Project Title
Computer Vision Tools for Bridge Inspections and Reporting
Researcher(s)
Mostafa Tazarv, PhD, PE – PI
Kwanghee Won, PhD – Co-PI
Project Description
The Alaska Department of Transportation & Public Facilities (DOT&PF) is responsible for condition assessment of approximately 1000 bridges in the state. Each year, Alaska DOT&PF engineers inspect about 500 bridges. Per the Alaska Bridge Inspection Program, the inspector must complete both a National Bridge Inventory (NBI) inspection (following the FHWA Recording and Coding Guide) and an element level inspection (following the AASHTO Manual for Bridge Element Inspection, MBEI) for each bridge. Using either NBI or MBEI, a significant amount of data must be collected and reported. However, the data collection/reporting is usually done manually, which is time consuming, error prone, and sometimes not consistent when repeated. For example, the deck defect mapping requires manual detection and measurement of delaminated concrete, patch repairs, exposed reinforcing steel, and spalling. Computer vision, a type of image processing that incorporates artificial intelligence (AI) for analyzing the surroundings, can significantly expedite the process of damage/defect identification and measurement only using photographs of bridge deck and other elements. Furthermore, this and other AI tools can be utilized to expedite and unify reporting. The main goal of the present study is to develop practical AI tools that help inspectors with measurements and reporting of bridge defects following NBI and MBEI requirements. To achieve this goal, a few bridge elements (e.g., decks and girders) will be targeted for further investigation, inspection database including photographs of the selected elements with/without damage will be compiled, and computer vision tools will be developed for the selected elements to recognize the element defects, quantify the defect per NBI/MBEI, and produce a report following the DOT&PF standard practice. The tools, which can be standard software or web-based, will incorporate mobile devices for the ease of data collection, access, sharing, and reuse in future inspections.
Mostafa Tazarv, PhD – PI
Kwanghee Won, PhD – Co-PI