Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

Strategic Study of CAE >> 2005, Volume 7, Issue 10

A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks

1. Management School, Tianjin University, Tianjin 300072, China

2. School of Civil Engineering, Tianjin University, Tianjin 300072, China

Funding project:国家自然科学基金资助项目(50479048) Received: 2004-11-24 Revised: 2004-12-23 Available online: 2005-10-20

Next Previous

Abstract

Owing to the difficulty of traditional multi-variable regression methods to represent the surrounding rock deformation curve with inflexion points, a method for forecasting tunnel surrounding rock deformation using radial basis function neural networks is presented. This method not only can be utilized to approximate the complex deformation curves, but also has higher convergence speed and better globally-searching ability than those using BP neural networks. An example is given to show the effectiveness and practicability of this method.

Figures

图1

图2

图3

图4

图5

References

[ 1 ] JJHopfield.Neuralnetworksandphysicalsystem withemergentcollectivecomputationabilities[A].In:ProcNatlAcadSci[C], Vol79, 1982.2554~2558

[ 2 ] 周建春, 魏 琴.公路隧道围岩变形时程曲线拟合的BP算法[J].湖南大学学报, 2002, 29 (4) :79~84 link1

[ 3 ] 马万权, 王新平, 程崇国.神经网络技术在阳宗隧道围岩变形预测中的应用[J].公路交通技术, 2003, (2) ;56~59 link1

[ 4 ] MoodyJ, DarkenC.Fastlearninginnetworksof locallytunedprocessing[J].NeuralComputation, 1989, (1) :281~289

[ 5 ] ChenS, CowanCFN, GrantPM.Orthogonalleast squareslearningalgorithmforradialbasisfunction networks[J].IEEETransactionsonNeural Networks, 1991, 2 (2) :302~309

[ 6 ] ChenT, ChenH.Approximationcapabilityto functionsofseveralvariables, nonlinearfunctionsand operatorbyradialbasisfunctionneuralnetwork[J].IEEETransonNeuralNetworks, l995, 5 (6) :904~910

Related Research