Rock mass excavatability estimation using artificial neural network |
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| Authors: | Sajad Haghir Chehreghani Aref Alipour Mehdi Eskandarzade |
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| Institution: | (1) Indian School of Mines, Dhanbad, India |
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| 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. |
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