与常规土压平衡盾构掘进相比，气压辅助掘进条件下的地表变形过程更为复杂。为提高预测模型的工程适应性，准确预测气压辅助条件下的地表变形量，保障该条件下盾构顺利掘进，引入支持向量机（SVM）理论，利用粒子群算法（PSO）对支持向量机的超参数组合进行优化; 同时，优化模型输入参数，将隧道上覆黏土层厚度和气压值设为输入参数，并针对上覆非均质土层改进模型参数计算方法，建立适用于气压辅助掘进的PSO-SVM地表变形预测模型。为验证预测模型的准确性和实用性，以广州地铁18号线陇枕出入场线某一区间为例，使用优化了输入参数的模型进行预测，并在此基础上，综合采用PSO-SVM、SVM和PSO-BP 3种模型进行地表变形预测分析。结果表明： 1）针对气压辅助掘进工法优化输入参数的PSO-SVM模型可以较好地满足工程需要； 2）PSO-SVM模型的适应性显著高于PSO-BP和SVM模型，具有较好的工程适用性。
Compared with the conventional earth pressure balance
shield tunneling method, the surface deformation that results from air pressureassisted
tunneling is more complex. To improve the adaptability of the prediction model,
accurately predict the surface deformation under the condition of air pressure,
and ensure successful shield tunneling, the theory behind the support vector
machine (SVM) is introduced, and particle swarm optimization (PSO) is used to
optimize the superparameter combination for the SVM. The input parameters of
the model are also optimized at the same time. The thickness of the clay layer
that overlies the tunnel and the air pressure are set as the input parameters,
and the method for calculating the model parameters is improved for the
overlying heterogeneous soil layer. A PSOSVM for model
for air pressureassisted tunneling is thus
established. To verify the accuracy and practicability of the prediction model,
the model is applied to a section of the Longzhen entrance and exit line of
Guangzhou metro line 18, and the data with optimized input parameters are used
for prediction. On this basis, an analysis of the predictions of the surface
deformation is conducted using the PSOSVM, SVM, and
PSObackpropagation(BP)3
models. The results show that: (1) the PSOSVM model
for optimizing the input parameters of airpressure
assisted tunneling can meet the engineering requirements; and (2) the PSOSVM model is significantly more adaptable than the PSOBP and SVM models, which indicates its applicability to engineering.
