Rock Mechanics
Aram Ardalanzdeh; Seyed Davoud Mohammadi; Vahab Sarfarazi; Hossein Shahbazi
Abstract
Creating holes in rocks using different methods presents various challenges. In this research, an attempt was made to investigate these characteristics and the existing problems in creating holes based on the texture and brittleness of the rock. For this purpose, several core specimens were taken from ...
Read More
Creating holes in rocks using different methods presents various challenges. In this research, an attempt was made to investigate these characteristics and the existing problems in creating holes based on the texture and brittleness of the rock. For this purpose, several core specimens were taken from the Alvand granitic batholith of Hamadan, and the petrological and textural indexes of the rocks were determined. There are four types of rock textures, ranging from coarse-grained to fine-grained. The texture coefficients (TC) for the four types of rocks (G1 to G4) were 1.709, 1.730, 1.774, and 1.697, respectively. The brittleness index (B1) for the four types of rocks (G1 to G4) were 9.13, 11.01, 12.07, and 10.65, respectively. After that, using a diamond drill, one hole was created in each rock core specimen, and at the end of drilling, a fracture pit was separated from the bottom of each hole in the specimen. The results show that as the mineral size decreases, the fracture pit depth also decreases, and in porphyry texture, the fracture pit depth is between the fracture pit depths of coarse-grained and medium-grained rocks. As the texture coefficient (TC) and brittleness of the rock specimens increase, the fracture pit depth decreases, and in porphyry texture, the fracture pit depth remains between the fracture pit depths of coarse-grained and medium-grained rocks. Finally, the results from laboratory tests indicate that creating holes using a drill to study the effect of the holes on rock behavior can cause damage to the rocks.
Exploitation
Blessing Olamide Taiwo; Oluwaseun Victor Famobuwa; Melodi Mbuyi Mata; Mohammed Sazid; Yewuhalashet Fissha; Victor Afolabi Jebutu; Adams Abiodun Akinlabi; Olaoluwa Bidemi Ogunyemi; Ozigi Abubakar
Abstract
The purpose of this research work is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo State, aggregate quarries. In addition, an Artificial Neural Network (ANN) model for granite profitability was developed. A structured survey questionnaire was ...
Read More
The purpose of this research work is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo State, aggregate quarries. In addition, an Artificial Neural Network (ANN) model for granite profitability was developed. A structured survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. In this study, the efficacy of granite fragmentation was assessed using the WipFrag software. The relationship between particle size distribution, blast design, blast efficiency, and uniformity index were analyzed using the WipFrag result. The optimum blast design was also identified and recommended for mine production. The result revealed that large burden distances result in bigger X50, X80, and Xmax fragmentation sizes. A burden distance of 2 m and a 2 m spacing were identified as the optimum burden and spacing. The finding revealed that blast mean size and 80% passing mesh size have a positive correlation. The result from this study indicated that the uniformity index has a positive correlation with blast efficiency and a negative correlation with maximum blast fragmentation size. The prediction accuracy of the developed models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). The error analysis revealed that the ANN model is suitable for predicting quarry-generated profit.
Exploitation
Babatunde Adebayo; Blessing Olamide Taiwo; BUSUYI THOMAS AFENI; Aderoju Oluwadolapo Raymond; Joshua Oluwaseyi Faluyi
Abstract
The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. ...
Read More
The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. Also, the rate at which drill bit buttons wear out during rock drilling in Ile-Ife, Osogbo, Osun State, and Ibadan, Oyo State, Southwest, Nigeria was investigated. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and adaptive moment Estimation-based Long Short-Term Memory (LSTM) machine learning approaches were used to create models for estimating the bit wear rate based on circularity factor, rock grain size, equivalent quartz content, uniaxial compressive strength, porosity, and abrasive properties of the rock. The performance of the models was measured using a new error estimation index and four other convectional performance estimators. The analysis of performance shows that the adaptive moment estimation algorithm-based LSTM model did better and more accurately than the other models. Thus, the LSTM models presented can be used to improve drilling operations in real-life situations.
kausar Sultan shah; Naeem Abbas; Li Kegang; Mohd Hazizan bin Mohd Hashim; Hafeez Ur Rehman; Khan Gul Jadoon
Abstract
The rocks in the studied area are prone to deterioration and failure due to frequent exposure to extreme temperature variations and loading conditions. In the context of rock engineering reliability assessment, understanding the energy conversion process in rocks is critical. Therefore, this research ...
Read More
The rocks in the studied area are prone to deterioration and failure due to frequent exposure to extreme temperature variations and loading conditions. In the context of rock engineering reliability assessment, understanding the energy conversion process in rocks is critical. Therefore, this research work aims to assess the energy characteristics and failure modes of pink and white-black granite subjected to uniaxial compression loading at various temperatures. Samples of pink and white-black granite are heated to a range of temperatures (0 °C, 200 °C, 400 °C, 600 °C, 900 °C, and 1100 °C), and their failure modes and energy characteristics including total energy, elastic energy, and dissipated energy are studied by testing preheated samples under uniaxial compression. The results show that the dissipation energy coefficient initially rises rapidly, and then falls back to its minimum value at the failure stage. The micro-structures of granite rock directly affect its elastic and dissipation energy. Axial splitting failure mode is observed in most of the damaged granite specimens. After heating granite to 600 °C, the effect of temperature on the failure mode becomes apparent.
