Addressing the Issue of Insufficient Information in Data-Based Bridge Health Monitoring

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.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; References; Tables;
  • Pagination: 84p

Subject/Index Terms

Filing Info

  • Accession Number: 01626657
  • Record Type: Publication
  • Report/Paper Numbers: CAIT-UTC-042
  • Contract Numbers: DTRT12-G-UTC16
  • Created Date: Feb 27 2017 9:25AM