地铁盾构施工诱发地表沉降关键影响因素分析
Key parameters analysis of surface subsidence induced by tunneling construction

为了明确地铁盾构施工诱发地表沉降的关键因素，提出了一种基于粗糙集支持向量机(RS-SVM)的关键参数及其组合的建模与求解方法。利用信息熵规则将影响地表沉降的内摩擦角、内聚力等7个连续变量进行离散化处理；结合粗糙集遗传算法进行属性约简处理，获得影响盾构施工地表沉降的4个关键参数，即单环注浆压力、内摩擦角、比扭矩均值、切口泥水压力均值；采用支持向量机辨识对盾构参数与地表沉降之间关系反映效果最好的参数组合，作为实际盾构施工过程的关键参数。并将其运用到武汉轨道交通2号线越江隧道工程中，结果论证了该方法的科学性和可行性。
In order to identify the key factors inducing the surface subsidence while shield tunneling，a key parameter selection model and solving method based on RS-SVM is proposed. The information entropy rules were used to discretize seven continuous variables including Internal Friction Angle(IFA)，Cohesive Force(CF)etc. Genetic Algorithm and Rough Set for attribute reduction were combined to obtain several collections that significantly affect the surface subsidence; Using the statistical learning of RVM to select the best collection which optimally reflects the relationship between parameters and surface subsidence，we get four parameters: Single Ring Grouting Pressure，Internal Friction Angle，Specific Torque(ST)，Incision of Slurry Pressure and each of the collections was the critical parameter that should be considered in construction. The method was applied in a completed metro tunnel in Wuhan，China and the results indicated the feasibility of the method.