利用PLSR-DNN耦合模型预测TBM净掘进速率

Predicting TBM penetration rate with the coupled model of partial least squares regression and deep neural network

科学预测隧道掘进机(TBM)净掘进速率,对于隧道(洞)工程施工方法选择、施工进度安排以及成本估计具有重要意义。鉴于TBM施工过程具有高度非线性、模糊性和复杂性等特征,为提高TBM净掘进速率的预测精度和计算效率,采用偏最小二乘回归(PLSR)提取影响参数主成分,再利用深度神经网络(DNN)进行训练预测,提出了一种基于PLSR-DNN耦合方法的TBM净掘进速率预测模型。基于兰州水源地建设工程输水隧洞双护盾TBM施工实测数据,选择岩石单轴抗压强度、单轴抗拉强度、刀盘推力、刀盘转速、岩体完整性系数和岩石耐磨性指数,共6个影响参数,验证了模型预测的合理性,并对不同预测方法的拟合精度和预测精度进行了对比分析。研究结果表明:(1)偏最小二乘回归可有效克服自变量之间的多重共线性问题,将提取的主成分作为深度神经网络的输入层进行训练,简化了神经网络结构;(2)PLSR-DNN耦合预测模型避免了过拟合与拟合不足问题,具有收敛速度快,求解稳定和拟合精度高等特点;(3)PLSR-DNN耦合预测模型平均相对拟合误差2.96%,平均相对预测误差3.27%,其拟合精度和预测精度均明显高于偏最小二乘回归模型、BP神经网络模型以及支持向量回归(SVR)模型。

The scientific prediction of the TBM penetration rate is of great significance to the selection of hydraulic tunnel construction methods,construction schedule and cost estimation.In view of the high nonlinearity,fuzziness and complexity of TBM excavation process,and in order to improve the prediction accuracy and computational efficiency,the partial least squares regression(PLSR)has been applied to extract the principal components of the influencing parameters.Then the deep neural network(DNN)is employed to train and forecast the TBM penetration rate.A prediction model of TBM penetration rate based on the coupled method of PLSR and DNN is proposed.Based on the measured data of the double-shield TBM construction of a water conveyance tunnel in the Lanzhou water source construction project,six impact parameters including the rock uniaxial compressive strength,rock uniaxial tensile strength,cutter head thrust,cutter head speed,rock mass integrity coefficient and rock Cerchar abrasiveness index are selected to verify the prediction reasonability of the model.The fitting and prediction accuracy of the different prediction methods are compared and analyzed.The research results show that the PLSR can effectively overcome the problem of multiple collinearity between the independent variables.The extracted principal components are trained as the input layer of the DNN,which simplifies the structure of the neural network.The PLSR-DNN coupled model effectively avoids the over-fitting and inadequate fitting problems.It has the characteristics of fast convergence,stable solution and high fitting accuracy.The average relative fitting error of the PLSR-DNN prediction model is 2.96%,and the average relative prediction error is 3.27%.The fitting accuracy and prediction accuracy of the PLSR-DNN prediction model is significantly higher than those of PLSR model alone,BP neural network model and SVR model,respectively.