摘要
Abstract
The prediction of surrounding rock deformation is an important basis for the safety evaluation of the tunnel and the construction of the later stage.In order to improve the precision of the deformation prediction,by combining with the engineering practice,the idea of PSO-SVM-BP prediction model is put forward.First of all,the deformation data are pre processed by three spline interpolation and smoothing method for two times,laying the foundation for the late deformation prediction;secondly,to optimize the parameters of support vector machine based on particle swarm algorithm,then PSO-SVM model is established,and the surrounding rock deformation is predicted preliminarily;at last,a BP neural network for error correction is used to achieve comprehensive forecasting purposes,and engineering examples are used for the test to verify the effectiveness of the prediction model.The results show that the relative error of preliminarily prediction results is all less than 5％,and the prediction accuracy after error correction increases to 0.97％,showing higher prediction accuracy,which proves the validity of the forecast model.The prediction model is feasible,and can provide a reference for similar research.关键词
隧道工程/粒子群算法/支持向量机/BP神经网络/动态预测Key words
tunnel engineering/particle swarm algorithm/support vector machine/BP neural network/dynamic prediction分类
交通工程
