Internal defect detection quantification and three-dimensional localization based on impact echo and classification learning model

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.