基于PSO-RF模型的复杂地层双模盾构土压掘进模式下密封舱压力预测

Prediction of sealed cabin pressure in the earth pressure excavation mode of dual-mode shield tunnelling mode in complex formations based on PSO-RF model

依托广州地铁7号线2期工程洪圣沙-裕丰围区间隧道工程,采用粒子群算法优化随机森林方法(particle swarm optimization-random forest algorithm,PSO-RF)建立双模盾构土压掘进模式下密封舱压力预测模型。通过对盾构掘进参数进行相关性分析,筛选出对密封舱压力影响较大的掘进参数,包括螺机转速、螺机扭矩、刀盘转速、推进速度、贯入度、刀盘扭矩、总推力,将筛选出的掘进参数作为预测模型输入参数,密封舱压力作为模型的输出参数,对密封舱压力进行预测。结果表明:采用PSO-RF预测模型能够有效预测双模盾构密封舱压力;相比于传统神经网络预测模型,PSO-RF模型预测精度更高,平均绝对误差均在10%以内,预测值和实际值的拟合优度R^(2)为0.9014,在预测精度及模型的泛化能力上明显优于BP神经网络。

Relying on the Hongshengsha-Yufengwei Section Tunnel Project of the second phase of Guangzhou Metro Line 7,this paper used the particle swarm optimization random forest method(PSO-RF)to establish a sealed cabin pressure prediction model in the dual-mode shield soil pressure boring mode.Through the corelation analysis of shield boring parameters,the tunneling parameters that had a great influence on the pressure of the sealing chamber were screened out,including screw speed,screw torque,cutterhead speed,propulsion speed,penetration degree,cutterhead torque,and total thrust,and the screened tunneling parameters were used as the input parameters of the prediction model,and the pressure of the sealing chamber was used as the output parameter of the model to predict the pressure of the sealing chamber.The results showed that the random forest PSO-RF prediction model improved by particle swarm algorithm could effectively predict the pressure of the dual-mode shield sealed cabin,and compared with the traditional neural network prediction model,the PSO-RF model had higher prediction accuracy,the average absolute error was within 10%,and the predicted value and the actual goodness-of-fit R^(2)were 0.9014,which was significantly superior to the BP neural network in terms of prediction accuracy and generalization ability of the model.