CAJ | 학술논문

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。
위료명학지철순구시공유발지표침강적관건인소，제출료일충기우조조집지지향량궤（RS‐SVM ）적관건삼수급기조합적건모여구해방법。이용신식적규칙장영향지표침강적내마찰각、내취력등7개련속변량진행리산화처리；결합조조집유전산법진행속성약간처리，획득영향순구시공지표침강적4개관건삼수，즉단배주장압력、내마찰각、비뉴구균치、절구니수압력균치；채용지지향량궤변식대순구삼수여지표침강지간관계반영효과최호적삼수조합，작위실제순구시공과정적관건삼수。병장기운용도무한궤도교통2호선월강수도공정중，결과론증료해방법적과학성화가행성。
In order to identify the key factors inducing the surface subsidence w hile 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 .