@ARTICLE{Tayarani_Narges_Saadat_Combination_2020,
 author={Tayarani, Narges Saadat and Jamali, Saeed and Zadeh, Mehdi Motevalli},
 volume={vol. 65},
 number={No 2},
 journal={Archives of Mining Sciences},
 pages={337-346},
 howpublished={online},
 year={2020},
 publisher={Committee of Mining PAS},
 abstract={The deformation modulus of the rock mass as a very important parameter in rock mechanic projects generally is determined by the plate load in-situ tests. While this test is very expensive and time-consuming, so in this study a new method is developed to combin artificial neural networks and numerical modeling for predicting deformation modulus of rock masses. For this aim, firstly, the plate load test was simulated using a Finite Difference numerical model that was verified with actual results of the plate load test in Pirtaghi dam galleries in Iran. Secondly, an artificial neural network is trained with a set of data resulted from numerical simulations to estimate the deformation modulus of the rock mass. The results showed that an ANN with five neurons in the input layer, three hidden layers with 4, 3 and 2 neurons, and one neuron in the output layer had the best accuracy for predicting the deformation modulus of the rock mass.},
 type={Article},
 title={Combination of Artificial Neural Networks and Numerical Modeling for Predicting Deformation Modulus of Rock Masses},
 URL={http://journals.pan.pl/Content/116363/PDF/Archiwum-65-2-10-Jamali.pdf},
 doi={10.24425/ams.2020.133196},
 keywords={Artificial Neural Networks, numerical simulation, Finite Difference Method, Deformation Modulus of Rock Mass, Arch Dam},
}