Classification and recognition method of concrete defects based on deep learning of impact echo features

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (11) : 19-28.

SHOCK AND EXPLOSION

Classification and recognition method of concrete defects based on deep learning of impact echo features

WANG Kui*1, CHEN Yongjiang1, FAN Zhengqiang1,2, LI Pengfei1

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Abstract

Accurately identifying and locating internal defects is crucial for ensuring safety and durability of concrete structures. The impact echo (IE) method has unique advantages for concrete structures with only a single inspection surface. When using IE method for testing internal defects in concrete structures, the difficulty lies in analyzing features of IE signals and classifying and identifying defects. Here, a method for identifying internal defects in concrete was proposed based on deep learning of time-frequency maps of IE signals. Firstly, IE tests were conducted for concrete slabs containing different types of defects including holes, honeycombs and cracks. Then, the original IE signals were converted into time-frequency maps with short-time Fourier transform and continuous wavelet transform, respectively. Finally, 2 types of time-frequency maps were combined for data expansion to construct datasets containing defect types, hole sizes and crack depth time-frequency maps. 3 types of deep learning models of Swin Transformer, Vision Transformer and ResNet18 were used for training. Based on the relevant evaluation indexes of deep learning models, performances of different deep learning models under different datasets were analyzed, and the classification and recognition effects of models on defects were compared. The results showed that Swin Transformer performs the best among 3 models, its correctness rate is over 95.00% in defect type classification, and it has good recognition performance.

Key words

concrete / impact echo (IE) / deep learning / defect / classification and recognition

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WANG Kui1, CHEN Yongjiang1, FAN Zhengqiang1, 2, LI Pengfei1. Classification and recognition method of concrete defects based on deep learning of impact echo features[J]. Journal of Vibration and Shock, 2025, 44(11): 19-28

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