Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

作     者：JAVAD Gholamnejad NARGES Tayarani 

作者机构：Department of Mining and Metallurgical EngineeringYazd University Mining & Metallurgical EngineeringYazd University 

出 版 物：《Mining Science and Technology》 (矿业科学技术（英文版）)

年 卷 期：2010年第20卷第5期

页      面：727-733页

核心收录：

学科分类：0808[工学-电气工程] 0709[理学-地质学] 0819[工学-矿业工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081104[工学-模式识别与智能系统] 0818[工学-地质资源与地质工程] 0708[理学-地球物理学] 0807[工学-动力工程及工程热物理] 0815[工学-水利工程] 0805[工学-材料科学与工程（可授工学、理学学位）] 0813[工学-建筑学] 0814[工学-土木工程] 0811[工学-控制科学与工程] 0702[理学-物理学] 

主　　题：人工神经网络 隧道掘进机 神经网络预测 普及率 应用 岩石质量指标 单轴抗压强度 渗透速度 

摘      要：Rate of penetration of a Tunnel Boring Machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN) technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe II hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.