{'fraction_non_alphanumeric': 0.01693889897156685, 'fraction_numerical': 0.0, 'mean_word_length': 5.891666666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Two-Stage Model for Optimized Mitigation and Recovery of Bridge Network with Final Goal of Resilience

Ensuring resilience of critical infrastructure systems when facing disruptions is of great importance to engineers, stakeholders, and decision makers. Providing an optimal strategy for strengthening infrastructure system performance before disruption and rapidly recovering systems after disruption are two visible approaches to enhance system resilience efficiently. However, because of the complexity of the interrelationship among system infrastructures and the budgetary limitation, there is an imperative requirement for a rigorous decision-making process to track the costs induced by any enhancement to the system. To address this issue, in this paper, a multiobjective and two-stage stochastic programming model was developed for minimizing network-level cost and mean risk by considering both pre- and post-event maintenance actions. To account for the effects of different improvement strategies on network resilience, this model was tested under various disruption scenarios that highlighted the hazard uncertainty by combining a variety of occurrence probabilities. In this model, pre-event activities represent bridge retrofit that could contribute to increasing robustness and redundancy of the network system, whereas post-event activities are bridge repair and recovery on the basis of the resilience-enhancing effects advanced by the pre-event actions. The consequential optimization is the optimal social-economic outcome that considers different construction and disruption scenarios, and indirect costs associated with the system.