Quant CR for Transformative Bridge Asset Management[UGA-2024-01] : This project aims to empower local and state bridge owners to make informed decisions on MRR, optimizing budget plans across various scenarios. Thus, the research approach centers on developing an AI-powered quantitative condition rating (QUANT CR) system and decision analysis tool for bridges, which leverages historical data, including the extensive records of the National Bridge Inventory (NBI)’s element data, Long-Term Bridge Performance Program, bridge MRR scenarios, inspectors’ narratives, and ultimately risk management including environmental, traffic, vehicle, and other relevant data to predict long-term bridge performance. A web-based Geographic Information System (GIS) platform will store and visually represent bridge network assets, providing uniform access to all databases. A hierarchical approach to quantify bridge condition ratings and assigning them to the network analysis tool will improve long-term bridge performance predictions. Deep learning algorithms will analyze historical data and detailed bridge condition narratives to improve the bridge performance predictions at element level. Incorporating the element-level inspection results and detailed defect items is a key task for building a Quant CR model. Text recognition techniques will further interpret inspectors’ descriptive reports, transforming qualitative comments into actionable quantitative data.
