Uniaxial compressive strength (UCS) and the static Young’s modulus (E_s) are fundamental parameters for the effective design of engineering structures in a rock mass environment. Determining these two parameters in the laboratory is time-consuming and costly, and the results may be inappropriate if the testing process is not properly executed. Therefore, most researchers prefer alternative methods to estimate these two parameters. This work evaluates the thermal effect on the physical, chemical, and mechanical properties of marble rock, and proposes a prediction model for UCS and E_S using multi-linear regression (MLR), artificial neural networks (ANNs), random forest (RF), and k-nearest neighbor. The temperature (T), P-wave velocity (P_V), porosity (η), density (ρ), and dynamic Young’s modulus (E_d) were taken as input variables for the development of predictive models based on MLR, ANN, RF, and KNN. Moreover, the performance of the developed models was evaluated using the coefficient of determination (R²) and mean square error (MSE). The thermal effect results unveiled that, with increasing temperature, the UCS, E_S, P_V, and density decrease while the porosity increases. Furthermore, ES and UCS prediction models have an R² of 0.81 and 0.90 for MLR, respectively, and 0.85 and 0.95 for ANNs, respectively, while KNN and RF have given the R² value of 0.94 and 0.97 for both E_S and UCS. It is observed from the statistical analysis that P-waves and temperature show a strong correlation under the thermal effect in the prediction model of UCS and E_S. Based on predictive performance, the RF model is proposed as the best model for predicting UCS and E_S under thermal conditions.

Evaluation of fragmentation is an indispensable step in the optimisation of blasting. The various techniques for analysing blast fragmentation have their inherent issues causing various degrees of accuracy. Hence, each method is suitable for some specific purposes. This study evaluates and compares the particle size distribution of FYS granite aggregate quarry using WipFrag digital image processing software and the Kuz-Ram empirical model. Three different blasts of the quarry performed with the same design were analysed using the two methods. Blast design parameters were obtained and evaluated as inputs for the Kuz-Ram model. Muckpile images were captured and uploaded into computer for WipFrag analysis. The results of the fragment size distribution obtained from Kuz-Ram significantly vary with that of the WipFrag but follow the same trend. The three results from WipFrag are in a very close range. The uniformity index of Kuz-Ram was 1.26 while the WipFrag analyses put the same as 1.11, 1.16 and 1.12 for the three blasts, respectively. The Kuz-Ram model only gives idealistic results due to the variation in the structural characteristics of the rocks. However, the model is good for preliminary evaluation of blast design. A new factor was introduced as an indicator of blast fragmentation efficiency.

Purpose. Surface water from mining activities may undergo various physico-chemical changes that can impact its quality and ecological health. This study conducted a comprehensive physico-chemical assessment of surface water affected by mining operations, with a particular emphasis on heavy metal content. Methods. These parameters were chosen due to their importance as indicators of water quality and potential contamination. Water samples were collected from different locations within and around Maiganga Coal mine area and analyzed using standard laboratory techniques. The assessment included the measurement of physico-chemical parameters such as temperature, total dissolved solids and concentrations of heavy metals such as chromium, lead, manganese, cadmium and copper. Also, cations and anions such as calcium, magnesium, sodium, potassium, nitrate, chloride sulfate and fluoride that can impact water quality were considered. Findings. The results of the physico-chemical assessment revealed substantial variations of chromium (0.00-0.03 mg/l), lead (0.00-0.05 mg/l), manganese (0.00-12.11 mg/l), cadmium (0.10-0.14 mg/l) and copper (0.00-1.02 mg/l) concentrations. Also, cations and anions such as calcium (0.00-1.13 mg/l), magnesium (11.90-30.07 mg/l), sodium (0.20-1.11 mg/l), potassium (0.10-0.66 mg/l), nitrate (3.90-4.78 mg/l), chloride (84.0-319.0 mg/l), sulphate (8.0-240.0 mg/l) and fluoride (0.00-0.89 mg/l) can impact water quality levels across the sampled surface water bodies. Originality. Evidence of acid mine drainage, caused by mine effluents that are limited to surface water and do not reach groundwater, has been found through analysis of data from wells, ponds, and streams. Practical implications. These variations obtained could be attributed to the discharge of acidic or alkaline substances associated with coal mining activities.