Shaib Abdulazeez Shehu

Mining and geotechnical engineering depend on the understanding of rock strength since it influences the excavation design, stability evaluation, and reserve extraction productivity. This review summarizes advances in predictive modeling of rock strength using microstructural properties, with emphasis on machine learning approaches such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and hybrid optimization algorithms. It highlights key influencing factors (grain size, porosity) and discusses modeling challenges and future directions. The latest developments have improved rock strength prediction models in machine learning and microstructural research. The combination of microstructural features with data-driven approaches is the main emphasis of this paper's overview of current techniques and technology utilized in the predictive modelling of rock strength. Microstructural characteristics of rock have a great and direct influence on predicting the mechanical properties and failure mechanism under loading. This paper reviewed the current model techniques that have been adopted, future directions, and the major problems. However, the authors strongly advised that the statistical methods of regression analysis should be encouraged to save time and cost.

This study aims to assess the potential of Obajana marble as a raw material in the production of cement and other industrial applications by evaluating its mineralogical and chemical composition. Five samples from Obajana were analyzed using energy dispersive XENEMETRIX XRF and Rigaku Mininflex XRD to ascertain its mineralogical and chemical composition respectively. The tests explored the applicability of marble as a replacement for limestone in clinker formation which reduces the environmental impact of cement production while maintaining comparable chemical and mechanical properties; the incorporation of marble powder as a Supplementary Cementitious Materials (SCM) and utilization in metal production have enhanced workability, durability, and strength, contributing to reduced clinker consumption and carbon emissions. The presence of calcium carbonate (CaCO3: 91.99 %), calcium oxide (CaO: 92.42%), silica (SiO₂: 1.38–5.65 %), alumina (Al₂O₃: 1.50–3.82%), and iron oxide (Fe₂O₃: 0.29%) in Obajana marble supports its role in clinker formation by promoting cementitious compound development and function as a fluxing agent in pyrometallurgical processes in metal smelting. The high CaCO₃ and minor SiO2, and Al₂O₃ content, enables Obajana marble serve as a cost-effective filler, improving packing density, microstructure, and mechanical properties of concrete. The findings underscore the potential suitability of Obajana marble in promoting resource efficiency, circular economy principles, and sustainable building materials. With a high CaO content of 92.93% and minimal impurities such as SiO₂, Al₂O₃, and Fe₂O₃, the marble meets key chemical criteria for use in cement and lime production. Its low MgO and Fe₂O₃ levels further suggest suitability for white cement and high-grade construction materials. The trace elements present are within acceptable industrial limits, reinforcing its potential for eco-friendly and versatile applications. This research provides valuable insights into the role of Obajana marble in advancing sustainable construction practices and reducing dependency on imported raw materials by integrating detailed geochemical characterization of Obajana marble with its industrial suitability for cement and construction material production and application.

Coal is a combustible material consisting mainly of macerals and minor minerals. Macerals are the microscopic organic entities of coal while the inorganic components are the minerals. Maceral composition is vital for assessing the quality of coal and predicting its behaviour in metallurgical, combustion and other processes. It also helps to understand the geological history of coal deposits. Geological mapping of Ibobo coal deposit in the Anambra Basin of Nigeria was conducted and coal samples evaluated for maceral characteristics and vitrinite reflectance to determine the suitability of the coal for metallurgical processes and electricity generation. Petrographic analysis reveals the presence of the three maceral groups; vitrinite, liptinite and inertinite in all the samples. The proportion of the vitrinites and inertinites are higher than the liptinites in the samples. On mineral matter-free basis, the coal, on average, contains 60.80% vitrinite, 12.70% liptinite and 26.50% inertinite. With mineral matter counted, on average, it consists of 51.70% vitrinite, 11.40% liptinite, 23.10% inertinite and 13.80% mineral matter. The coal, on average, also contains 63.10% reactives (vitrinite + liptinite) and 36.90% inerts (inertinite + mineral matter). It has mean vitrinite reflectance measurement of 0.42 RoMax on average, which classifies the coal as sub-bituminous. These petrographic characteristics suggest that the coal does not possess coking qualities suitable for coke making for metallurgical processes thereby placing it on low quality and non-coking. However, it is good for electric power generation and as a domestic fuel.

