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Scholars Journal of Engineering and Technology | Volume-14 | Issue-04
Flood-Resilient Urban Infrastructure Planning Through Combined Hydrologic Modeling, Structural Assessment, and Constructability Analysis
Nahida Sultana, Sonjoy Paul Avi, Mohammad Imran Khan, Abdullah Al Abid
Published: April 11, 2026 | 9 5
Pages: 161-171
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Abstract
Urban flooding has been recognized as a major issue for cities, which is being exacerbated by the consequences of rapid urbanization, expansion of impervious areas, and the increase in rainfall intensity, which is being influenced by climate variability. In the conventional approach to flood management studies, either hydrologic modeling or infrastructure assessment is typically conducted, which does not necessarily provide an effective approach to urban infrastructure planning under flood conditions. In this study, a framework for flood resilient urban infrastructure planning is proposed, which integrates hydrologic flood modeling, infrastructure assessment, and constructability assessment based on a multi-criteria decision approach. Hydrologic modeling is conducted to assess the rainfall-runoff behavior and to identify flood-prone areas based on flood depth and inundation maps. On the other hand, structural assessment focuses on the assessment of the reliability of structural elements such as bridges, embankments, culverts, and drainage channels subjected to hydraulic loading. The constructability assessment focuses on the assessment of the feasibility of the proposed mitigation measures based on the complexity of the construction, spatial constraints, and construction costs. The integrated decision analysis compares the flood mitigation measures based on integrated performance measures. The results show that the expansion of the drainage network and the hydraulic structures are viable for flood risk mitigation with favorable construction conditions in dense urban areas. The proposed method provides a framework for supporting the development of resilient urban infrastructures in flood-prone regions.