基于蚁群优化支持向量机的公路隧道围岩变形预测模型及应用

为及时掌握隧道施工中围岩变形趋势以便采取措施加以控制,采用基于结构风险最小化的支持向量机(SVM)进行预测。介绍支持向量机的基本原理,研究蚁群算法(ACO)实现支持向量机参数优化的方法,构建ACOSVM模型。对某公路隧道随机选取的2个监测断面的预测结果表明,该模型预测精度较高,泛化性能较好,用蚁群算法进行SVM参数优选是一种简单、优选的方法,可以有效指导隧道的施工。

Highway Tunnel Surrounding Rock Deformation Prediction Model Based on Support Vector Machine Optimized by Ant Colony Optimization and Its Application

Support vector machine （SVM） based on structural risk minimization is used to predict the deformation of tunnel surrounding rocks, so as to understand the deformation trend of the surrounding rocks and to take measures to control the deformation. In the paper, the principle of support vector machine is described, the parameter optimization method based on ant colony optimization （ACO） is studied, and the ACOSVM model is established. The deformation prediction model has been applied in the construction of a highway tunnel, which shows that the model has high precision and can provide effective guidance for the tunnel construction. The paper can provide reference for similar projects in the future.