PDF(2870 KB)
Research on tunnel lining disease identification method based on LSTM neural network
Hao XIE, JingYu WANG
Prog Geophy ›› 2025, Vol. 40 ›› Issue (5) : 2227-2236.
PDF(2870 KB)
PDF(2870 KB)
Research on tunnel lining disease identification method based on LSTM neural network
With the rapid development of tunnel transportation in China, the workload of routine maintenance of tunnels has increased dramatically. The use of Ground Penetrating Radar (GPR) to detect tunnel lining diseases is becoming a common method due to its economic, efficient and non-destructive characteristics. But the reflection feature recognition and interpretation from massive radar data collected is time consuming and highly dependent on human experiences. In order to improve the detection efficiency and accuracy, this paper proposed an identification method of the tunnel lining diseases based on LSTM neural network. This method integrated the advantages of LSTM neural networks in the field of image recognition and the characteristics of GPR reflection signals from tunnel lining diseases. Then LSTM neural networks were applied to detect and recognize tunnel lining diseases for the first time. First, the reflected radar signals corresponding to different tunnel disease models were obtained by FDTD numerical simulation. Secondly, the dataset was constructed through different ways, such as changing the type, the thickness and the buried depth of the disease area, and adding noise to the radar signals. Then the whole dataset was randomly divided into the training dataset, the testing dataset and the validation dataset. Thirdly, a neural network was built based on LSTM and the network model parameters and structures were optimized. A conclusion was drawn that the three-layer Bi-LSTM network has higher accuracy and more stable training process. Finally, the neural network was verified, and three parameters (the accuracy rate, the precision rate and the recall rate) were used for the result evaluation. The results show that the identification method based on LSTM neural network can quickly identify two tunnel diseases with high accuracy and stable process. This research provides a new option for the popularization and development of intelligent identification methods of tunnel lining diseases in the future. It also broadens the application area of LSTM neural network.
Deep learning / LSTM neural network / Ground penetrating radar / Finite difference time domain / Intelligent identification
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感谢审稿专家提出的修改意见和编辑部的大力支持！
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