基于局部线性嵌入和支持向量机回归的TBM施工参数预测

依托吉林引松工程开展隧道掘进机（TBM）施工参数预测研究，提出TBM施工数据分段提取算法，提取上升段前30 s的总推进力、刀盘转速、推进速度、刀盘扭矩、刀盘转速电位器设定值、推进速度电位器设定值、贯入度、贯入度指数(FPI)、扭矩切深指数(TPI)9个参数作为输入；通过局部线性嵌入（LLE）完成对上升段数据特征的降维；基于支持向量机回归（SVR）建立TBM施工控制参数（推进速度、刀盘转速）和负载参数（总推进力、刀盘扭矩）预测模型. 分析是否结合前一掘进循环的FPI、TPI指数进行预测对预测效果的影响. 结果表明，上述方法在推进速度、刀盘转速、总推进力、刀盘扭矩的预测中均取得了较好的预测效果，平均预测绝对百分比误差均小于15%，验证了该预测方法的有效性，该方法可以为TBM现场施工提供指导.

TBM tunneling parameters prediction based on Locally Linear Embedding and Support Vector Regression

Tunnel?boring?machine (TBM) tunneling parameter prediction was conducted based on the Yinsong project in Jilin. A TBM tunneling data segmentation method was proposed to extract features from rising phase and stable phase. Thrust, cutter head speed, advance rate, torque, cutter head speed setting, advance rate setting, penetration rate, field penetration index (FPI) and torque penetration index (TPI) in the first 30 s of rising phase were extracted as the input of the prediction models. The locally linear embedding (LLE) was used to reduce the dimensions of the characteristic data of rising phase. A prediction model for TBM construction control parameters (propulsion speed, cutter head speed) and load parameters (total propulsion force, cutter head torque) was established based on the support vector regression (SVR). The impact on the prediction effect of whether to combine the FPI and TPI indexes of the previous tunneling cycle was analyzed and compared. Results show that favorable prediction effects for propulsion speed, cutter head speed, total propulsion force and cutter head torque were obtained based on the proposed model. The mean absolute percentage errors on the test set were all below 15%. The proposed method can provide guidance for TBM site operation due to the high prediction accuracy.