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Modeling of Geomechanical Processes from Open Pit to Underground Mining with Complex Morphology
No full textThe relevance of this study is the need to optimize the transition from open-pit to underground mining in mines with complex deposit morphologies, such as the Akzhal Mine. This is essential to ensure the safety of mining operations and to prevent adverse manifestations of rock pressure and mass cave-ins when changing the type of mining. This study aimed to develop a geomechanical basis for selecting an optimal mining system for the transition from open-pit to underground mining. Particular attention is paid to rock mass stability and its behavior during mining operations, which makes it possible to optimize the parameters of the mining system by considering the characteristics of a mine with a complex deposit morphology. This study used methods to assess the strength of the rock mass, including the concept of the geological structure of the natural environment, the methodology of determining the structural weakening coefficient, and the determination of the rock mass deformation modulus using the fracturing ratio and stability of the rock mass coefficient with an analytical functional relationship of geo-structural factors. The study results made it possible to systematize the rock mass by stability categories and proposed recommendations for the safe operation of deposits during the transition to underground mining, on the choice of mining system, and on the design of its elements. The novelty of this study lies in an integrated approach for predicting the behavior of rock mass and selecting the optimal mining system, which makes it possible to improve the safety and efficiency of production under difficult geological conditions
Shear Strength of One-Way Slabs Subjected to Concentrated Loads
No full textReinforced concrete (RC) one-way slabs without transverse reinforcement are found extensively in bridge constructions. In the presence of concentrated loads (CLs) close to the supports, the shear strength (SS) of such slabs is usually determined using design expressions provided by the codes of practice that were derived originally from tests performed on beams or one-way slabs that were loaded along their entire width, which are inconsistent with the actual shear failure mechanism of one-way slabs under CLs. This paper presents an empirical SS model developed using the gene expression programming method (GEP), where the SS is related to six influencing parameters. The proposed model is derived employing the test results of 238 RC one-way slabs that experienced shear failure from the literature. The accuracy of the proposed model is measured using several statistical indices and compared with the existing shear design methods. The GEP model agreed favorably with the test results. The GEP model was also employed to conduct a parametric study for further validation and sensitivity analysis to define the contribution of input parameters to the SS. The parametric study demonstrated the efficiency of the GEP model in replicating the physical behavior, and the sensitivity analysis revealed that the SS is sensitive to the concrete strength and the shear span-effective depth ratio
Effects of Carbon Nanotubes on Asphalt Binder Rheology and Wearing Course Mixes
No full textThis study explores the influence of Carbon Nanotubes (CNTs) on the rheological and mechanical performance of asphalt binders and mixtures, with the objective of determining an optimal CNT content for enhanced pavement durability. CNTs were incorporated into asphalt binders at concentrations ranging from 0.5% to 2.0% by weight, and the modified binders were subjected to a comprehensive testing program. Rheological behavior was assessed using Rotational Viscosity (RV), Dynamic Shear Rheometer (DSR), Multiple Stress Creep Recovery (MSCR), and Bending Beam Rheometer (BBR) tests. Mechanical properties were evaluated through Marshall stability and Wheel Tracking tests, while microstructural characteristics were analyzed using Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD). The results demonstrated that CNT modification enhanced binder viscosity, high-temperature stiffness, and rutting resistance, with optimal performance observed at 1.5% CNT content. At this dosage, rutting depth was reduced from 15.0 mm to 6.2 mm, and Marshall stability increased from 11.7 kN to 17.4 kN. Additionally, tensile strength peaked at 1290 kPa, and moisture resistance (TSR > 86%) was significantly improved. However, higher CNT concentrations (>1.5%) resulted in particle agglomeration, adversely affecting workability and fatigue resistance. The findings identify 1.5% CNT as the optimal dosage, offering a balanced enhancement in performance without compromising binder flexibility
Effect of Air Pressure on Changes in Parameters and Soil Settlement Behavior in Very Soft Soils
