Detection and Monitoring of Material Aging and Structural Deterioration using Electromagnetic and Mechanical Sensors with Virtual Reality and Machine Learning Modeling (3.19)
The problem we are trying to solve is the detection and monitoring of aging civil infrastructure components and systems in New England by using visual information and subsurface images in a virtual reality (VR) environment for data visualization and machine learning (ML) for data interpretation. Material aging and structural deterioration of selected candidate structures (e.g., highway bridges) will be frequently (from twice a day to once a week) inspected to develop large amount of sensor data for condition assessment using machine learning. The problem is important because, with frequent inspection of civil infrastructure systems, processing and visualization of large amount of multiple-format sensor data have become a challenging task at the system’s level for bridge engineers. Registration of 2D photographs and sensor images in a 3D environment aided with VR equipment (e.g., headsets, handles, controllers) can help bridge engineers to better register inspection and monitoring data in multiple formats (e.g., photographs, images, texts, sketches) to actual structures. Frequent inspection of structures can produce large amount of data required by machine learning, as well as capturing the weekly, monthly, and seasonal changes of background/baseline information. In this base funded project, we propose to 1) collect electromagnetic (EM) (e.g., optical, radar, and laser sensors) and mechanical (e.g., impact-echo, ultrasonic testing, pulse tomography sensors) sensor data on a frequent basis (from twice a day to once a week) to develop large amount of data for machine learning interpretation, 2) study the effect of material aging on structural deterioration of highway bridges, and 3) develop a VR platform for rendering sensor data (including bridge rating and inspection reports) of inspected bridges in a 3D environment for the convenient interpretation of sensor data.
- Record URL:
Language
- English
Project
- Status: Active
- Funding: $423634
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Contract Numbers:
69A3551847101
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590Massachusetts Department of Transportation
10 Park Plaza
Boston, MA United States 02116University of Massachusetts Lowell
One University Drive
Lowell, MA United States 01854 -
Managing Organizations:
Transportation Infrastructure Durability Center
University of Maine
Orono, ME United States 04469Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590Massachusetts Department of Transportation
10 Park Plaza
Boston, MA United States 02116 -
Project Managers:
Dunn, Denise
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Performing Organizations:
Transportation Infrastructure Durability Center
University of Maine
Orono, ME United States 04469University of Massachusetts Lowell
One University Drive
Lowell, MA United States 01854 -
Principal Investigators:
Yu, Tzuyang
Wei, Jianqiang
Anaraki, Farhad
- Start Date: 20220601
- Expected Completion Date: 20250331
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Aging (Materials); Data fusion; Detection and identification technologies; Deterioration; Infrastructure; Machine learning; Monitoring; Structural health monitoring; Virtual reality
- Geographic Terms: New England
- Subject Areas: Data and Information Technology; Maintenance and Preservation; Materials; Transportation (General);
Filing Info
- Accession Number: 01851445
- Record Type: Research project
- Source Agency: Transportation Infrastructure Durability Center
- Contract Numbers: 69A3551847101
- Files: UTC, RIP
- Created Date: Jul 15 2022 3:24PM