|Table of Contents|

Research on coordinate localization of concrete cracks based on regression model and acoustic emission(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2026年2期
Page:
57-72
Research Field:
桥梁与隧道工程
Publishing date:

Info

Title:
Research on coordinate localization of concrete cracks based on regression model and acoustic emission
Author(s):
YAN Dong-huang;  YUAN Zhen;  WANG Shuo
(School of Civil Engineering, Changsha University of Science and Technology,Changsha 410114, Hunan, China)
Keywords:
bridge engineering;  crack detection;  convolutional neural network;  concrete;  acoustic emission;  regression model
PACS:
U448.34
DOI:
10.19721/j.cnki.1671-8879.2026.02.005
Abstract:
To achieve high-precision localization of crack sources in reinforced concrete beams, this study investigated the use of acoustic emission(AE)technology combined with a deep learning regression model. Based on AE signals from both simulated and real crack sources, a deep regression neural network consisting of convolutional layers, pooling layers, flattening layers, fully connected layers, and dropout layers was designed to directly predict the spatial coordinates of crack sources. The network was trained and optimized by using simulated signals, and its localization performance was validated with real crack signals. The results show that the model achieves an average localization error of 2.51 cm in reinforced concrete structures, demonstrating good localization accuracy. In sensitivity analysis, the model exhibits high localization accuracy in the early and middle-stages of crack propagation. However, as the crack extends into the later stages, the complexity of the signals increases and the overlap of microcracks affects the model, leading to an increase in prediction errors and a decrease in localization accuracy. Robustness analysis under different noise levels shows that the model can maintain stable localization performance even under low signal-to-noise ratio(SNR)conditions. Furthermore, by introducing Euclidean distance as the loss function, the model's ability to adapt to complex materials and its accuracy in predicting crack locations are significantly improved. The findings suggest that the model has good potential for application in the localization of crack sources in reinforced concrete structures.1 tab, 22 figs, 30 refs.

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Last Update: 2026-04-20