No full textAn effective soil improvement method is essential in soft soil due to the poor bearing capacity for construction loads. To address the challenge, the use of the staged air pressure method with Suction Assisted Vacuum Preloading (SAVP) has shown significant potential when applied through Geosystem Air Booster Vacuum Preloading (GAVP), specifically designed with a sensor system as a real-time measuring tool for soil parameter changes. Therefore, this research aims to examine the effectiveness of the SAVP method in relation to the discharge of drained water from prefabricated vertical drains (PVD) on changes in soil parameters due to air pressure and vacuum using the GAVP tool. The method used five PVDs in large-diameter soil sample tubes, applying air pressure and vacuum simultaneously and selectively. This experimental setup was designed to examine the fundamental aspects of soil parameter changes, namely permeability, consolidation, and volume compression coefficient. The results showed that soil parameters during testing interacted with each other, where air pressure balanced with vacuum caused changes and optimized settlement and consolidation efficiency. Decreasing air pressure enhanced vacuum performance, causing a corresponding rise in soil settlement and consolidation degree. However, increasing air pressure decreased soil settlement and the degree of consolidation
Enhancing Post-Fire Performance of Lightweight RC Slabs Using Expanded Polystyrene and Steel Fibers: An Experimental Study
No full textAggregate significantly influences the mechanical properties of concrete material and has a crucial role in post-fire behavior. This research focuses on investigating the post-fire behavior of a fiber-reinforced one-way slab made from lightweight expanded polystyrene (EPS) aggregate concrete. The experimental study consisted of testing fourteen fiber-reinforced self-compacting concrete (SCC) one-way slabs with EPS as a partial replacement of coarse aggregate. All specimens have identical dimensions of 1800×500×125 mm. The main parameters investigated included fire exposure, EPS replacement ratio, and steel fiber content. The tested specimens were divided into two groups. The first group included seven specimens tested under monotonic static load, whereas the seven specimens of the second group were tested under monotonic static load after being exposed to a steady-state temperature of 700°C for one hour. Following exposure to fire, results revealed a dramatic decrease in the structural performance of the slab specimens, including cracking load, ultimate load, stiffness, absorbed energy, and ductility, especially for the non-fibrous lightweight samples. However, adding EPS beads in the concrete mixture helps in reducing strength degradation due to fire exposure, and the higher the EPS content, the less strength degradation. This result exposed the positive impact of EPS on the structural performance of RC lightweight slabs exposed to fire due to their thermal properties. Moreover, results revealed a significant enhancement in post-fire stiffness, ductility, and absorbed energy of the RC slab due to steel fiber inclusion, showing their constructive impact on the slab performance
Numerical Assessment of Inter-Pillar Stability in Inclined Ore Bodies for Underground Mining Design
No full textThis paper presents a methodology for assessing the stability of stoping chambers and inter-chamber pillars (ICPs) during underground mining of ore bodies with varying dip angles. The objective is to determine optimal parameters for excavation elements (chamber width and pillar spacing) that ensure the stability of the mining system under fractured rock mass conditions. The Zhezkazgan deposit’s geomechanical properties were used as the modeling case study. The methodology includes geotechnical core mapping (with RQD, Q-system, and GSI classifications), laboratory strength testing, field–laboratory correlation, and numerical modeling using the finite element method. Particular focus is placed on the sensitivity of stability to variations in GSI, depth, and excavation geometry. The results indicate that increasing the dip angle significantly reduces the stability of both chambers and pillars. The novelty of this study lies in the comprehensive assessment of structural factors and excavation geometry on mass stability under site-specific geological conditions
Probabilistic Reliability Framework for Nanomaterial-Stabilized Soft Clays: Model Calibration and Geometry Effects
No full textThe stabilization of soft clay soils using nanomaterials offers a promising alternative to conventional additives such as lime and cement, yet most studies remain deterministic, neglecting soil variability and treatment geometry. This study proposes an experimental–probabilistic framework combining triaxial shear and model footing tests with Monte Carlo simulations to evaluate nano-SiO₂, nano-MgO, and nano-clay. Dosages from 1% to 5% were examined, and 3% was selected as optimal based on strength improvement and economic feasibility. Classical bearing capacity models (Terzaghi, Meyerhof, Hansen) were applied and calibrated using regression factors, with input variability modeled under normal and lognormal distributions. Results indicate that nano-MgO achieved the lowest probability of failure ( < 0.1), nano-SiO₂ showed intermediate but geometry-sensitive performance, and nano-clay provided limited reliability. The calibrated Terzaghi model (R² = 0.742) yielded the most consistent predictions. Enlarged treatment zones improved stress redistribution and reduced failure risk. The study also identifies priorities for future work: durability under cyclic loading, hybrid nanomaterial blends (e.g., SiO₂ + MgO), and scalability for large infrastructure projects. Collectively, the findings establish a reliability-based framework that integrates probabilistic modeling, calibration, and material geometry optimization for resilient geotechnical design
Benchmarking Classical and Deep Machine Learning Models for Predicting Hot Mix Asphalt Dynamic Modulus
