基于RS-SVR的上软下硬地层盾构施工地表沉降预测

为了提高由盾构施工引起的软硬不均地层地表沉降预测的准确性，建立基于粗糙集-支持向量回归（RS-SVR）的地表沉降预测模型，并将该模型应用于实际地铁隧道工程的地表沉降预测中。首先，根据特定地质条件，从几何因素、地层因素和盾构施工因素选取影响地表沉降的条件属性，采用粗糙集理论的Pawlak属性重要度方法删除冗余数据，获取影响地表沉降的最优条件属性集。在此基础上，基于支持向量回归（SVR）建立RS-SVR地表沉降预测模型，并与没有经过属性约简的SVR模型进行对比分析。为了比较不同核函数对SVR模型的影响，RS-SVR和SVR模型分别选取径向基函数（RBF）、Sigmoid函数、Polynomial函数作为核函数对训练样本及测试样本进行回归预测。最后，利用佛山地铁2号线南湖区间上软下硬地层的20组地表沉降监测数据，对该模型予以试算。研究结果表明：将选取的影响地表沉降的12项条件属性约简为包含7项的最优条件属性集，分别为硬层比、黏聚力、内摩擦角、土仓压力、总推力、刀盘扭矩以及掘进时间，地表沉降分类结果与约简前保持一致；同类模型进行横向对比时，RBF作为核函数的RS-SVR模型和SVR模型预测误差分别为5.54%、13.10%，均低于以Sigmoid函数和Polynomial函数作为核函数时的预测误差；以同种核函数进行纵向对比时，RS-SVR模型预测误差分别为5.54%、11.48%、13.26%，均低于SVR模型预测误差的13.10%、15.71%、19.68%。

Predicting for Ground Surface Settlement Induced by Shield Tunneling in Upper-soft and Lower-hard Ground Based on RS-SVR

To improve the prediction accuracy of ground surface settlement induced by shield tunneling in heterogeneous ground, a model based on rough set-support vector regression (RS-SVR) for predicting ground surface settlement was established and applied to ground settlement in actual subway tunnelengineering. Conditional attributes affecting ground settlement including geometric, ground, and shield construction factors were selected according to specific geological conditions. Pawlak's degree of attribute method of rough set theory was used to delete redundant data to obtain the optimal set of attribute sets for ground settlement.On this basis, support vector regression (SVR) was applied to establish an RS-SVR ground settlement prediction model and was compared with the SVR model without attribute reduction.Moreover, to compare the influence of different kernel functions, a radial basis function (RBF), sigmoid function, and polynomial function were applied as kernel functions for regression prediction for training samples and test samples in RS-SVR and SVR models.Finally, the models were tested with 20 sets of ground settlement monitoring data of upper-soft and lower-hard ground in the Nanhu section of Foshan Metro Line 2.The results show that attribute reduction can condense 12 conditional attributes that affect ground settlement to the optimal conditional attribute set containing 7 items (hard layer ratio α, cohesive force c, internal friction angle φ, pressure of the soil bin, total thrust, torque of the cutter disk, and the driving time). Classification results with attribute reduction are the same as those without attribute reduction.When compared with a similar model,the prediction errors of RBF as a kernel function on RS-SVR and SVR models are 5.54% and 13.10%, respectively, which are lower than the prediction error when the sigmoid and polynomial functions are used as kernel functions.The prediction errors of the RS-SVR model are 5.54%, 11.48%, and 13.26%, respectively, which are lower than the SVR model prediction errors of 13.10%, 15.71%, and 19.68% when the same core function is used for longitudinal contrast.