Project-Level Optimization of Repair Activities for the Recovery of Transportation Assets after Inland Flood Events

Abstract

Postdisaster reconstruction poses a major challenge as it requires a trade-off between rapid restoration and a more prolonged, cost-conscious approach. Quick restoration reduces community disruptions but comes at a higher cost due to expedited resource acquisition, increased labor, and equipment mobilization. Conversely, a slower, budget-conscious approach, while saving on immediate costs, might amplify indirect socioeconomic losses due to prolonged disruptions such as driver delays, reduced accessibility to critical facilities, supply chain interruptions, or impacts on vulnerable populations. Attempts to optimize between these approaches are further complicated by a lack of data on the response to different damages. Additionally, the ambiguity of time and costs for restoration depends on available resources, severity of events and associated damages, and accessibility. This paper addresses these gaps by proposing a framework to optimize restoration schedules for various damage scenarios involving different types of damage to roads or bridges and by collecting data on tasks, costs, and times required to restore different damages. The optimization uses a simulation-based approach, generating multiple schedule solutions for each scenario using probabilistic distributions of tasks and their precedence relations to find the optimal schedule. Data on damages and associated costs and times are based on 15 years of data from Iowa, United States, which were reported as part of the detailed damage inspection reports submitted to receive emergency relief funds by Iowa Department of Transportation. It covers 10 bridge and six road damage scenarios. By evaluating these scenarios, the framework provides a realistic understanding of the required steps to restore each damaged bridge and a holistic view of possible restoration strategies, allowing decision-makers to select the best course of action to minimize duration, cost, or balance both. The practical applications of this study’s framework are highly relevant for improving postflood restoration efforts in transportation systems. By optimizing resource allocation based on specific damage scenarios and regional traffic volumes, this model can guide transportation authorities in prioritizing tasks to reduce direct and indirect costs. In high-traffic areas, where delays cause significant economic disruption, the framework helps decision-makers speed up restoration efforts, even if it means higher initial costs. In contrast, in low-traffic regions, the focus shifts to more cost-effective strategies that might extend the timeline but save on direct expenses. This model can be applied by state departments of transportation and local governments to streamline postdisaster recovery, minimizing the economic impact of road and bridge closures. By tailoring restoration strategies to traffic patterns and damage severity, the framework ensures more efficient use of labor and equipment, ultimately leading to faster recovery and lower overall costs. Moreover, it can be adapted for other types of disasters and different geographical regions, making it a versatile tool for improving infrastructure resilience.