چکیده مقاله
The prediction of performance of tunnel boring machines TMB penetration rate is important for project planning and selection of economic tunneling methods This paper presents an attempt to predict penetration rate of TBM with a generalized regression neural network The Queens Water tunnel data have been used to develop this network which includes three layers input, hidden and output layers The compressive trength, peak slope index, distance between planes of weakness and orientation of discontinuities in rock mass are chosen as input data penetration rata of TBM as output data The results show that develop network is capable of predicting TBM penetration rata with correlation coefficient of 0 911 It was concluded that the penetration rata can be reliably estimated using the generalized neural network
کلیدواژهها
نویسندگان
REZA MIKAEIL
Faculty of mining , Petroleum & Geophysics, Shahrood University of tech, Daneshgah Blvd, shahrood, Iran
Omid Frough
Faculty of mining , Petroleum & Geophysics, Shahrood University of tech, Daneshgah Blvd, shahrood, Iran
Reza Khalokakaie
Faculty of mining , Petroleum & Geophysics, Shahrood University of tech, Daneshgah Blvd, shahrood, Iran
محمد عطائی
Faculty of mining , Petroleum & Geophysics, Shahrood University of tech, Daneshgah Blvd, shahrood, Iran
شیوه ارجاع
MIKAEIL, REZA and Frough, Omid and Khalokakaie, Reza and Ataei, Mohammad,1388,Prediction of TBM Penetration Rate with Generalized Regression Neural Network in Hard Rock Condition,8th International Congress on Civil Engineering,Shiraz
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