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The shape of a blasted rock mass, or simply muckpile, affects the efficiency of loading machines. A muckpile is defined with two main parameters known as throw and drop, while several blasting parameters will influence the muckpile shape. This paper studies the prediction of the muckpile shape in open-pit mines by applying an artificial neural network designed by a genetic algorithm. In that regard, a genetic algorithm has been used in preparing the neural network architecture and parameters. Moreover, input variables have been reduced using the principal component analysis. Finally, the best models for predicting throw and drop determined. Analyzing the performance of the proposed models indicates their superiority in predicting the muckpile shape. As a result, the Mean Squared Error of the throw was 0.53 for the training data and 1.24 for the testing data. While for the drop, the errors were 0.45 and 0.58 for the training and testing data, respectively. Furthermore, the sensitivity analysis shows that specific-charge effects drop and throw more. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of jointed rock mass properties on the Minimum Required Caving Span (MRCS) in the block caving method using numeric and heuristic approaches. To do so, the effects of five parameters of jointed rock mass, namely joint set number, joint spacing, joint inclination angle, joint surface friction angle, and undercut depth on MRCS, were investigated using a discrete element code. For this purpose, many numerical models were generated with various rock mass parameters. Moreover, Gene Expression Programming and Artificial Neural Networks were employed to create a heuristic model for MRCS. The model parameters were subjected to sensitivity analysis. All model input parameters showed sensitivity to the model. There are several effective parameters on MRCS, but joint dip and joint set numbers are the most important and the smallest. [ABSTRACT FROM AUTHOR]

Nanotechnology is a new filed in concrete structures which can improve the mechanical properties of them in confronting to impact and blast. However, in this paper, a mathematical model is introduced for the concrete models subjected to impact load for wave propagation analysis. The structure is simulated by the sinusoidal shear deformation theory (SSDT) and the governing equations of the concrete model are derived by energy method and Hamilton's principle. The silicon dioxide (SiO2) nanoparticles are used as reinforcement for the concrete model where the characteristics of the equivalent composite are determined using Mori-Tanaka approach. An exact solution is applied for obtaining the maximum velocity of the model. In order to validate the theoretical results, three square models with different impact point and Geophone situations are tested experimentally. The effect of different parameters such as SiO2 nanoparticles volume percent, situation of the impact, length, width and thickness of the model as well as velocity, diameter and height of impactor are shown on the maximum velocity of the model. Results indicate that the theoretical and experimental dates are in a close agreement with each other. In addition, using from SiO2 nanoparticles leads to increase in the stiffness and consequently maximum velocity of the model. [ABSTRACT FROM AUTHOR]

In order to detennine the rate of penetrability, water pressure test is used before the grouting. One of the parameters which have the highest effect is pressure. Mathematical modeling is used for the first time in this study to detennine the optimum pressure. Thus, the joints that exist in the rock mass are simulated using cylindrical shell model. The joint surroundings are also modeled through Pasternak environment In order to validate the modeling, pressure values obtained by the model were used in the sites of Seymareh and Aghbolagh dams and the relative error rates were measured considering the differences between calculated and actual pressures recorded in these operations. In water pressure test, in Seymareh dam, tire error values were equal to 4.75, 3.93,4.8 percent and in the Aghbolagh dam, were 22.43,5.22,2.6 percent and in grouting operation in Seymareh dam were equal to 9.09,32.50, 21.98,5.57,29.61 percent and in the Aghbolagh dam were 2.96,5.40,4.32 percent. Due to differences in rheological properties of water and grout and based on the overall results, modeling in water pressure test is more accurate than grouting and this error in water pressure test is 7.28 percent and in grouting is 13.92 perce [ABSTRACT FROM AUTHOR]

Grouting is an operation often carried out to consolidate and seal the rock mass in dam sites and tunnels. One of the important parameters in this operation is grouting pressure. In this paper, analytical models used to estimate pressure are investigated. To validate these models, grouting data obtained from Seymareh and Aghbolagh dams were used. Calculations showed that P-3 model from Groundy and P-25 model obtained from the results of grouting in Iran yield the most accurate predictions of the pressure and measurement errors compared to the real values in P-25 model in this dams are 12 and 14.33 Percent and in p-3 model are 12.25 and 16.66 respectively. Also, SPSS software was applied to define the optimum relation for pressure estimation. The results showed a high correlation between the pressure with the depth of the section, the amount of water take, rock quality degree and grout volume, so that the square of the multiple correlation coefficient among the parameters in this dams were 0.932 and 0.864, respectively. This indicates that regression results can be used to predict the amount of pressure. Eventually, the relationship between the parameters was obtained with the correlation coefficient equal to 0.916 based on the data from both dams generally and shows that there is a desirable correlation between the parameters. The outputs of the program led to the multiple linear regression equation of P=0.403 Depth+0.013 RQD+0.011 LU-0.109 V+0.31 that can be used in estimating the pressure. [ABSTRACT FROM AUTHOR]