Machine Learning Enabled Information Fusion of Heterogeneous Sensing for Infrastructure Monitoring (1.22)

In this proposed research, the research team plans to develop a framework for machine learning enabled information fusion of heterogeneous sensing for infrastructure health monitoring. Traditionally, off-the-shelf sensors such as accelerometers and strain gages have been used to collect real-time measurements of structural responses to facilitate health monitoring. While certain level of successes have been achieved, they also exhibit limitations such as relatively low detection sensitivity to incipient damage. In recent years, significant progresses have been achieved on exploring new sensors and sensing technologies that achieve structural damage identification through active interrogation using such as piezoelectric transducers and magneto-mechanical transducers. These transducers feature high frequency bandwidth and can produce active interrogation responses in high frequency range with small wavelength. Although possessing high detection sensitivity, they may lead to false alarms due to noise and environment variations. Another challenge is that there always exists discrepancy between physical structure and the numerical model such as finite element model. Therefore any model-based inverse identification may be subject to error. The team proposes to (1) establish a benchmark testbed that incorporates various sensors to assess different sensing mechanisms; (2) develop a machine learning based fault detection and identification approach that can fuse together heterogeneous sensing information that can take full advantage of different sensors and avoid their respective shortcomings; and (3) investigate scalability strategies that can result in actual implementation of the new framework. Potential applications are large-scale infrastructure such as bridges.

• Record URL:
  https://tidc.umaine.edu/tidc_project/1-22-uconn/

Language

• English

Project

• Status: Active
• Funding: $80597
• Contract Numbers:

  69A3551847101

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Transportation Infrastructure Durability Center

  University of Maine
  Orono, ME  United States  04469

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Project Managers:

  Dunn, Denise

• Performing Organizations:

  Transportation Infrastructure Durability Center

  University of Maine
  Orono, ME  United States  04469

  University of Connecticut

  Sponsored Programs Services
  438 Whitney Road Ext 1133
  Storrs, CT  United States  06269-1133
• Principal Investigators:

  Tang, Jiong

• Start Date: 20231001
• Expected Completion Date: 20250630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Data fusion; Infrastructure; Machine learning; Monitoring; Sensors; Structural health monitoring
• Subject Areas: Bridges and other structures; Data and Information Technology; Maintenance and Preservation; Transportation (General);

Filing Info

• Accession Number: 01917498
• Record Type: Research project
• Source Agency: Transportation Infrastructure Durability Center
• Contract Numbers: 69A3551847101
• Files: UTC, RIP
• Created Date: May 1 2024 5:04PM