Intelligent Classification of Rocks in Mountain Highway Tunnels Using ISSA-ELM Model

During construction, mountainous highway tunnels are often subjected to complex forces and are prone to large deformations, which severely affect the long-term stability of the surrounding rock. Therefore, it is crucial to explore rapid identification and intelligent classification methods for rock surrounding tunnels. In response to the above issues, this study proposes a new intelligent classification and prediction method for rock surrounding highway tunnels on the basis of an index classification system of the environmental characteristics of rock surrounding highway tunnels combined with deep learning algorithms. This method can optimize the generative adversarial network for tabular data (CTGAN) via a genetic algorithm (GA) to increase the data volume and then use the Kolmogorov–Smirnov (K–S) test to determine the optimal parameters and samples in the CTGAN with a small number of samples. By combining the sparse search algorithm (SSA) and extreme learning machine (ELM) to construct the SSA-ELM model, the Singer mapping method is used to handle the sparrow random initialization problem, the parameters of the SSA-ELM model are further optimized via the K-fold cross-validation method, and the ISSA-ELM combination model is established. Finally, on the basis of actual engineering cases, 160 sets of data were optimized to analyse and evaluate the classification of surrounding rocks, verifying the rationality and effectiveness of the model. The research results show that the ISSA-ELM combination model minimizes the negative impact of subjective factors on the model and has the advantages of extremely low error, accurate stability, and high robustness. This can provide an important reference for predicting the stability of the surrounding rock in mountain road tunnels.