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Structural Engineering and Mechanics Volume 44, Number 2, October25 2012 , pages 179-196 DOI: https://doi.org/10.12989/sem.2012.44.2.179 |
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An eigenspace projection clustering method for structural damage detection |
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Jun-hua Zhu, Ling Yu and Li-li Yu
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| Abstract | ||
| An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs. | ||
| Key Words | ||
| structural damage detection; eigenspace projections; fuzzy clustering; principal component analysis; kernel principal component analysis | ||
| Address | ||
| Jun-hua Zhu: MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou, 510632, P. R. China; China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, 510610, P. R. China Ling Yu: MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou, 510632, P. R. China; Department of Mechanics and Civil Engineering, Jinan University, Guangzhou, 510632, P. R. China Li-li Yu: MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou, 510632, P. R. China; School of Materials Science and Engineering, Hebei University of Technology, Tianjin 300130, P. R. China | ||