No full textThe dynamic modulus (|E*|) of hot-mix asphalt (HMA) is a crucial mechanistic characteristic essential in defining the strain response of asphalt concrete (AC) mixtures under varying loading rates and temperatures. This paper aims to conduct a comprehensive investigation of classical machine learning (ML) and deep learning (DL) algorithms as applied to the prediction of |E*| and compare their performance with renowned |E*| regression models (Witczak NCHRP 1-37A, Witczak NCHRP 1-40D, and Hirsch). Eight state-of-the-art ML and DL algorithms are attempted with diverse structures, including multiple linear regression (MLR), decision trees (DT), support vector regression (SVR), ensemble trees (ET), Gaussian process regression (GPR), artificial neural networks (ANN), recurrent neural networks (RNN), and convolutional neural networks (CNN). A comprehensive database was assembled, incorporating 50 AC mixtures, of which 25 were from the Kingdom of Saudi Arabia and 25 were from the state of Idaho, USA. This database encompasses an extensive dataset of 3,720 |E*| measurements, associated with thirteen input features representing the proposed AC mixtures' aggregate gradations, binder characteristics, and volumetric properties. This pioneering study surpasses existing research by examining various algorithms to predict |E*| on the same dataset, applying them with different structures and individual optimization to achieve optimal performance. The developed models are evaluated based on multi-stage assessment criteria, including the accuracy and complexity performance measures and rationality based on a sensitivity analysis. The multi-stage comparative analysis results reveal that the bagging ETs, GPR with exponential kernel, and DT record the highest prediction accuracy; however, only the bagging ETs yield the highest accuracy, lowest training and testing complexity, and rational trends throughout the sensitivity analysis. The research outcome has the potential to provide pavement engineers with advanced tools for predicting |E*| and, therefore, optimizing pavement designs and rehabilitations. Doi: 10.28991/CEJ-2025-011-01-06 Full Text: PD
Seismic Assessment of First and Second Secant Stiffness for the Masonry Infilled RC Frame
No full textThe seismic resilience of composite concrete frame structures composed with masonry infill walls, is a critical research area due to its impact on structural performance during earthquakes. Most studies on reinforced concrete (RC) frames focus on key seismic response parameters like lateral strength and overall hysteretic behavior under cyclic loading, often analyzing the first and second secant stiffness throughout the seismic loading process. The present study examines the first and second secant stiffness as the structural performance during earthquake. A series of experimental tests are performed on half scaled RC frames filled with autoclaved aerated concrete (AAC) block masonry with external dimensions of 1.5 m x 1.5 m. These frames are subjected to displacements ranging from 2 mm to 6 mm and frequencies between 1 Hz and 7 Hz for simulating earthquake loading conditions. The experimental program aimed to evaluate the resistance of structure to earthquake loading by using dual mode of testing viz. displacement and frequency-controlled loading protocol. During test the RC frame responded elasto-plastically due to minor cracking at block joints and localized yielding at the interface between the frame and the infill at lower frequencies and displacements. Conversely the degradation of both first and second secant stiffness values became more pronounced at higher frequencies. The first secant stiffness decreased by 73.4%, while the second secant stiffness showed increase of 24.6% at a displacement of 4 mm and a frequency of 4 Hz with respect to the previous loading cycle which indicated the complex stress redistribution and temporary stabilization. Doi: 10.28991/CEJ-2025-011-02-05 Full Text: PD
A Novel Approach to Detect Parking Space Occupancy for Efficient Urban Management
No full textObjectives: This study aims to develop A Novel Approach to Detect Parking Space Occupancy for Efficient Urban Management utilization and enhance user experience with real-time, accurate data. Methods/Analysis: The proposed system detects the parking space occupancy for efficient urban management by using a Multi-Component Attention Graph Convolutional Neural Network (DPSO-MCAGCNN) and processes data from the PKLot dataset. Pre-processing is performed using the Maximum Correntropy Quaternion Kalman Filter (MCQKF) for normalization. Key features like area, perimeter, and aspect ratio are extracted using the Second-Order Time-Reassigned Multi synchro squeezing Transform (SOTRMT) and analyzed through MCAGCNN. The Leaf-in-Wind Optimization (LWO) technique is incorporated to optimize the MCAGCNN for higher accuracy. Findings: The proposed system achieves significant improvements over existing methods, including 27.84%-29.27% higher accuracy, 25.87%-29.84% improved R², and 16.27%-19.84% reduced Mean Squared Error (MSE). Evaluation metrics such as RMSE, MAE, and MAPE confirm its robust performance. Novelty/Improvement: The integration of LWO into MCAGCNN enhances optimization and precision, surpassing the performance of state-of-the-art methods like EUPE-SVM, RTPM-YOLOv5, and MASP-LSTM, making it an innovative solution for intelligent parking management. Doi: 10.28991/CEJ-2025-011-02-015 Full Text: PD