Mohammad Rezaei; Navid Nyazyan
Abstract
Rock drilling is one of the most important processes in the mining operations, which involves high costs. Deep knowledge of the drilling conditions and rock mass properties can help the optimum selection of drilling system, precise determination of type and number of drilling equipment, and accurate ...
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Rock drilling is one of the most important processes in the mining operations, which involves high costs. Deep knowledge of the drilling conditions and rock mass properties can help the optimum selection of drilling system, precise determination of type and number of drilling equipment, and accurate prediction of drilling rate. The above process leads to enhance the drilling efficiency and mining productivity. In this work, relationships between the rock the physico-mechanical properties and horizontal drilling rate (HDR) are investigated. For this purpose, HDR is firstly measured during the drilling process at the Malawi marble quarry mine, Islamabad-e-Gharb, Iran. Then core samples are prepared from the representative minor rock blocks to conduct the laboratory tests and evaluate the influence of rock properties on HDR. The experimental results prove that natural density (ρn), dry density (ρd), slake durability index (Id), Schmidt hammer rebound (SHR), compression wave velocity (Vp), point load index (PLI), uniaxial compressive strength (UCS), and modulus of elasticity (E) have inverse relationships with HDR. Conversely, HDR has a direct relationship with porosity (n), water content (Wa), Los Angeles abrasion (LAA), and Poisson ratio (ν). Generally, it is proved that HDR is more associated with the rock's physical properties than the mechanical characteristics. Moreover, sensitivity analysis confirm that n and ρd are the most and least effective variables on HDR. Furthermore, new optimum empirical equations with acceptable accuracy are proposed to predict HDR based on the statistical modeling. Finally, experimental verification analysis confirm the superiority of this study compared to the prior similar studies.
H. Fattahi
Abstract
The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due ...
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The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due to the difficulties associated with the experimental procedure, it is usually preferred that the σt is evaluated in an indirect way. For these reasons, in this work, the adaptive network-based fuzzy inference system (ANFIS) is used to build a prediction model for the indirect prediction of σt of sandstone rock samples from their physical properties. Two ANFIS models are implemented, i.e. ANFIS-subtractive clustering method (SCM) and ANFIS-fuzzy c-means clustering method (FCM). The ANFIS models are applied to the data available in the open source literature. In these models, the porosity, specific gravity, dry unit weight, and saturated unit weight are utilized as the input parameters, while the measured σt is the output parameter. The performance of the proposed predictive models is examined according to two performance indices, i.e. mean square error (MSE) and coefficient of determination (R2). The results obtained from this work indicate that ANFIS-SCM is a reliable method to predict σt with a high degree of accuracy.
H. Fattahi; N. Babanouri
Abstract
The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, ...
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The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, which influence its mechanical behavior at a fundamental level. In this paper, a new approach combining the support vector regression (SVR) with a cultural algorithm (CA) is presented in order to predict TS of rocks from their physical properties. CA is used to determine the optimal value of the SVR controlling the parameters. A dataset including 29 data points was used in this study, in which 20 data points (70%) were considered for constructing the model and the remaining ones (9 data points) were used to evaluate the degree of accuracy and robustness. The results obtained show that the SVR optimized by the CA model can be successfully used to predict TS.
