A probabilistic framework for post-disaster recovery modeling of buildings and electric power networks in developing countries

Eyitayo A. Opabola and Carmine Galasso

Reliability Engineering and System Safety, 2024, vol. 242, issue C

Abstract: Post-disaster recovery is a significant challenge, especially in developing countries. Various technical, environmental, socioeconomic, political, and cultural factors substantially influence post-disaster recovery. As a result, methodologies relevant in developed nations may not be directly applicable in Global South contexts. This study introduces a probabilistic framework for modeling the post-disaster recovery of buildings and electric power networks (EPNs) in developing countries. The proposed framework combines an asset-level assessment of individual buildings with a community-level assessment of EPNs to evaluate a building portfolio's post-event functionality state. As part of the framework, a stochastic network analysis is proposed to estimate the recovery time of damaged buildings while accounting for technical, environmental, socioeconomic, political, and cultural factors, quantified using empirical data gathered from past events. A probabilistic modeling approach is proposed to quantify the EPN's initial post-event outage levels. Empirical formulations for estimating the recovery time of an EPN as a function of its initial post-event outage levels are calibrated using post-event data from developing countries. A case study is presented to illustrate the application of the proposed framework to model the post-earthquake recovery of a synthetic low-income residential community. The analysis shows that negative technical, environmental, socioeconomic, political, and cultural factors could amplify the reconstruction time of damaged buildings by a factor of almost three. The proposed framework can support decision-makers in disaster planning and management strategies for vulnerable low-income communities.

Keywords: Post-earthquake recovery; Developing countries; Resilience; Electric power networks; Decision support (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023005938

DOI: 10.1016/j.ress.2023.109679

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