地质突变条件下基于组合模型的围岩等级和TBM掘进参数预测

为了提高硬岩隧道掘进机(TBM)施工的安全性和智能化,基于TBM掘进数据,提出了一种将双向长短期记忆(BiLSTM)网络与支持向量机回归(SVR)算法相结合的可以同时进行围岩等级和TBM掘进参数预测的组合模型(BiLSTM-SVR模型)。实例验证结果表明:BiLSTM-SVR模型对围岩等级的预测准确度较高,均方根误差均小于0.026 5、平均绝对百分比误差均小于0.95%；BiLSTM-SVR掘进参数预测中,推力和扭矩的预测准确度最高,净掘进速度和开挖比能的预测准确度最低；BiLSTM-SVR模型比BiLSTM模型和SVR模型的掘进参数预测准确度有较大的提高,因此进行单一模型的组合可以有效提高模型预测的准确度和鲁棒性。

Prediction of surrounding rock grades and TBM tunnelling parameters based on combined model under geological mutation conditions

In order to enhance the safety and intelligence in the tunnelling of hard rock tunnel boring machines (TBM), this paper proposed a prediction model (BiLSTM-SVR model) that combined the bidirectional long short-term memory (BiLSTM) neural network with the support vector regression (SVR) algorithm, and the proposed model was capable of concurrently predicting surrounding rock grades and TBM tunnelling parameters based on the TBM tunnelling data. Example verification results show that the BiLSTM-SVR model demonstrates high accuracy in predicting surrounding rock grades, with the root mean square error (RMSE) and the mean absolute percentage error (MAPE) both being less than 0.026 5 and 0.95%, respectively. In the prediction of tunnelling parameters using the BiLSTM-SVR model, the accuracy in predicting thrust and torque is the highest, while the accuracy in predicting net excavation speed and excavation efficiency is the lowest. The proposed model exhibits significantly improved accuracy in predicting tunnelling parameters compared to the BiLSTM model and the SVR model individually, thereby combining single model effectively enhances the accuracy and robustness of model prediction.