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Internal Defect Detection Quantification and Three-Dimensional Localization Based on Impact Echo and Classification Learning Model

Yanqi Wu,Jian Zhang, Chenhao Gao, Junde Xu

MEASUREMENT(2023)

Southeast Univ | China Railway Construction Suzhou Design & Res Ins

Cited 8|Views5
Abstract
Accurately identifying, localizing, and characterizing internal defects is crucial for ensuring the safety and durability of concrete structures. While impact echo (IE) is a highly promising non-destructive testing method for detecting concrete internal defects, previous studies have primarily focused on defect identification with less emphasis on quantification and depth localization of internal defects. In this paper, we propose an intelligent detection method based on IE and deep learning to achieve intelligent identification, area quantification, and depth localization of concrete internal defects. The proposed method includes the following three components: (1) A one-dimensional model combining wavelet packet decomposition and the Gate Recurrent Unit network was proposed to achieve an automatic diagnosis of defect signals inside concrete structures. (2) A method incorporating defect identification probability heatmap and threshold segmentation is employed to quantify the concrete defect area and detect the defect area detection rate. (3) A two-dimensional model combining wavelet transform and convolutional neural network was developed to achieve defect depth localization. The proposed method has been effectively validated in laboratory experiments involving concrete slabs with artificial defects.
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Key words
Defect recognition,Impact echo,Deep learning,Threshold segmentation,Region quantization,Depth localization
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要点】:本文提出了一种基于冲击回波和分类学习模型的新型智能检测方法,实现了混凝土内部缺陷的智能识别、面积量化及深度定位,提高了检测的准确性、量化度和定位精确性。

方法】:通过结合波let包分解和门控循环单元网络的一维模型来自动诊断混凝土内部的缺陷信号,并采用带有缺陷识别概率热图和阈值分割的方法来量化混凝土缺陷面积,以及开发了一种结合波let变换和卷积神经网络的二维模型来进行缺陷深度定位。

实验】:在含有人工缺陷的混凝土板实验室实验中验证了所提方法的有效性,使用的数据集为实验室制备的混凝土板数据,实验结果显示了方法在缺陷检测、量化和深度定位方面的准确性。