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Floods are among the most expensive and destructive natural disasters, causing significant damage to civil infrastructures, including transportation networks, buildings, and other critical systems. While the impacts of flooding on civil infrastructure systems have been extensively studied, significant challenges remain in effectively assessing and mitigating flood impacts. This review synthesizes research findings from more than 300 publications to provide a comprehensive overview of the current state of knowledge on flood-related infrastructure vulnerabilities. The paper examines the types and causes of floods, key historical events, and mechanisms of damage to civil infrastructure systems caused by flooding. This paper reviews and qualitatively evaluates various flood risk and damage assessment methods, including hydrological and hydraulic modeling tools (e.g., the Hydrologic Engineering Center–River Analysis System, stormwater management model, and MIKE Flood), remote sensing, geographic information system-based approaches, and decision-making frameworks. These methods are evaluated in terms of spatial and temporal resolution, data requirements, ease of use, and applicability. This review also discusses the growing role of emerging technologies such as real-time monitoring systems and the Internet of things in improving flood preparedness and response. A range of mitigation strategies is introduced, from structural interventions to policy and planning approaches. Based on the review, future research directions are also highlighted to address current gaps, including (1) standardizing data collection and sharing; (2) real-time monitoring and flood response systems; (3) integration of AI and machine learning for damage assessment and response; and (4) quantifying infrastructure damage and categorizing flood events. This paper aims to contribute to the development of more resilient and adaptive systems capable of mitigating the impacts of flooding on civil infrastructures.
Abegaz et al. (Wed,) studied this question.