Tunnel construction requires accurate and timely classification of surrounding rock masses to ensure safety and guide excavation. This research addresses the limitations of conventional methods and unimodal intelligent approaches by proposing a novel hybrid deep model, Random-Mamba, that integrates drilling parameters and digital images for enhanced classification performance. A dataset of 3361 synchronized samples was constructed, containing six drilling parameters, digital face images, and expert-classified rock mass grades. The model employs a dual-branch architecture: a Random Forest processes the drilling parameters, and a MambaVision network extracts visual features, with a multilayer perceptron performing the fusion. The proposed model achieved an overall accuracy of 92.12% and a macro-F1 score of 91.66%, outperforming the most comparable hybrid model by 2.61% in accuracy. It demonstrated particularly high precision in identifying Class III rock with an F1-score of 93.2%. Ablation and comparative experiments confirmed its superiority over both single-modality models, such as SVM and ResNet, and other hybrid architectures, like Random-Swin. SHAP-based sensitivity analysis further revealed that feed speed was the most influential drilling parameter for classification. The effective fusion of complementary mechanical and visual data provides a robust and practical solution for real-time rock mass assessment in tunneling engineering.
Yang et al. (2026) studied this question.