Coal is a combustible material consisting mainly of organic components (macerals) and minor inorganic components (minerals). The potential relevance of coal in metallurgical, power generation, and other industries depends on these components. Coal is considered one of the cheapest and most reliable energy sources responsible for electricity production worldwide. Hence, in sourcing coking coals for metallurgical industries and thermal coals for power generation in the country, coal samples from the Ikpabacha coal deposit in the northern Anambra Basin of Nigeria have been subjected to proximate analysis, ultimate analysis, calorific value determination, and free swelling index test primarily to determine the suitability of the coal for metallurgical processes and power generation. Results from the proximate analysis indicated that the average composition of the coal includes 18.70% moisture, 3.36% ash, 34.67% volatile matter, and 43.27% fixed carbon. The ultimate analysis also revealed that coal, on average, consists of 47.62% carbon, 3.47% hydrogen, 1.30% nitrogen, 13.43% oxygen, 1.06% sulphur, and 0.002% phosphorus. The coal has, on average, H/C ratio of 0.07 and O/C ratio of 0.28. The average heating (calorific) value of the coal samples is 8185 Btu/Ib (4547.59 kcal/kg), and it has a free swelling index (FSI) of zero (0). These characteristics suggest that coal is non-coking and cannot be used in the metallurgical industry to manufacture iron and steel. However, coal is appropriate for electricity generation, boilers, and ovens in industrial heating.

Blasting is the predominant technique employed for various applications and purposes for hard rock excavation. Blast energy from explosives cannot be fully utilized for rock fragmentation. The unused energy generated has adverse environmental impacts that may lead to human discomfort and damage to structures. Such unused or surplus blast energy may cause ground vibration, flyrock and airblast. Effective blasting plans should anticipate and reduce potential adverse impacts by estimating the expected vibration level. Factors like the geological condition of the site, the type and amount of explosives used, and the blast pattern play a significant role in determining these harmful effects. This study aims to establish the specific site constants (k, β) for Lafarge Quarry in Kanthan to predict blast-induced vibration and safe distance. The study estimates the site constants and applies them to assess vibration levels for a given distance and maximum instantaneous charge (Q). Ten different and independent blast events were investigated, with each blast's maximum charge per delay evaluated. Peak Particle Velocity (PPV) readings were taken using an Instatel MiniMate Plus, located 600 meters from the blasting area, and the scaled distance (SD) was calculated for each event. Regression analysis evaluated the site-specific constants (k and β) as -1.66 and 2,262, respectively, demonstrating that fixed numerical constants cannot universally predict blast-induced vibration due to varying global geological conditions. A predictive model was developed for the studied quarry based on these constants, and the model provides vibration predictions that are more accurate and closer to the actual measurements than those produced by the Australian Standards. This work confirms the previous works that blast constants are site-specific and depend on the geological conditions of the studied area..

Purpose. To develop comprehensive models for predicting the pH and electrical conductivity of surface water in Maiganga coal mine and environs affected by mining activities. Methodology. The research utilizes a combination of in-situ measurement, laboratory analysis, modeling technique using Ansys Workbench and Linear Regression for predicting the content of pollutants. In-situ measurement/data collection in the upstream and downstream were carried out to evaluate the potential impact of mining activities on surface and ground water quality. Electrical conductivity and pH were measured on the samples that were collected using Oakton 5/6 pH meter and TDS/EC meter. Findings. According to the results, the regression statistics model of pH and electrical conductivity (EC) shows that the predicted values have a pH range of 4.7–7.05 and a mean pH value of 5.5. In contrast, while the EC ranges from 454.52 to 2,720.68 s/cm (EC) with a mean value of 905 µs/cm of the downstream flow which is completely dependent on the mine inlet (pH-in and EC-in). The findings show a direct correlation between surface water pH, electrical conductivity, and mining activities in the Maiganga coal mine area and their detrimental effects on the ecosystem and water quality. Originality. The results were obtained directly from the mine site during field visit and can be compared to data from active coal mine sites. Practical value. The detrimental effect of the results of mining activities can be controlled if monitoring sensors are introduced at mines’ effluent outlet to alert the mine management of possible danger in real time.

Remote sensing is an essential tool for gathering vital information for the mining industry. With the latest advances in the field of remote sensing and the invention of software such as GIS (Geographic Information Systems), it is now the task of researchers to come up with different approaches of finding minerals that can reduce the cost of exploration. This research applied remote sensing techniques to Landsat 8 satellite imagery to extract geological information in southern Kelantan, Malaysia. The results produced images such as single band combination (RGB 6,5,2 and 5,4,2), band ratio (RGB 4/2, 6/7, 6/5) and principal component analysis (RBG from PC 123). The spatial survey revealed that the NE-SW line patterns and igneous rock distributions controlled the occurrence of the gold deposits. The generated map highlighted 2.40% of the total area as high potential zones recommended for further exploration.