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Predicting the vibration of the circular sawing machine is very important in examining the performance of the sawing process, as it shows the amount of energy consumption of the circular sawing machine. Also, this factor is directly related to maintenance cost, such that with a small increase in the level of vibration, the maintenance cost increases to a large extent. This paper presents new prediction models to assess the vibration of circular sawing machine. An evaluation model based on the imperialist competitive algorithm as one of the most efficient artificial intelligence techniques was used for estimation of sawability of the dimension stone in carbonate rocks. For this purpose, four main physical and mechanical properties of rock including Schimazek’s F-abrasivity, uniaxial compressive strength, mean Mohs hardness, and Young’s modulus as well as two operational parameters of circular sawing machine including depth of cut and feed rate, were investigated and measured. In the predicted model, the system vibration in stone sawing was considered as a dependent variable. The results showed that the system vibration can be investigated using the newly developed machine learning models. It is very suitable to assess the system vibration based on the mechanical properties of rock and operational properties.

In order to obtain minimum amount of ore wastage and maximum profitability in dimension stone quarries an optimal short-term production planning procedure has been established here according to the market demand. As the base of this procedure geological modelling has been first created and smoothing of ore-body boundaries was done. Then, an economical block model has been provided. In the next step, all probable shapes of minable-blocks were specified and their priorities in assignment were done according to desired size of the blocks by market demand. Finally, searching from a base sub-block was started in order to find the optimal plan. The procedure has been used for a dimension marble quarry located in northwest of Iran. It demonstrates a decrease about 26% of ore wastage and 21.3% of diamond wire assumption with comparing to the existent traditional procedure.

Ground vibration due to blasting causes damages in the existence of the surface structures nearby the mine. The study of vibration control plays an important role in minimizing environmental effects of blasting in mines. Ground vibration regulations primarily rely on the peak particle velocity (PPV, mm/s). Prediction of maximum charge weight per delay (Q, kg) by distance from blasting face up to vibration monitoring point as well as allowable PPV was proposed in order to perform under control blasting and therefore avoiding damages on structures nearby the mine. Various empirical predictor equations have proposed to determine the PPV and maximum charge per delay. Maximum charge per delay is calculated by using PPV predictors indirectly or Q predictor directly. This paper presents the results of ground vibration measurement induced by bench blasting in Sungun copper mine in Iran. The scope of this study is to evaluate the capability of two different methods in order to predict maximum charge per delay. A comparison between two ways of investigations including empirical equations and artificial neural network (ANN) are presented. It has been shown that the applicability of ANN method is more promising than any under study empirical equations.

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