UTC 2022 Funding - Cycle 2 Research Projects

Project Number: CY2-OSU-OU-01
Project Title: Automated Damage Assessment of Bridges Using Machine-Learning-Assisted Structural Health Monitoring
Performing Institution: Oklahoma State University and the University of Oklahoma
Principal Investigators: Mohamed Soliman, Oklahoma State University; Royce Floyd, University of Oklahoma
Proposed Start and End Date: 10/01/2024 to 09/30/2025
Project Description: Improving the performance of transportation structures is essential to reduce the likelihood of catastrophic failures and their adverse economic, social, and environmental impacts. Efforts to improve the performance of bridges rely on the application of optimized interventions (e.g., inspections or monitoring actions) to detect and correct damage in a timely manner. In this context, Structural Health Monitoring (SHM) is becoming essential for effectively detecting damage and quickly localizing areas that require close-up investigation. However, traditional SHM activities may require significant effort in data analysis and processing. This precludes the wide adoption of SHM in bridge management activities. Accordingly, there is a need for computationally efficient and more practical SHM-based approaches that can detect and localize damage in real-time.

The main goal of this project is to develop an integrated framework for automated damage detection and localization in bridges based on Machine Learning (ML) algorithms. The proposed work involves laboratory testing on large-scale specimens subjected to random variable loading, as well as processing and analysis of long-term monitoring data from an existing highway bridge instrumented as part of a previous Oklahoma DOT sponsored project.
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