نشریه روش های تحلیلی و عددی مهندسی معدن
پیاپی 11 (بهار و تابستان 1395)

Western and North-Western regions of Iran feature very cold winters, a lot of snow, and freezing temperatures during most nights in December, January, February, and March. This directly influences the selection and applications of dimension stones in these areas. Freezing influences both mechanical and physical properties of rocks. Therefore, measuring the changes in values of these parameters before and after freezing can be used to study the effects of freezing on rocks. The main objective of this study is to investigate the effect of freezing on the strength and durability of dimension stones. In this research, fourteen types of frequently utilized stones in North-Western parts of Iran were studied. Five freezing and thawing cycles were done on prepared cores. The results of statistical analysis showed that the uniaxial compressive strength and durability of stones respectively reduced by 7.99% and 1.07% after freezing. The uniaxial compressive strength reduced by 3.03% and durability by 0.6% in the case of the best stone. Using the fuzzy clustering technique, all rocks were classified in two separate clusters according to their properties and the reduction rate of uniaxial compressive strength and durability before and after freezing.

Estimation of roadheader performance is one of the main topics in determining the economics of underground excavation projects. The poor performance estimation of roadheader scan leads to costly contractual claims. In this paper, the application of soft computing methods for data analysis called adaptive neuro-fuzzy inference system- subtractive clustering method (ANFIS-SCM) and artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA) to estimate roadheader performance is demonstrated. The data to show the applicability of these methods were collected from tunnels for Istanbul’s sewage system, Turkey. Two estimation models based on ANFIS-SCM and ANN-HPSOGA were developed. In these models, Schmidt hammer rebound values and rock quality designation (RQD) were utilized as the input parameters, and net cutting rates constituted the output parameter. Various statistical performance indices were used to compare the performance of those estimation models. The results indicated that the ANFIS-SCM model has strong potentials to estimate roadheader performance with high degrees of accuracy and robustness.

A fourth order formulation of the displacement discontinuity method (DDM) is proposed for the crack analysis of brittle solids such as rocks, glasses, concretes and ceramics. A fourth order boundary collocation scheme is used for the discretization of each boundary element (the source element). In this approach, the source boundary element is divided into five sub-elements each recognized by a central node where the displacement discontinuity components are to be numerically evaluated. Three different formulating procedures are presented and their corresponding discretization schemes are discussed. A new discretization scheme is also proposed to use the fourth order formulation for the special crack tip elements which may be used to increase the accuracy of the stress and displacement fields near the crack ends. Therefore, these new crack tips discretizing schemes are also improved by using the proposed fourth order displacement discontinuity formulation and the corresponding shape functions for a bunch of five special crack tip elements. Some example problems in brittle fracture mechanics are solved for estimating the Mode I and Mode II stress intensity factors near the crack ends. These semi-analytical results are compared to those cited in the fracture mechanics literature whereby the high accuracy of the fourth order DDM formulation is demonstrated.

Some experimental relations have been developed for determining the grout curtain depth, but these relations cannot be applied to any dam with any geological condition. Therefore, the effect of the grout curtain depth on seepage through foundation and abutments of each dam should be studied separately. To examine this parameter in Karun 4 dam, the numerical modeling method was applied using FLAC in a 2-dimensional modeling process. Permeability coefficient is an important parameter in numerical modeling. In this research, the experimental relationships for Lugeon values and permeability coefficient were used to determine the required permeability. A transverse section was used to model each of the abutments and the foundation of Karun 4 dam. The grout curtain was designed as a wall with a very low permeability at different depths. After modeling the grout curtain with various lengths, the curtain depth was determined based on seepage level, curtain flow efficiency, drilling-injection costs, and value of seepage water. The grout curtain depths were calculated to be 70, 184, and 104 at the foundation, left abutment, and right abutment, respectively. Also analysis of the pore water pressure and flow lines shows when the curtain is sewed to the Pabdeh Formation, we have the high efficiency in the grout curtain.

Rock formations and structures can be subjected to both static and dynamic loadings. Static loadings resulting from different sources such as gravity and tectonic forces and dynamic forces are intermittently transmitted via vibrations of the earth’s crust, through major earthquakes, rock bursts, rock blasting and drilling and also, traffic. Reaction of rocks to cyclic and repetitive stresses resulting from dynamic loads has been generally neglected with the exception of a few rather limited studies. In this study, , two crystalline quarry stones in Iran; (Natanz gabbro and Green onyx) and one non-crystalline rock (Asmari limestone) are used to evaluate the effect of micro-structure of intact rock on fatigue behavior. These rocks have different mineral compositions and formation conditions. A new apparatus based on rotating beam fatigue testing machine (R.R.Moore), which is commonly used for laboratory fatigue test in metals, is developed and fatigue behavior and existence of the endurance limit were evaluated for the mentioned rocks based on stress-life method. The obtained results in the variation of applied amplitude stress versus loading cycle number (S-N diagram) followed common relationship in other materials. In addition, the endurance limit is perceived for all tested rocks. The results also illustrated that the endurance limits for all types of tested rocks in this study are ranged between 0.4 and 0.6 of their tensile strengths. The endurance limit to tensile strength fraction of green onyx and Natanz gabbro were approximated in a higher value compared to the Asmari limestone with non-crystalline micro-structure.

During wars and crises, the underground tunnels are used as a safe space. Therefore, the stability and safety of them under a blast is of particular importance. In this paper, the Finite Difference Method has been used to study the influence of the change in geotechnical parameters and depth on surface blasting on subway tunnels. Results showed that increasing the internal friction angle, modulus of elasticity and cohesion of the soil reduced the effects of blast loads on the vertical displacement and bending moment in the center of tunnel crown. Furthermore, the results showed that increasing the depth of the tunnel reduced the effects of blast loading. Comparing all parameters collectively showed that the increase in the modulus of elasticity of the soil and depth of the tunnel is the most effective in reducing the influence of the blast loads on the vertical displacement and bending moment of the tunnel crown, respectively.

Geo-statistical methods for reserve estimation are difficult to use when stationary conditions are not satisfied. Artificial Neural Networks (ANNs) provide an alternative to geo-statistical techniques while considerably reducing the processing time required for development and application. In this paper the ANNs was applied to the Choghart iron ore deposit in Yazd province of Iran. Initially, an optimum Multi Layer Perceptron (MLP) was constructed to estimate the Fe grade within orebody using the whole ore data of the deposit. Sensitivity analysis was applied for a number of hidden layers and neurons, different types of activation functions and learning rules. Optimal architectures for iron grade estimation were 3-20-10-1. In order to improve the network performance, the deposit was divided into four homogenous zones. Subsequently, all sensitivity analyses were carried out on each zone. Finally, a different optimum network was trained and Fe was estimated separately for each zone. Comparison of correlation coefficient (R) and least mean squared error (MSE) showed that the ANNs performed on four homogenous zones were far better than the nets applied to the overall ore body. Therefore, these optimized neural networks were used to estimate the distribution of iron grades and the iron resource in Choghart deposit. As a result of applying ANNs, the tonnage of ore for Choghart deposit is approximately estimated at 135.8 million tones with average grade of Fe at 56.14 percent. Results of reserve estimation using ANNs showed a good agreement with the geo-statistical methods applied to this ore body in another work.