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Piece-wise Linear Approximation for Improved Detection in Structural Health Monitoring

Essegbey, John W.
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342729241

Abstract Details

2012, MS, University of Cincinnati, Engineering and Applied Science: Electrical Engineering.
Structural health monitoring mostly refers to damage identification based on some variations in a system’s behavior with most techniques assuming linearity. Over years of practice within a field, expert knowledge of such variations is acquired resulting in the development of some non-linear mental models of the systems. The traditional quantitative statistical model has been integrated with a new qualitative mental model of day and night in a dynamic bridge system’s strain response. In some divide and conquer fashion, the more linear component of the strain response is analyzed separately with more prediction accuracy and threshold sensitivity. The introduction of such intelligence into the unsupervised learning phase of the linear regression based data analysis algorithm for structural health monitoring has been demonstrated to increase the overall efficiency of the monitor. The increase in sensitivity over the night and attendant detection addresses in part the issue of most structural health monitors being blinded by environmental variations. Using field data from the three-span Ironton-Russell truss bridge, the system behavior is better characterized as having some piecewise linearity, a new paradigm that could contribute towards improving the adequacy of data driven baseline models for monitoring of civil infrastructure. The use of the data itself has also been demonstrated to aid in selecting parameters of concern, threshold setting and system analysis for decision making.
Arthur Helmicki, PhD (Committee Chair)
Victor Hunt, PhD (Committee Member)
Ali Minai, PhD (Committee Member)
153 p.
Electrical Engineering
structural health monitoring; piece-wise linear approximation; mental model; intelligent parameter varying; improved detection; exploratory data analysis;

Recommended Citations

Citations

  • Essegbey, J. W. (2012). Piece-wise Linear Approximation for Improved Detection in Structural Health Monitoring [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342729241

    APA Style (7th edition)

  • Essegbey, John. Piece-wise Linear Approximation for Improved Detection in Structural Health Monitoring. 2012. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342729241.

    MLA Style (8th edition)

  • Essegbey, John. "Piece-wise Linear Approximation for Improved Detection in Structural Health Monitoring." Master's thesis, University of Cincinnati, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342729241

    Chicago Manual of Style (17th edition)

ucin1342729241
572
© 2012, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.