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Resilience-oriented decision making on the pre-shock intervention to road networks
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Date
2026-03-01
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Journal Article
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Abstract
Pre-shock intervention (PSI) has emerged as a proactive and promising approach to enhancing the resilience of critical infrastructure systems (CISs) against natural disasters. However, optimizing the decision-making on the PSI of real-world CISs is often challenging due to the inherent uncertainty of disaster events, as well as the fragility and recoverability of the physical systems. To address this challenge, this research introduces a search-based decision-making (SBDM) framework of selecting and scheduling PSI as a resilience amelioration tool of CISs, with a specific emphasis on road networks (RNs) subjected to damaging earthquakes. Accordingly, seismic performance enhancement of bridge structures has been adopted as a viable PSI approach, aiming to reduce their fragility while also facilitating the emergency restoration (ER) of RNs in the immediate aftermath of earthquakes. To quantify the impact of different selected and scheduled PSI lists on seismic resilience of RNs, an agent-based model (ABM) is developed to track the PSI-driven ERs and integrated into the SBDM framework. In this ABM, each recovery team involved in the ER campaign is modelled as an agent with behaviour determined by predefined attributes. Given a particular PSI list, the initial damage status of the RN under any given earthquake scenario is obtained, while the subsequent post-earthquake ER trajectory is also shaped by the ABM. Hence, the total loss of the whole RN is assessed. An optimal PSI list is thereafter identified by the SBDM framework as the one that best enhances seismic resilience of the RN. To examine its applicability, this SBDM framework is employed to guide the PSI of a real-world RN. Case study results demonstrate that the PSI list derived from the SBDM framework consistently outperform those derived from the intuition-driven, baseline strategies across diverse earthquake scenarios, underscoring its robustness.
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70