Deformation Prediction for a Tunnel Rock Mass Based on the Multi-Scale Combination Kernel Extreme Learning Machine Model
(1 School of Civil Engineering, Chongqing Three Gorges University, Chongqing 404100; 2 Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430010; 3 College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059; 4 Department of Building and Environmental Safety, Chongqing Vocational Institute of Safety & Technology, Chongqing 404100)
Abstract：
Tunnel rock deformation sequences are nonlinear and it is therefore difficult to obtain satisfactory preci? sion in predictions by conventional methods. To improve the prediction accuracy of tunnel surrounding rock deformation, a model of a multi-scale extreme learning machine with a combination kernel is proposed based on measured deformation data. The measured deformation data is divided into different scale sequences using the empirical mode decomposition technique, a sequence of each component is predicted by the combination of the extreme learning machine and the final forecast value is obtained by combining the results of each component. For the improved model, a compound kernel parameter is obtained by the linear weighting of the radial basis function and polynomial kernel function, kernel parameters and weighting coefficients are optimized by particle swarm optimization,the prediction results of the model are discussed using Markov Chain and the prediction accuracy for tunnel surrounding rock deformation is improved. The predicted surrounding rock deformation of the Daxiangling tunnel shows that higher accuracy can be achieved in both the one-step prediction and multi-step prediction of tunnel surrounding rock deformation using the improved model, the proposed model is better than the Bayesian regularization BP neural network and the deformation is acceptable compared to the measured deformation.
.Deformation Prediction for a Tunnel Rock Mass Based on the Multi-Scale Combination Kernel Extreme Learning Machine Model[J] MODERN TUNNELLING TECHNOLOGY, 2017,V54(6): 70-76
2017, Vol. 54 