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<p>Regular bridge inspection and maintenance are essential for safe transportation networks, but traditional manual methods often suffer from subjectivity, leading to potential errors in decision-making. Advancements in technologies such as laser scanning, drones, Bridge Information Modeling (BrIM), and digital image processing have enabled more accurate and reliable bridge assessments. However, current research does not adequately address challenges faced during urban bridge inspections, such as inspecting all bridge elements, accounting for multiple deteriorations, and aligning with condition rating systems. This study develops an integrated framework for digitizing urban bridge inspections, combining unmanned aerial vehicles (UAVs), terrestrial laser scanning (TLS), and BrIM. The framework enhances traditional methods by synchronizing with established rating systems, specifically the National Bridge Inventory (NBI) and National Highway Authority (NHA). Validated on an in-service concrete bridge, the framework ensures accurate and standardized inspections. Comparisons of manual and digitization-based ratings show consistency with the NBI. The study recommends using site-specific algorithms for image processing and point cloud analysis to optimize digitization accuracy. Additionally, it suggests employing BrIM to create an inspection database within Revit and integrating digitization with numerical or predictive models to provide condition ratings without physical inspections, further improving efficiency and accuracy.</p>

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Technology integrated framework for condition digitization of urban RCC bridge

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Waleed Ahmed Khan,
  • Farrukh Arif

摘要

Regular bridge inspection and maintenance are essential for safe transportation networks, but traditional manual methods often suffer from subjectivity, leading to potential errors in decision-making. Advancements in technologies such as laser scanning, drones, Bridge Information Modeling (BrIM), and digital image processing have enabled more accurate and reliable bridge assessments. However, current research does not adequately address challenges faced during urban bridge inspections, such as inspecting all bridge elements, accounting for multiple deteriorations, and aligning with condition rating systems. This study develops an integrated framework for digitizing urban bridge inspections, combining unmanned aerial vehicles (UAVs), terrestrial laser scanning (TLS), and BrIM. The framework enhances traditional methods by synchronizing with established rating systems, specifically the National Bridge Inventory (NBI) and National Highway Authority (NHA). Validated on an in-service concrete bridge, the framework ensures accurate and standardized inspections. Comparisons of manual and digitization-based ratings show consistency with the NBI. The study recommends using site-specific algorithms for image processing and point cloud analysis to optimize digitization accuracy. Additionally, it suggests employing BrIM to create an inspection database within Revit and integrating digitization with numerical or predictive models to provide condition ratings without physical inspections, further improving efficiency and accuracy.