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Intelligent Asset Management for Improved Mobility: Technology Transfer for South Carolina
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2024-07-01
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[PDF-1.45 MB]
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Edition:Final Report (October 2023-September 2024)
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Abstract:This report details the implementation of digital twin technology for bridge load rating in South Carolina, focusing on developing a graphical user interface (GUI). Traditional bridge load rating methods are highlighted for their high costs, time consumption, and traffic disruptions. To address these issues, the research team introduced a digital twin approach utilizing drones, fiber optic strain gauges, and acoustic emission sensors to gather data on crack evolution and inherent strain during loading. This data feeds into a high-fidelity finite element model (FEM), forming a digital twin of the bridge. The core of the report is the GUI, which integrates vehicle load assessment, FEM response calculation, model validation, and parameter updating. Key steps include using a machine learning algorithm to assess vehicle load from acoustic emission data, applying this load to the FEM to obtain mechanical responses, and updating the FEM and bridge load rating formula's condition factor based on field monitoring and drone inspection results. Additionally, the report summarizes a workshop on applying digital twin technology in bridge load rating and maintenance, with integration into intelligent asset management platforms like IBM Maximo. The study demonstrates the significant potential of this technology to enhance bridge safety, extend infrastructure lifespan, and reduce maintenance costs through the efficient and user-friendly GUI.
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