基于PSO-SVM非线性时序模型的隧洞围岩变形预报

现场量测获得的围岩变形信息，从宏观上反映了地下洞室围岩-支护系统力学性态变化。为克服人工神经元网络方法过学习问题，提出了一种新的预测地下洞室围岩变形的粒子群支持向量机方法，用粒子群算法优化最小二乘支持向量机的参数，避免了人为选择参数的盲目性，提高了预测模型的训练速度和预测推广能力。利用这种非线性智能预测方法，基于监测数据滚动预测围岩变形，可以及时优化和调整施工步序，保证洞室的稳定性。将该方法用于清江水布垭电站地下厂房的围岩收敛变形预测，获得了令人满意的预测效果。

Forecasting nonlinear time series of surrounding rock deformations of underground cavern based on PSO-SVM

The in-situ monitoring data of the surrounding rock displacements reflect the changing of mechanical situation of surrounding rock-supporting system. To overcome the excessive learning problem of ANN, a new method of PSO LS_SVM to forecast the nonlinear displacements of surrounding rock in underground engineering is presented based on monitoring data, Particle swarm optimization is used to choose the parameters of support vector machine, which can avoid the man-made blindness and enhance the efficiency and capability of forecasting. The method can rolling forecast the surrounding rock deformations based on monitoring data, in order to adjust the supporting schemes dynamically and ensure the stability of the cavern, It is used to forecast the surrounding rock convergent deformations of Qingjiang Shuibuya Underground Powerhouse; and it is shown that the method is feasible and precise.