-based modeling techniques, the proposed methodology estimates the interdependent effects of a given infrastructure failure which are then used to optimize resource allocation such that the network-level resilience is maximized. Findings: The findings of the study show that allocating resources with the proposed methodology, where optimal infrastructure reinforcement interventions are implemented, can improve the resilience of infrastructure networks with respect to both direct and interdependent risks of extreme events. These findings are also verified by the results of two case studies. Practical implications: As the two case studies have shown, the proposed methodological framework can be applied to the resource allocation process in asset management practices. Social implications: The proposed methodology improves the resilience of the infrastructure network, which can alleviate the social and economic impact of extreme events on communities. Originality/value: Capitalizing on the combination of agent-based modeling and simulation-based optimization techniques, this study fulfills a critical gap in infrastructure asset management by incorporating infrastructure interdependence and resilience concepts into the resource allocation process.