Abstract Bridges are crucial civil infrastructure, but their deterioration over time poses significant safety risks. Traditional human visual inspections are limited in accuracy and efficiency, leading to challenges in maintaining the inventory of bridges in the United States, particularly in economically disadvantaged communities. Leveraging recent advancements in computer vision (CV), artificial intelligence (AI), and augmented reality (AR), the team proposes a novel human-centered approach to enhance the accuracy and efficiency of concrete bridge inspections and promote equity in infrastructure maintenance. By automating detection and documentation of damage in concrete bridges, and empowering human inspectors by overlaying real-time detection results onto bridges thereby enabling human-machine collaboration, the project aims to improve inspection effectiveness and efficiency, promote equity in infrastructure maintenance, and enhance public safety.
Description Bridges are crucial civil infrastructure, but their deterioration over time poses significant safety risks. Traditional human visual inspections are limited in accuracy and efficiency, leading to challenges in maintaining the inventory of bridges in the United States, particularly in economically disadvantaged communities. Leveraging recent advancements in computer vision (CV), artificial intelligence (AI), and augmented reality (AR), the team proposes a novel human-centered approach to enhance the accuracy and efficiency of concrete bridge inspections and promote equity in infrastructure maintenance. By automating detection and documentation of damage in concrete bridges, and empowering human inspectors by overlaying real-time detection results onto bridges thereby enabling human-machine collaboration, the project aims to improve inspection effectiveness and efficiency, promote equity in infrastructure maintenance, and enhance public safety.
Objective This project primarily addresses the USDOT Strategic Goal of Safety and Equity.
Impacts/Benefits The project is expected to produce an advanced inspection tool that leverages computer vision, artificial intelligence, and augmented reality to revolutionize concrete bridge inspection. This tool will autonomously detect concrete bridge damages and overlays the detection results onto physical bridges through holographic imagery, enabling a human-centered approach to inspection, tracking, and documentation. By harnessing these technologies, the tool promises to significantly enhance the accuracy and efficiency of bridge inspection processes, thereby fostering greater equity in maintenance efforts, particularly for disadvantaged communities. Furthermore, the project will yield valuable Insights into the usability, effectiveness, and impact of the developed tool on real-world bridge inspections, taking into account factors such as prior knowledge, skills, and comfort with technology.