Journal of Geological Society of India (Online archive from Vol 1 to Vol 78)

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Rock Mass Excavatability Estimation using Artificial Neural Network


Affiliations
1 Department of Mining Engineering, Urmia University, Urmia, Iran, Islamic Republic of
2 Department of Mining Engineering, Urmia University of Technology, Urmia, Iran, Islamic Republic of
3 Mechanical Engineering, Urmia University of Technology, Urmia, Iran, Islamic Republic of
     

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One important decision in design of surface mine is the selection of mine equipment and plant. Demand for mechanical excavation is growing in mining industry because of its high productivity and excavation in large scale with lower costs. Several models have been developed over the years to evaluate the ease of excavation and machine performance against rock mass properties. Due to complexity of excavation process and large number of effective parameters, approaches made for this purpose are essentially empirical. There are many uncertainties in results of these models. An attempt is made in this paper to revise the exisiting models. Neural network models for estimation of rock mass excavatability and production rate of VASM-2D excavating machine at Limestone quarry in Retznei, Austria, is presented. Input parameters of this model are Uniaxial compressive strength, tensile strength and discontinuities spacing of rocks. Output is the specific excavation rate per power consumption (bcm/Kwh) as the productivity indicator. Average of deviation between actual data and results estimated by neural network model was only 15% which is in an acceptable range.

Keywords

Rock Mass Excavatability, Artificial Neural Network, Retznei, Austria.
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  • Rock Mass Excavatability Estimation using Artificial Neural Network

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Authors

Sajad Haghir Chehreghani
Department of Mining Engineering, Urmia University, Urmia, Iran, Islamic Republic of
Aref Alipour
Department of Mining Engineering, Urmia University of Technology, Urmia, Iran, Islamic Republic of
Mehdi Eskandarzade
Mechanical Engineering, Urmia University of Technology, Urmia, Iran, Islamic Republic of

Abstract


One important decision in design of surface mine is the selection of mine equipment and plant. Demand for mechanical excavation is growing in mining industry because of its high productivity and excavation in large scale with lower costs. Several models have been developed over the years to evaluate the ease of excavation and machine performance against rock mass properties. Due to complexity of excavation process and large number of effective parameters, approaches made for this purpose are essentially empirical. There are many uncertainties in results of these models. An attempt is made in this paper to revise the exisiting models. Neural network models for estimation of rock mass excavatability and production rate of VASM-2D excavating machine at Limestone quarry in Retznei, Austria, is presented. Input parameters of this model are Uniaxial compressive strength, tensile strength and discontinuities spacing of rocks. Output is the specific excavation rate per power consumption (bcm/Kwh) as the productivity indicator. Average of deviation between actual data and results estimated by neural network model was only 15% which is in an acceptable range.

Keywords


Rock Mass Excavatability, Artificial Neural Network, Retznei, Austria.

References