This study investigates the effect of the partial replacement of ordinary Portland cement (OPC) with cement kiln dust (CKD) on the engineering properties of pozzolanic concrete for use in block manufacturing. Ultimately, this could potentially reduce cement consumption and CO₂ emissions. The study was carried out on cylindrical concrete samples prepared from five mixtures comprising 71.13% pozzolan and 14.16% water, with 0, 5, 10, 15, or 20% of the OPC fraction (14.71 wt.%) replaced by CKD. The samples were tested for density and compressive strength (UCS) at curing ages of 7, 14, 28, 56, and 91 days; and for voids and water absorption after 28 curing days. Results show that increasing CKD content lowered the voids and increased the water absorption. The 15% CKD sample had the highest UCS, regardless of the curing age. X-ray diffraction analysis confirmed that the phase responsible for hydration, calcium silicate hydrate, was higher in the 15% CKD sample than the control.

The accurate characterization and mapping of low-grade ore deposits necessitate the utilization of a robust exploration technique. Induced polarization (IP) tomography is a powerful geophysical method for mineral exploration. An integrated survey using electrical resistivity tomography (ERT) and IP was employed in this study to characterize and map (Zn-Pb-Ag) ore deposits in NE New Brunswick, Canada. The survey encompassed twelve parallel lines across the study area. The 2D and 3D inversion of the results provided a detailed image of the resistivity and chargeability ranges of subsurface formations. The boundaries of sulfide mineralization were determined based on resistivity values of (700–2000 Ohm.m) and chargeability values of (3.5 mV/V) and were found to be located at an approximate depth of 80–150 m from the surface. The findings were validated through a comparison with data from borehole logs and mineralogy data analysis. The size and shape of sulfide deposits were successfully characterized and mapped in the study area using this cost-effective mapping approach.

Analysis of the electromechanical-size-dependent bending of piezoelectric composite structural components with flexoelectricity has been considered by many researchers because of the developments of nanotechnology and the applicability of piezoelectric composite nanobeam structures in Micro/Nano-Electro-Mechanical Systems (MEMS/NEMS). Therefore, the work investigates the size-dependent electromechanical bending of piezoelectrically layered perforated nanobeams resting on elastic foundations including the flexoelectric effect. Within the framework of the modified nonlocal strain gradient elasticity theory, both the microstructure and nonlocality effects are captured. The governing equilibrium equations including piezoelectric and flexoelectric effects are derived using Hamilton’s principle. Closed forms for the non-classical electromechanical bending profiles are derived. The accuracy of the proposed methodology is verified by comparing the obtained results with the available corresponding results in the literature within a 0.3% maximum deviation. Parametric studies are conducted to explore effects of perforation parameters, elastic foundation parameters, geometric dimensions, nonclassical parameters, flexoelectric parameters, as well as the piezoelectric parameters on the bending behavior of piezoelectrically layered perforated nanobeams. The obtained results demonstrate that incorporation of the nondimensional elastic foundation parameters, K_p = 2 and K_w = 20, results in a reduction in the relative percentage reduction in the maximum nondimensional mechanical transverse deflection due to increasing the perforation filling ratio from 0.2 to 1 from 199.86% to 91.83% for a point load and 89.39% for a uniformly distributed load. On the other hand, with K_p = 5 and K_w = 50, the relative percentage difference of the electromechanical bending deflection due to increasing the piezoelectric coefficient, e₃₁₁, reaches about 8.7% for a point load and 8.5% for a uniformly distributed load at a beam aspect ratio of 50. Thus, the electromechanical as well as mechanical behaviors could be improved by controlling these parameters. The proposed methodology and the obtained results are supportive in many industrial and engineering applications, i.e., MEMS/NEMS.

Understanding the rheology behavior of non-Newtonian mine paste backfill is critical to ensure its flowability in the pipeline distribution system. Several rheology measurements methods for paste backfill have been proposed in the literature to quantify the rheology properties. However, there is no definite conclusion on the best measurement method to correctly predict pressure loss in the paste backfill pipeline system with a high solid concentration. This study addresses the issue by comparing several rheology measurements techniques, i.e., coaxial rheometer with various configurations, vane viscometer, and slump and pipe loop tests, with regard to Bingham yield stress, Bingham plastic viscosity, and pressure loss using statistical analysis. The paste backfill samples are prepared from two different mine tailings: finer and coarser particles. The pressure loss from the pipe loop test along with Buckingham-Reiner transformation using the Darcy-Weisbach equation are used as reference properties. The results suggest that a simple slump test can accurately predict the Bingham yield stress for coarser tailing. At the same time, a coaxial rheometer with MVDIN cup is found to predict the Bingham paste properties accurately for finer tailing.

This study presents an integrated approach combining the Hardy Cross method and a gradient boosting (GB) optimization model to enhance ventilation systems in underground mines, with a specific application at the Jabal Sayid mine in Saudi Arabia. The Hardy Cross method addresses variations in airflow resistance caused by obstacles within ventilation pathways, enabling accurate predictions of the flow distribution across the network. The GB model complements this by optimizing fan placement, pressure control, and airflow intensity to achieve reduced energy consumption and improved efficiency. The results demonstrate significant improvements in fan efficiency, optimized energy usage, and enhanced ventilation effectiveness, achieving a 31.24% reduction in electricity consumption. This study bridges deterministic and machine learning methodologies, offering a novel framework for the real-time optimization of underground mine ventilation systems. By combining the Hardy Cross method with GB, the proposed approach outperforms traditional techniques in predicting and optimizing airflow distribution under dynamic conditions.