Addressing the issue of insufficient information in data-based bridge health monitoring : final report.

Details

• Creators:
  Betti, Raimondo
• Corporate Creators:
  Columbia University. Department of Civil Engineering and Engineering Mechanics
• Corporate Contributors:
  Rutgers University. Center for Advanced Infrastructure and Transportation ; United States. Department of Transportation. Research and Innovative Technology Administration ; United States. Federal Highway Administration ; New York (State). Dept. of Transportation
• Subject/TRT Terms:
  • Structural health monitoring
  • Inspection
  • Pavement distress
  • Pavement performance
  • Bridges
  • Materials and structures protection
  • Data analysis
  • Structural health monitoring
  • bridges
  • damage detection
  More +
• Publication/ Report Number:
  CAIT-UTC-042
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Rutgers University. Center for Advanced Infrastructure and Transportation
• Abstract:
  One of the most efficient ways to solve the damage detection problem using the statistical pattern recognition ; approach is that of exploiting the methods of outlier analysis. Cast within the pattern recognition framework, ; damage detection assesses whether the patterns of the damage sensitive features extracted from the response of ; the system under unknown conditions depart from those drawn by the features extracted from the response of ; the system in a healthy state. The metric dominantly used to measure the testing feature’s departure from the ; trained model is the Mahalanobis Squared Distance (MSD). Evaluation of MSD requires the use of the inverse of ; the training population’s covariance matrix. It is known that when the feature dimensions are comparable to the ; number of observations, the covariance matrix is ill-conditioned and numerically problematic to invert. When ; the number of observations is smaller than the feature dimensions, the covariance matrix is not even invertible. ; In this work, four alternatives to the canonical damage detection procedure were investigated to address the ; issue: data compression through Discrete Cosine Transform, use of pseudo-inverse of the covariance matrix, use of ; shrinkage estimate of the covariance matrix, and a combination of the three techniques. The performance of the ; four methods was first studied for solving the damage identification problem on simulated data from a four DOFs ; shear-type system, and on experimental data recorded on a four story steel frame excited at the base by means ; of the shaking table facility available at the Carleton Laboratory at Columbia University. Finally, the proposed ; techniques were also investigated in the context of damage location applications on simulated data from a bridge ; deck model.
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• Format:
  PDF
• Funding:
  DTRT12-G-UTC16
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31797/dot_31797_DS1.pdf
• File Type:
  [PDF - 826.90 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:d9a68de3ea8ffeea39e321072aad80dc6d5953d610ef85ebb927cf70d13153a2