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This paper presents a framework to evaluate the regional and local resilience of infrastructure networks following disruptions from natural hazards. Herein, the regional resilience of a network relates to the accessibility of a community within a larger network, whereas the local resilience concerns the ability of a network to provide its intended service within the boundaries of a community. Using this framework, a methodology is developed to demonstrate its application to a road and highway transportation network disrupted by ground shaking and inundation under a Cascadia Subduction Zone earthquake and tsunami scenario. The regional network extents encompass the entire coast of the US state of Oregon. Embedded within this regional network are 18 local networks associated with coastal communities. Regional and local connectivity indexes are defined to identify the initial damage and then track the postdisaster recovery of the transportation network, i.e., evaluate the network resilience. The study results identify the attributes that lead to a regionally or locally resilient network and highlight the importance of considering local infrastructure networks embedded within larger regional networks. It is shown that without regional considerations, the time to recover may be severely underpredicted. The methodology is further used as a decision support tool to demonstrate how mitigation options impact the transportation network’s resilience. The importance of strategically considering mitigation options is emphasized as some communities see significant reductions in time to recover, whereas others see little to no improvement.
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A stochastic programming approach to enhance the resilience of infrastructure under weather‐related risk
ABSTRACT The presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course.
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- Award ID(s):
- 1751844
- PAR ID:
- 10395224
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Computer-Aided Civil and Infrastructure Engineering
- Volume:
- 38
- Issue:
- 4
- ISSN:
- 1093-9687
- Page Range / eLocation ID:
- p. 411-432
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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