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Sensors 2016, 16(4), 447; doi:10.3390/s16040447

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

1
Anhui and Huaihe River Institute of Hydraulic Research, No. 771 Zhihuai Road, Bengbo 233000, China
2
Machine Learning Lab, GE Global Research Center, Niskayuna, NY 12309, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 15 February 2016 / Revised: 21 March 2016 / Accepted: 23 March 2016 / Published: 26 March 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2701 KB, uploaded 26 March 2016]   |  

Abstract

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. View Full-Text
Keywords: defect detection; extreme learning machine; feature extraction; machine learning; nondestructive testing; wavelet transform defect detection; extreme learning machine; feature extraction; machine learning; nondestructive testing; wavelet transform
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Zhang, J.-K.; Yan, W.; Cui, D.-M. Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines. Sensors 2016, 16, 447.

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