Machine Learning Enabled Information Fusion of Heterogeneous Sensing for Infrastructure

Details

• Creators:
  Tang, Jiong ; Zhang, Yang ; Wang, Ting ; Zhou, Qianyu
• Corporate Creators:
  University of Maine. Transportation Infrastructure Durability Center (TIDC) ; University of Connecticut
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Structural strengthening
  • Sensors
  • Detection and identification technologies
  • Optimization
  • Machine learning
  • Artificial intelligence
  • Infrastructure
  • Structural damage identification
  • inverse analysis
  • sensor network optimization
  More +
• Publication/ Report Number:
  69A3551847101
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Abstract:
  In this research, we develop a framework for machine learning enabled information fusion 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 levels of successes have been achieved, they also exhibit limitations such as relatively low detection sensitivity to incipient damage and especially limited detection range and coverage. In this project, we 1) establish a benchmark testbed that incorporates various sensors to assess different sensing mechanisms; 2) develop a machine learning based approach that can leverage sensing information and extrapolate to full-field measurement; and 3) investigate scalability strategies that can result in actual implementation of the new framework. Potential applications are large-scale infrastructure such as bridges.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88739/dot_88739_DS1.pdf
• File Type:
  [PDF - 27.04 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:9ebce12756bd1170dd2956282038955da722a4a6aa981daf7a97561cf5532d562610cde9dd7238bc5cba020d72b51e50710543db19a7d975a9de5b65225963e7