基于粒子群算法优化的模糊支持向量机的盾构隧道地质变形预测研究

Study on Prediction of Geological Deformation of Shield Tunnel Based on Fuzzy Support Vector Machine Optimized by Particle Swarm Optimization

盾构速调地质变形预测对于提高盾构隧道施工可靠性,提高盾构隧道施工的现代化水平具有非常关键的作用,因此,可将基于粒子群算法优化的模糊支持向量机应用于盾构隧道地质变形的预测中。本文首先分析了盾构隧道地质变形计算的理论模型;然后研究了模糊支持向量机的基本理论;接着讨论了粒子群算法,并且设计了相应的算法流程;最后进行盾构隧道地质变形预测的仿真分析。仿真结果表明,基于粒子群算法优化的模糊支持向量机能够提高盾构隧道地质变形的预测精度。

The shield klystron geological deformation prediction for improving reliability of shield tunnel construction, improving the modernization level of the shield tunnel construction has very important role, therefore, we will be based on fuzzy particle swarm optimi- zation algorithm and the application of support vector machine deformation in shield tunnel geological prediction. First of all, the theoret- ical model of tunnel deformation is analyzed. Secondly, the basic theory of fuzzy support vector machine is studied, and then the particle swarm optimization algorithm is discussed, and the corresponding algorithm is designed. Finally, the simulation analysis of the tunnel deformation prediction is carried out. The simulation results show that the fuzzy support based on particle swarm optimization can improve the prediction accuracy of the shield tunnel geological deformation.