Combination of artificial neural networks and numerical modeling for predicting deformation modulus of rock masses

• Autorzy: Tayarani Narges Saadat , Jamali Saeed , Zadeh Mehdi Motevalli

Identyfikatory

DOI

10.24425/ams.2020.133196

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

artificial neural networks; numerical simulation; finite difference method; deformation modulus of rock mass; arch dam

PL

sztuczne sieci neuronowe; symulacja numeryczna; metoda różnic skończonych; masy skalne

• Wydawca: Instytut Mechaniki Górotworu PAN
• Czasopismo: Archives of Mining Sciences
• Rocznik: 2020
• Tom: Vol. 65, no. 2
• Strony: 337--346
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr.

Twórcy

autor

Tayarani Narges Saadat

• Toossab Consulting Engineers Company, Mashhad, Iran

autor

Jamali Saeed

• Isfahan University of Technology (IUT), Department of Mining Engineering, Isfahan- 84156-83111, Iran

autor

Zadeh Mehdi Motevalli

• Toossab Consulting Engineers Company, Mashhad, Iran

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)