  Disaster recovery is widely regarded as the least understood phase of the
disaster cycle. In particular, the literature around lifeline infrastructure
restoration modeling frequently mentions the lack of empirical quantitative
data available. Despite limitations, there is a growing body of research on
modeling lifeline infrastructure restoration, often developed using empirical
quantitative data. This study reviews this body of literature and identifies
the data collection and usage patterns present across modeling approaches to
inform future efforts using empirical quantitative data. We classify 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
most frequently modeled, followed by the restoration of multiple
infrastructures, water infrastructure, and transportation infrastructure.
Interdependency between multiple infrastructures is increasingly considered in
recent literature. Researchers gather the data from a variety of sources,
including collaborations with utility companies, national databases, and
post-event damage and restoration reports. This study provides discussion and
recommendations around data usage practices within the lifeline restoration
modeling field. Following the recommendations would facilitate the development
of a community of practice around restoration modeling and provide greater
opportunities for future data sharing.
