Journal of Engineering Geology

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(Paper pages 513-522) Estimation of engineering properties of rocks and flow rate is an important issue in rock engineering. Properties of discontinuities have considerable effect on rock mass inflow, because they are the main pass of water flow in fracture rock masses. Despite the bulky research about water flow in rock mass, there is no clear evidence as to relationships between all of these parameters and water inflow in rock masses. Neural network systems have a great advantage in dealing with complicated problems such as forecasting, classification and pattern recognition. In this paper, artificial neural network techniques were used in order to forecast Lugeon amount and Hydraulic conductivity behavior of Granodioritic rock mass of Shoor-Jiroft dam site from some characterization of discontinuities such as Rock quality designation, Fracture frequency, Aperture, Weighted joint density, Fracture zone and depth. Relationships between these factors were analyzed with Simple Linear Regression, Multivariate Regression and Stepwise Regress-ion. A Multilayer Perceptron Neural Network (MLPNN) with back propaga-tion procedure was developed for training the network. A Dataset containing 304 values of water pressure test in Granodioritic rock mass of Shoor-Jiroft Dam project was used to train and test the network with the Levenberg-Marquardt training algorithm. The results indicated that neural network forecast hydraulic conductivity considerably better than regression methods do.

Keywords: Multilayer Perceptron Neural Network, Lugeon, discontinuity properties, Multivariate Regression, Shoor-Jiroft Dam Site, Forecasting

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