Damage detection with small data set using energy-based nonlinear features
Name
Damage detection with small data set using energy-based nonlinear features.pdf
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2.53 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Mohammadi Ghazi Mahalleh, Reza
Buyukozturk, Oral
Date Issued
January 2016
Journal
Structural Control and Health Monitoring
Publisher
Wiley Blackwell
Citation
Mohammadi Ghazi, Reza, and Büyüköztürk, Oral. “Damage Detection with Small Data Set Using Energy-Based Nonlinear Features.” Structural Control and Health Monitoring 23, 2 (July 2015): 333–348 © 2015 John Wiley & Sons
Version
Original manuscript
Abstract
This study proposes a new algorithm for damage detection in structures. The algorithm employs an energy-based method to capture linear and nonlinear effects of damage on structural response. For more accurate detection, the proposed algorithm combines multiple damage sensitive features through a distance-based method by using Mahalanobis distance. Hypothesis testing is employed as the statistical data analysis technique for uncertainty quantification associated with damage detection. Both the distance-based and the data analysis methods have been chosen to deal with small size data sets. Finally, the efficacy and robustness of the algorithm are experimentally validated by testing a steel laboratory prototype, and the results show that the proposed method can effectively detect and localize the defects.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/stc.1774
