Postdisaster Decision Framework for Bridge Repair Prioritization to Improve Road Network Resilience

Abstract

Road networks are critical to a community’s ability to recover from a disaster. The ability to move goods and people efficiently is dramatically affected by disruptions to vulnerable components of the network, especially bridges. Widespread damage to bridges after a natural hazard and limited resources available for repair warrant a need to have an efficient framework to restore the network to the predisaster performance level quickly. Previous studies on postdisaster resilience tend to characterize the recovery based on only one or two performance metrics. This study proposes a decision framework to prioritize bridge repair after a disruptive event using a network performance metric developed using three categories of network performance measures: (1) functional measures defined as the change in total travel distance and total travel time, (2) a topological measure that considers the importance of a bridge to network connectivity modeled with reference to the number of shortest paths passing through each bridge, and (3) a social measure defined with reference to access to healthcare facilities and measured by the change in travel time to an emergency facility. The performance metric is then used to determine an optimal bridge repair sequence that maximizes the network performance during the recovery period. The framework is demonstrated using the road network of Mobile, Alabama, assuming four bridges crossing the Dog River are damaged by a natural hazard. The results of the case study show that the proposed framework is effective in guiding the prioritization of bridge repair after a disaster.

Conflict Resilience Framework for Critical Infrastructure Peacebuilding

“…The decision-making for recovery prioritisation depends on a number of attributes which differ significantly from the ones relating to natural disasters (Zamanifar & Hartmann, 2021). For example, for transportation infrastructure, post-disaster recovery commonly uses topological, social, and functional attributes (Merschman et al, 2020) as well as the criticality of the asset Fig. 2.…”

“…In what follows, the steps of the proposed resilience framework are described in detail, including the evolution of network performance for which the process described in Merschman et al (2020) was used (see Appendix A). The method is extended in this paper because limited resources (funds, materials, workforce) will be available post-war.…”

“…NP is calculated based on C1, C2 and C3 considering variable weighting factors (γ 1 , γ 2 , γ 3 ) ranging from 1 to 5. These values are reasonable based on literature (Merschman et al, 2020) and were used to examine the sensitivity of NP to topological and functional parameters, which take a minimum value of 1.0 (small influence) to 5.0 (dominant parameter). In post-disaster decisions for bridge repair, these weights reflect different priorities of the operators and decision-makers during the reinstatement of the network's operability, i.e.…”

Agent-based model on resilience-oriented rapid responses of road networks under seismic hazard

“…A multi agent-based model is proposed in this study, as an adaptive and inclusive modelling strategy, with regard to the interacting post-shock rapid response and the regular repair of RNs (Sun et al 2019b), to investigate whether such a time frame is realistic, when applied to a realcase scenario, given the current data on repair and replacement delivery. Previous studies have looked into the prioritization of bridge repair on the basis of various criteria (Merschman et al 2020), nonetheless, the working assumption being that the repair phase occurs by considering one bridge at a time. In the present study, three different Repair Unit Agents have been considered to model the restoration of bridges, as discussed in the previous section.…”

Seismic Vulnerability Assessment Methodologies for Roadway Assets and Networks: A State-of-the-Art Review

“…Kilanitis and Sextos [108] described the road network configuration in groups of nodes (intersections and other points of interest), links (road segments), and key component (bridges). Nevertheless, Merschman et al [109] considered intersections, endpoints, and zones as the main nodes for the road network. In addition, the study of a system of assets (e.g., roadway on embankment and a retaining wall in one of its sides) or grade separation of roads (planarity conditions) requires more advanced network-based analysis [110][111][112][113].…”