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Sponsors:
Sponsors:
Alaska Department of Transportation & Public Facilities (DOT&PF), and Center for Transformative Infrastructure Preservation and Sustainability (CTIPS) – University Transportation Center (UTC)
Project Funds: $450,000 ($250,000 from DOT&PF, and $200,000 match from CTIPS)
Year: 2024-2026
Personnel:
Personnel:
PI: Mostafa Tazarv, PhD, PE
Co-PI: Kwanghee Won (Computer Science, SDSU) and Marco Ciarcia (Mechanical Engineering, CSU)
Graduate Research Assistants: Dongyoun Kim
Project Technical Panel: Ben Fetterhoff, Jesse Escamilla, David McAdoo, and Jared Jones
Project Summary:
Project Summary:
The Alaska Department of Transportation and Public Facilities (DOT&PF) is responsible for condition assessment of approximately 1,000 bridges in the state. Of which, 44% of the bridges are in good condition, 49% in fair condition, and the remaining 7% are in poor condition. When a new inspection indicates significant damage or when higher loads are expected for a bridge, a load rating is performed, which can be done through analysis or field testing. Despite several advantages of bridge load rating using field testing data, it is not a common practice for the evaluation of bridge performance. Instead, the analytical load rating is more common. One main reason is the cost of field operations and instrumentation. One effective way to reduce the inspection cost is to utilize new technologies such as drones. Furthermore, recent studies have confirmed the feasibility of bridge field testing using computer vision in which video recording from fixed cameras are used to extract bridge response with no need for conventional sensors. Digital image correlation (DIC) allows extracting full-field displacements and strains from images and videos.
The Alaska Department of Transportation and Public Facilities (DOT&PF) is responsible for condition assessment of approximately 1,000 bridges in the state. Of which, 44% of the bridges are in good condition, 49% in fair condition, and the remaining 7% are in poor condition. When a new inspection indicates significant damage or when higher loads are expected for a bridge, a load rating is performed, which can be done through analysis or field testing. Despite several advantages of bridge load rating using field testing data, it is not a common practice for the evaluation of bridge performance. Instead, the analytical load rating is more common. One main reason is the cost of field operations and instrumentation. One effective way to reduce the inspection cost is to utilize new technologies such as drones. Furthermore, recent studies have confirmed the feasibility of bridge field testing using computer vision in which video recording from fixed cameras are used to extract bridge response with no need for conventional sensors. Digital image correlation (DIC) allows extracting full-field displacements and strains from images and videos.
The main goal of this proposal is to develop frameworks and necessary tools to field test bridges using computer vision including either a ground DIC system and/or drone-DIC system. Instead of using conventional displacement/strain sensors and data acquisition equipment, a few cameras, or a fleet of drones each equipped with a camera system will be deployed to measure the bridge responses. To achieve the project goals, the most recent DIC technologies will be reviewed, current commercial and research products suitable for bridge response measurements will be evaluated, low-cost DIC tools and practical frameworks for bridge field testing will be developed, and a few bridges will be load tested using DIC and conventional sensors to validate and/or refine the tools. An expert panel will be formed by DOT&PF and will guide the research team for practical outcomes.
The main goal of this proposal is to develop frameworks and necessary tools to field test bridges using computer vision including either a ground DIC system and/or drone-DIC system. Instead of using conventional displacement/strain sensors and data acquisition equipment, a few cameras, or a fleet of drones each equipped with a camera system will be deployed to measure the bridge responses. To achieve the project goals, the most recent DIC technologies will be reviewed, current commercial and research products suitable for bridge response measurements will be evaluated, low-cost DIC tools and practical frameworks for bridge field testing will be developed, and a few bridges will be load tested using DIC and conventional sensors to validate and/or refine the tools. An expert panel will be formed by DOT&PF and will guide the research team for practical outcomes.
Project Work Plan:
Project Work Plan:
Task 1: Literature Review,
Task 1: Literature Review,
Task 2: Evaluation of Current DIC Technologies,
Task 2: Evaluation of Current DIC Technologies,
Task 3: Development of Computer Vision Displacement Measurement Tools,
Task 3: Development of Computer Vision Displacement Measurement Tools,
Task 4: Development of Bridge Field Testing Frameworks,
Task 4: Development of Bridge Field Testing Frameworks,
Task 5: Field Validation,
Task 5: Field Validation,
Task 6: Project Deliverables
Publications:
Publications:
Project in Progress
Presentations:
Presentations:
Project in Progress
Proposed Methodology
Proposed Methodology
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