Comparison study of feature extraction methods in structural damage pattern recognition

Wenjia Liu; Bo Chen; R. Andrew Swartz

doi:10.1117/12.880570

13 April 2011 Comparison study of feature extraction methods in structural damage pattern recognition

Wenjia Liu, Bo Chen, R. Andrew Swartz

Author Affiliations +

Proceedings Volume 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011; 798106 (2011) https://doi.org/10.1117/12.880570
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2011, San Diego, California, United States

Abstract

This paper compares the performance of various feature extraction methods applied to structural sensor measurements acquired in-situ, from a decommissioned bridge under realistic damage scenarios. Three feature extraction methods are applied to sensor data to generate feature vectors for normal and damaged structure data patterns. The investigated feature extraction methods include identification of both time domain methods as well as frequency domain methods. The evaluation of the feature extraction methods is performed by examining distance values among different patterns, distance values among feature vectors in the same pattern, and pattern recognition success rate. The test data used in the comparison study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case data sets, including undamaged cases and pier settlement cases (different depths), are used to test the separation of feature vectors among different patterns and the pattern recognition success rate for different feature extraction methods is reported.

Citation Download Citation

Wenjia Liu, Bo Chen, and R. Andrew Swartz "Comparison study of feature extraction methods in structural damage pattern recognition", Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 798106 (13 April 2011); https://doi.org/10.1117/12.880570

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