Prediction of EPB Shield Tunneling Advance Rate in Mixed Ground Condition Using Optimized BPNN Model

Abstract

Tunneling in mixed ground often results in severe torque fluctuations and a low advance rate. Therefore, choosing a reasonable set of parameters for accurate advance rate prediction is paramount to reduce cutter wear and improve tunneling efficiency. However, since the geological parameters in mixed ground conditions are diverse and uncertain, the prediction of the advance rate (AR) of EPB shield tunneling is significantly more difficult than that in homogeneous ground (i.e., full-face hard-rock ground). In addition, the operating parameters of the EPB shield tunneling can be subjective and suboptimal, and each of them has some intricate influence on AR. In this paper, an optimized back-propagation neural network by genetic algorithm (BPNN-GA) was proposed for reasonable operating parameter selection and accurate AR prediction, and four typical machine learning methods were used for comparison. Five processing strategies with different input parameters were also proposed and compared to determine the optimum selection of geological parameters in mixed ground conditions. The proposed models with strategies were adopted in the case study of the Nanjing Metro Line S6 project, and a total of 1188 rings of datasets were used for this study. The results showed that the proposed modified BPNN with the genetic algorithm could be effectively implemented for the AR prediction. It concluded that Strategy B—i.e., using the composite ratio and the geological parameters of each layer as input—was the best strategy in mixed ground conditions for advance rate prediction. Hence, a high correlation between measured and predicted AR was observed in this study with a correlation coefficient (R2) of 0.920.