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Proceedings Paper

Advanced signal processing for structural identification: experimental studies
Author(s): Jian Zhang; Tadanobu Sato; Tara C. Hutchinson
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Paper Abstract

The aim of this study is to use observed data from a shaking table test to verify experimentally an SVR-based (support vector regression) structural identification approach. The method has been developed in previous work and showed excellent performance for large-scale structural health monitoring in numerical simulations. SVR is a promising data processing method employing a novel &egr;-insensitive loss function and the 'Max-Margin' idea. Thus the SVR-based approach identifies structural parameters accurately and robustly. In this method, a sub-structure technique is used thus the SVR-based analysis is reduced in dimension. Experimental validation is necessary to verify the method's capability to identify structural status from real data. For this purpose, a five-floor shear-building shaking table test has been conducted and two cases, input excitations to the shaking table of the Kobe (NS 1995) earthquake and a Sine wave with constant frequency and amplitude are investigated.

Paper Details

Date Published: 11 April 2007
PDF: 9 pages
Proc. SPIE 6532, Health Monitoring of Structural and Biological Systems 2007, 65320S (11 April 2007); doi: 10.1117/12.715109
Show Author Affiliations
Jian Zhang, Kyoto Univ. (Japan)
Univ. of California, San Diego (United States)
Tadanobu Sato, Kobe Gakuin Univ. (Japan)
Tara C. Hutchinson, Univ. of California, San Diego (United States)

Published in SPIE Proceedings Vol. 6532:
Health Monitoring of Structural and Biological Systems 2007
Tribikram Kundu, Editor(s)

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