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Proceedings Paper

Extraction of a series of novel damage sensitive features derived from the continuous wavelet transform of input and output acceleration measurements
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Paper Abstract

This paper proposes a series of novel Damage Sensitive Features for earthquake damage estimation. The features take into account input (ground motion) and output acceleration (structure response) measurements. The Continuous Wavelet Transform is applied to both acceleration signals in order to obtain both time domain and frequency domain resolution. An algorithm that has been proposed for Maximum Entropy Deconvolution is applied to the Continuous Wavelet Transforms in order to obtain a matrix that relates the output wavelet coefficients to the input ones. The Damage Sensitive Features are then derived through statistical processing of the resulting matrix. This algorithm has been applied on data acquired from shake table tests where the structures were subjected to progressive damage. The proposed features are compared to response quantities that are indicative of damage (such as the hysteretic energy dissipated) and show high correlation with the extent of damage. The data utilized has not been pre-processed, illustrating the robustness of the algorithm against sensor noise. The proposed algorithm has several advantages: Minimal input and knowledge of the structure is required. More information on the structure's state is extracted through use of both the input and output signals than when only output signal is considered. Only two acceleration measurements are required to obtain a damage forecast utilizing primarily the strong motion recordings, resulting in easier sensor deployment. The use of strong motion recordings allows for information delivery immediately after an earthquake without additional data collection.

Paper Details

Date Published: 8 March 2014
PDF: 9 pages
Proc. SPIE 9061, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014, 90611L (8 March 2014); doi: 10.1117/12.2045861
Show Author Affiliations
Konstantinos Balafas, Stanford Univ. (United States)
Anne S. Kiremidjian, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 9061:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014
Jerome P. Lynch; Kon-Well Wang; Hoon Sohn, Editor(s)

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