| description abstract | Rapid and efficient infrastructure restoration is critical to reducing the impacts of extreme events on community lifelines. Following a large-scale extreme event, infrastructure restoration at various stages is carried out simultaneously by agencies at various government levels and jurisdictions. Since each agency has different roles, responsibilities, and boundaries within which it operates, coordination and communication among them are challenging. With the overall goal of providing a common operating picture and facilitating concerted planning and action among emergency response agencies, this research proposes a data-driven and equity-centered framework that links the various stages—damage identification, restoration scheduling, and monitoring and control—of infrastructure restoration. This study takes a particular focus on the highway restoration caused by flood inundation. In detail, the framework is composed of three parts, including (1) a systematic data-driven approach that quickly provides spatially distributed estimates of highway inundation, (2) an equity-centered restoration scheduling strategy that prioritizes restoration tasks based on community social vulnerability, and (3) a Bayesian-based approach that provides an up-to-date indication of the impacts of component level changes on the overall restoration progress. A case study on highway inundation in Harris County during Hurricane Harvey was conducted to demonstrate the feasibility and applicability of the proposed framework. In the case study, multisource data, including physical highway topology, geospatial information, field inspection results, and socioeconomic and demographic data, were used. Our framework generates outputs that can be used for rapid damage identification, automated restoration scheduling, and real-time progress updating. In practice, these outputs facilitate quick and shared situational awareness among the involved agencies, which is expected to ease communication and coordination and help overcome challenges resulting from parallel and fragmented restoration efforts. To the authors’ best knowledge, this is the first framework that aims to support the management of infrastructure restoration by synthesizing various restoration stages. | |
