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

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., def...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2016-03, Vol.16 (4), p.447-447
Main Authors: Zhang, Jing-Kui, Yan, Weizhong, Cui, De-Mi
Format: Article
Language:English
Subjects:
Concretes
Construction
Data analysis
defect detection
Defects
extreme learning machine
feature extraction
Fourier transforms
Machine learning
Methods
Neural networks
Nondestructive testing
Pattern recognition
Sensors
Signatures
Spectrum analysis
Thickness
wavelet transform
Wavelet transforms
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s16040447