Non-contact Intelligent Inspection of Infrastructure (1.15)

The objective of this research is to develop non-contact sensing mechanism for infrastructure monitoring as well as the associated machine-learning based technique for decision making. Currently available sensory systems for structural health monitoring are almost all based on transducers that are directly attached to or embedded in structures monitored. As a result, they face with critical barriers, such as extremely high implementation cost in very large scale structures and relatively high false alarm rate due to malfunction of sensors themselves. The non-contact nature of the proposed sensing modality will cause paradigm shift: it leads to mobile sensory system that can monitor very large scale structures employing only a small number of sensors, and it allows us to increase considerably the confidence level of structural health monitoring. In this research, concurrent breakthroughs in sensor synthesis and data analysis will be pursued. The project team will (a) develop a new non-contact impedance-based sensing mechanism via two-way magneto-mechanical dynamic interaction that is enhanced by adaptive electrical circuitry integration, which facilitates the tunable high-frequency interrogation to disclose structural anomaly; and (b) formulate accurate and robust decision making strategies that that take full advantage of the new machine learning techniques. Potential applications are large-scale infrastructure components such as railway tracks.

• Record URL:
  https://tidc.umaine.edu/tidc_project/1-15-uconn/

Language

• English

Project

• Status: Active
• Funding: $156846
• Contract Numbers:

  69A3551847101

• Sponsor 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

  University of Connecticut, Storrs

  Connecticut Transportation Institute
  270 Middle Turnpike, Unit 5202
  Storrs, CT  United States  06269-5202
• 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

  University of Connecticut, Storrs

  Connecticut Advanced Pavement Laboratory
  270 Middle Turnpike, Unit 5202
  Storrs, CT  United States  06269-5202
• Project Managers:

  Dunn, Denise

• Performing Organizations:

  Transportation Infrastructure Durability Center

  University of Maine
  Orono, ME  United States  04469

  University of Connecticut, Storrs

  Connecticut Advanced Pavement Laboratory
  270 Middle Turnpike, Unit 5202
  Storrs, CT  United States  06269-5202
• Principal Investigators:

  Tang, Jiong

• Start Date: 20210801
• Expected Completion Date: 20240930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Infrastructure; Intelligent agents; Machine learning; Railroad tracks; Sensors; Structural health monitoring
• Subject Areas: Data and Information Technology; Maintenance and Preservation; Safety and Human Factors; Terminals and Facilities; Transportation (General);

Filing Info

• Accession Number: 01780654
• Record Type: Research project
• Source Agency: Transportation Infrastructure Durability Center
• Contract Numbers: 69A3551847101
• Files: UTC, RIP
• Created Date: Aug 28 2021 6:09PM