In order to establish an optimal model for reasonably predicting the uniaxial compressive strength (UCS) of rocks, a method based on feature optimization and SSA-XGBoost was proposed. Firstly, the UCS predictor system of rocks, considering petrographic and physical parameters, was determined based on the systematic discussion of the factors affecting the UCS of rocks. Then, a feature selection method combining the RReliefF algorithm and Pearson correlation coefficient was proposed to further determine the optional input features. The XGBoost algorithm was used to establish the prediction model for rock UCS. In the process of model training, the Sparrow Search Algorithm (SSA) was used to optimize the hyperparameters. Finally, model evaluation was carried out to test the performance of the UCS prediction model. The method was applied and validated in a granitic tunnel. The results show that the proposed UCS prediction model can effectively predict the UCS of granitic rocks. Compared with simply adopting petrographic or physical parameters as the input features of the model, the UCS predictor considering petrographic and physical characteristics can improve the generalization ability of the SSA-XGBoost UCS prediction model effectively. The prediction method proposed in this study is reasonable and can provide some reference for establishing a universal method for accurately and quickly predicting the UCS of rocks.

The rock classification system was initially applied to drill-and-blast tunnels and subsequently adapted for TBM tunnels; however, the majority of these systems primarily focused on rock stability while neglecting considerations of boreability. Compared with conventional tunnels, TBM tunnels are characterized by their rapid tunneling speed and excellent self-stabilization of the rock mass. Therefore, it is imperative to develop a novel rock mass classification system that considers both the tunneling efficiency of TBMs and the required support measures for tunnel construction. This paper introduces a novel rock classification system for TBM tunnels that accurately predicts the construction rate by evaluating the penetration rate and daily utilization, enabling a more precise assessment of daily advancement in tunneling. Firstly, the penetration rate and construction utilization in different rock strata are analyzed based on comprehensive statistics of existing construction data. Consequently, a discriminant matrix for classifying the boreability of rock is derived. Subsequently, employing the Ensemble Classifier method, a regression prediction model for rock boreability classification is established by incorporating input parameters such as thrust, torque, rotational speed, field penetration index, and the uniaxial compressive strength of rock. The validity of the proposed model is verified by comparing predicted machine performance with actual data sets. The proposed method presents a novel approach for predicting the performance of TBM construction.