Fu, BowenDueñas-Osorio, Leonardo2023-02-062023-02-062022-09-13Fu, Bowen and Dueñas-Osorio, Leonardo. "Resilience-informed infrastructure network dismantling." <i>13th International Conference on Structural Safety & Reliability (ICOSSAR 2021-2022),</i> (2022) https://doi.org/10.25611/EY0N-GR64.https://hdl.handle.net/1911/114308Large-scale networked infrastructure systems contribute significantly to modern society. Highly intra- and interconnnected systems enable communities to be more productive, at the expense of becoming more vulnerable to extreme events, cascading failures, and operational demands, including random failures and even targeted attacks. The resilience of infrastructure systems against common but random failure and rare but intentional attacks is critical for safe communities, as it covers multiple other types of contingencies in between. Network dismantling is a process to make the network dysfunctional by removi ng a fraction of components, which provides insights for robustness and resilience under many events, from common to rare. In particular, to protect networks from uncertain dismantling, we need to understand how to optimally fragment networks into small clusters by removing a fraction of their assets with minimal cost. Approximation methods are desirable because finding the optimal dismantling strategy is NP-hard, thus impractical on infrastructure networks. First attempts rely on iterative removal of the nodes with the highest adaptive importance, either from basic centralities, such as degree and betweeness, or from some more advanced metrics like collective influence. However, the additive nature of such methods fails to capture the synergistic nature of the dismantling problem. An algorithm connecting network dismantling problems with network decycling problems, identifies better the collective dismantling set. Other recent strategies add realism by adopting nonuniform node remo val costs, and applying a bisecting algorithm based on weighted spectral approximations iteratively. Despite these efforts, the combinatorial optimization nature of the network dismantling problem still requires global solutions, even if approximated. Additionally, the cost to remove components is the only factor considered in most previous methods. Network resilience, which can inform what to protect from dismantling to facilitate recovery, is seldom included as part of the cost. In this work, we propose a method employing Karger`s contraction algorithm and node-transferring heuristic optimization to approximate the optimal dismantling set, considering both component removal cost and network resilience after dismantling. The proposed method, resilDism, obtains good performance compared to state-of-the-art network dismantling methods, and provides valuable insights to guide network design and resilience enhancement in practice.engMade available under a Creative Commons Attribution LicenseConference paperNetwork dismantlingResilience assessmentInfrastructure systemshttps://doi.org/10.25611/EY0N-GR64