Application of Improved One-Dimensional Convolutional Neural Network in Tunnel Surrounding Rock Deformation Prediction

To address the inherent limitations of traditional forecasting methods in the context of complex geological environments, this study introduces a novel and enhanced one-dimensional convolutional neural network (1D-CNN) tailored specifically for predicting tunnel surrounding rock deformation. By meticulously refining the architecture of the convolutional and pooling layers, the proposed model significantly augments its ability to extract and interpret intricate features from time-series data, thereby improving prediction accuracy. The research encompasses a comprehensive system framework that includes algorithm development and optimization, model training, validation, and rigorous system simulation. During the simulation phase, an extensive array of datasets comprising real-world tunnel surrounding rock measurements is employed to thoroughly evaluate the model's predictive accuracy and robustness, benchmarking it against conventional approaches. The simulation results demonstrate that the upgraded model achieves superior performance, with the mean error reduced by more than 15% compared to traditional methods, highlighting its enhanced stability and reliability. Detailed data analysis further validates the algorithm's broad applicability across diverse geological conditions, offering substantial technical support for real-time monitoring and early warning systems in tunnel engineering. This study not only contributes to the advancement of predictive modeling techniques but also provides a robust foundation for future research in optimizing tunnel stability under varying geological conditions.