Deep hybrid neural network-aided electromechanical impedance method for automated damage detection of lining concrete under freeze-thaw cycling

Abstract

Cold regional tunnels extensively suffer from severe damage in concrete linings under cyclic freeze-thaw environment. Therefore, accurate detection and evaluation of cyclic freeze-thaw damage within lining concrete is of great significance to help grasp structural health state and guarantee timely maintenance. This study pioneered the application of electromechanical impedance (EMI) method to monitor the freeze-thaw damage in bended concrete beams. The mass loss and flexural strength degradation of concrete beams under two different bending loads were thoroughly assessed to quantify the evolution of cyclic freeze-thaw damage. Moreover, the conductance signatures driven by d31 and d33 modes were analyzed, respectively. It was found that the variation in the d31 mode-dominated signal well agreed with the progressive damage characterized by the flexural strength degradation. The key innovation of this study is that a deep hybrid neural network DenseNet–GRU was constructed and well trained to predict the cyclic freeze-thaw damage from augmented EMI data. The results indicated that the proposed model achieved excellent performance with determination coefficients exceeding 0.997 for both bending scenarios. Additionally, DenseNet–GRU outperformed conventional baseline machine or deep learning models in prediction accuracy and noise-resistance capacity. Notably, it demonstrated good adaptability when trained with limited data samples. In summary, the proposed methodology enabled automated detection and accurate forecasting of the cyclic freeze-thaw damage in lining concrete without hand-crafted features.