Bridge Monitoring Through a Hybrid Approach Leveraging a Modal Updating Technique and an Artificial Intelligence (AI) Method [Research Brief]

Details

• Creators:
  Cho, Chunhee ; Moon, DoSoo
• Corporate Creators:
  Pacific Southwest Region 9 UTC, University of Southern California ; METRANS Transportation Center (Calif.) ; University of Hawaii at Manoa
• 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:
  • Bridges
  • Detection and identification system applications
  • Machine learning
  • Predictive models
  • Structural deterioration and defects
  • Structural health monitoring
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Edition:
  Research Brief
• Corporate Publisher:
  METRANS Transportation Center (Calif.)
• Abstract:
  The objective of this project is to develop a post processing damage detecting algorithm by leveraging a modal updating technique and an artificial intelligence (AI) method. Problem Statement: The proposed project integrated two methods (i.e., a model updating technique and a data-driven prediction method) that can compensate for each other’s the weakness that otherwise imposed difficulty in precise real-time application of health monitoring systems. This project leveraged a mode updating technique with high-fidelity experimental data to obtain an accurate digital model that represents an actual bridge. The drawback of the model updating technique (i.e., high computational time) can be overcome by applying a data-drive method that is known to be computationally efficient. The proposed approach will then result in a fast and accurate method (i.e., a model-based data-driven method) for early damage identification of bridge structures.
• Format:
  PDF
• Funding:
  69A3551747109
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/67552/dot_67552_DS1.pdf
• File Type:
  [PDF - 227.21 KB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:674bba159ef1c0bef2bc15594884e240f27aebc735118ef6defe56285f0223d2b3fd352d4b7693529476d852b99a9aac873a83af23c6c3bd4f3c9caff8a42347