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.
Highlights
Detection of internal defects of concrete structures and, more importantly, accurately identifying and characterizing the internal defects are crucial in ensuring the safety of concrete structures
The IE method is only effective for detecting certain defects in plate like concrete structures, and, most importantly, cannot be used for full condition assessment of concrete elements, mainly because its data analysis and interpretation involves the simple spectrum analysis and the simple thickness resonant frequency calculation
Our machine learning based condition assessment system consists of two primary components, feature extraction and condition assessment
Summary
Detection of internal defects of concrete structures and, more importantly, accurately identifying and characterizing the internal defects are crucial in ensuring the safety of concrete structures. Numerous non-destructive testing (NDT) methods have been developed and applied for detecting internal defects of concrete structures. These NDT methods include ultrasonic pulse, impact-echo, and ground penetrating radar technologies, to name a few. For example, [1,2,3,4,5], provided an overview of different NDT methods. Among those different NDT technologies, the impact-echo (IE) method is the most popular one used for detecting cracks and delamination of concrete structures. The popularity of the IE method comes from its advantages of:
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