软土地层盾构掘进参数分析及掘进速度预测

Analysis of Boring Parameters of Shield in Soft Soil Strata and Prediction of Driving Speed

依托佛山地铁3号线逢沙站—创意园站区间隧道工程,通过现场实测数据,详细分析了土压平衡盾构穿越软土地层时盾构掘进参数内在变化规律,并建立了掘进速度预测模型。首先,对盾构掘进参数进行数理统计分析,对各掘进参数的分布进行正态性检验;其次,进行Pearson相关性分析,找出线性相关性较强参数间变化规律;再次,利用基于互信息的特征选择算法,筛选与掘进速度非线性相关性较高的参数变量;最后,分别建立随机森林回归预测模型和基于遗传算法优化BP神经网络预测模型,对掘进速度进行预测。研究结果表明:在软弱地层盾构隧道工程中,通常采用较低的刀盘转速、刀盘扭矩及较高的掘进速度、贯入度、盾构总推力、土仓压力;掘进速度等参数均通过了采用K-S检验法的正态性检验;掘进速度与贯入度存在极强相关性关系;基于遗传算法优化BP神经网络预测模型的预测精度略优于随机森林回归预测模型,随机森林回归预测模型在测试集中的平均绝对误差、均方根误差、拟合优度分别为4.055、5.038、0.871,而基于遗传算法优化BP神经网络预测模型分别为0.822、1.244、0.991。

Taking the tunnel project from Fengsha Station to Creative Park Station of Foshan Metro Line 3 as background,the inherent variation tendency of boring parameters of shield when the EPB shield tunnel crossed the soft soil strata was analyzed in detail through the on-site measured data,and the different prediction models of driving speed were built.Firstly,the shield tunneling parameters were analyzed by mathematical statistics,and the distribution of each tunneling parameter was tested.Secondly,the Pearson correlation analysis was performed to find out the variation law between the parameters with strong linear correlation.Then using the feature selection algorithm based on mutual information,the parameter variables with high nonlinear correlation with the driving speed were screened.Finally,the random forest regression prediction and the BP neural network prediction model based on genetic algorithm optimization were established respectively to predict the driving speed.The research results showed that in shield tunnel projects in soft formations,lower cutterhead speed,cutterhead torque,higher tunneling speed,penetration,total shield thrust and soil silo pressure were usually used.The parameters such as the excavation speed passed the normality test using the K-S test method.There was a strong correlation between the speed of excavation and the degree of penetration.The average absolute error,root mean square error and goodness of fit of the random forest regression prediction model in the test set were 4.055,5.038 and 0.871,respectively,while the optimization of the BP neural network prediction model based on genetic algorithm was 0.822,1.244 and 0.991,respectively.