Review of Empirical Quantitative Data Use in Lifeline Infrastructure Restoration Modeling
Publication: Natural Hazards Review
Volume 22, Issue 4
Abstract
Disaster recovery is considered one of the less-understood phases of the disaster cycle. In particular, the literature on lifeline infrastructure restoration modeling frequently mentions the lack of available data. Despite limitations, there is a growing body of research on modeling lifeline infrastructure restoration using empirical quantitative data. This study reviewed this body of literature and identified the data collection and usage patterns present across modeling approaches to inform future efforts. We classified the modeling approaches into simulation, optimization, and statistical modeling. The number of publications in this domain has increased over time, with the most rapid growth of statistical modeling. Electricity infrastructure restoration is modeled most frequently, followed by the restoration of multiple infrastructures, the interdependency of which increasingly is considered in recent literature. Researchers gather the data from various sources, including collaborations with utility companies, national databases, and postevent damage and restoration reports. This study discusses and provides recommendations for data usage practices to facilitate a community of practice for restoration modeling and to provide greater opportunities for future data sharing.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the National Science Foundation (NSF Grant No. CMMI-1824681).
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Information & Authors
Information
Published In
Natural Hazards Review
Volume 22 • Issue 4 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Published online: Aug 10, 2021
Published in print: Nov 1, 2021
Discussion open until: Jan 10, 2022
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