基于v-SVR和MVPSO算法的边坡位移反分析方法及其应用

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Abstract To address the problems of the limit search space and local optimization in traditional particle swarm optimization algorithm，a modified variation particle swarm optimization(MVPSO) algorithm is proposed based on particle migration and variation by introducing migration operator and adapting mutation operator. The results of the benchmark test functions show that the convergence rate of this MVPSO algorithm has significantly improved than the traditional particle swarm optimization algorithm. For the nonlinear and multimodal problems，the proposed MVPSO functions well in searching the global minimum. In order to establish a nonlinear relation between the mechanical parameters of rock mass and the displacements，the MVPSO algorithm is adopted to search for the most suitable parameters of the v-SVR model. The results show that the prediction accuracy and generalization ability of the v-SVR have been significantly increased. Then，the optimal v-SVR model is an alternative for the time-consuming FLAC calculations；and the MVPSO algorithm can be used to search for the best group of the mechanical parameters of rock mass. Consequently，a new displacement back analysis method is developed in combination of the v-SVR with the MVPSO algorithm. Compared with the traditional displacement back analysis methods，including BP-GA and the v-SVR-GA，the proposed method has its merits in inversion efficiency and accuracy. Finally，the new method is applied to the parametric back analysis of rock mass in the right-bank slope of Dagangshan hydropower station. Based on the back-analyzed parameters，the deformation and stability of the slope during subsequent construction period are analyzed. The results demonstrate that the proposed method has high accuracy and good applicability.

Key words： slope engineering v-SVR particle swarm optimization(PSO) modified particle swarm optimization displacement back analysis

Received: 11 January 2013

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https://rockmech.whrsm.ac.cn/EN/Y2013/V32/I6/1185

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