Advanced signal processing for structural identification: experimental studies

Jian Zhang; Tadanobu Sato; Tara C. Hutchinson

doi:10.1117/12.715109

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11 April 2007 Advanced signal processing for structural identification: experimental studies

Jian Zhang; Tadanobu Sato; Tara C. Hutchinson

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Proceedings Volume 6532, Health Monitoring of Structural and Biological Systems 2007; 65320S (2007) https://doi.org/10.1117/12.715109
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2007, San Diego, California, United States

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.

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Jian Zhang, Tadanobu Sato, and Tara C. Hutchinson "Advanced signal processing for structural identification: experimental studies", Proc. SPIE 6532, Health Monitoring of Structural and Biological Systems 2007, 65320S (11 April 2007); https://doi.org/10.1117/12.715109

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