智能三维位移反分析法在浅埋偏压隧道中的应用  

Intelligent displacement back analysis method of three-dimension applied in unsymmetrical pressure tunnel with shallow depth

作　　者：万良勇[1] 张雪峰[2] 刘开云[1] 

机构地区：[1]北京交通大学土木建筑工程学院,北京100044 [2]北京工业大学城市与工程安全减灾省部共建教育部重点实验室,北京100124

出　　处：《北京交通大学学报》2012年第4期29-33,共5页JOURNAL OF BEIJING JIAOTONG UNIVERSITY

基　　金：中央高校基本科研业务费专项资金资助(2011JBM267)

摘　　要：支持向量机(Support Vector Machines,SVM)算法具有小样本、全局优化和泛化性能好的优点,且不存在过拟合的弊病.结合张石高速北口隧道浅埋偏压段的施工,将一种改进的支持向量机算法引入隧道工程位移反分析,并采用遗传算法在样本训练阶段自动搜索训练效果最优的SVM参数,建立起围岩力学和初始地应力参数与洞周位移的非线性SVM映射;然后,以遗传算法在围岩力学和初始地应力参数范围内,自动搜索能使SVM计算位移与实测位移最接近的参数组合,完成围岩力学和初始地应力参数的智能辨识.应用结果表明,这种新型的智能位移反分析法能在监测数据有限的情况下,高精度地反演辨识围岩力学与初始地应力场参数,为围岩变形超前预报提供计算参数以指导施工,并为类似工程提供借鉴.Support Vector Machines algorithm has the advantages of small sample, global optimization and generalization performance. A direct optimization method based on genetic algorithm and the im- proved support vector regression algorithm （GA-SVR） is applied in order to identify multinomial pa- rameters intelligently and forecast displacements fast and exactly, combined with an unsymmetrical pressure tunnel with shallow depth section of the left line of import in Beikou Tunnel on Zhangjiakou Shijiazhuang highway. The application result shows the new type of intelligent displacement back analysis could obtain accurately the parameters of rock mechanics and initial stress in limited monitor- ing data and provide parameters for advance forecast of rock deformation. It is also applied on tunnel and similar geotechnical engineering.

关 键 词：隧道工程 智能反分析 支持向量回归 遗传算法 直接优化法 

分 类 号：U452.2[建筑科学—桥梁与隧道工程]