The penetration rate (PR) is a critical indicator affecting the safety and cost of shield tunnel construction. However, due to the complexity of geological conditions and the nonlinear nature of tunneling parameters, traditional prediction methods struggle to achieve high-accuracy predictions. To address this issue, six hybrid deep extreme learning machine models were developed for PR prediction. Normalized mutual information (NMI) was employed to select key features, and an isolation forest (IForest) algorithm was employed to remove outliers and construct a valid dataset. Subsequently, deep extreme learning machines optimized using six metaheuristic algorithms were applied to predict the penetration rate. Finally, the key factors influencing tunneling rate prediction were identified based on SHAP analysis. The experimental results demonstrate that among the six optimized algorithm models, along with the BP neural network, uniaxial compressive strength (UCS), rock quality designation (RQD), and cutterhead torque were identified as key factors influencing PR. For the first time, the CTCM-DELM model is applied to predict the advance rate of shield tunneling. Combined with SHAP analysis, it is quantitatively revealed that the contribution of geological parameters is greater than that of equipment parameters, which provides novel insight for engineering practice.
Yuan et al. (Tue,) studied this question.