Shahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Genetic programming based prediction of the angle of draw: A key parameter in underground coal mine subsidence11631173374910.22044/jme.2026.16240.3147ENPGANESANVNIT, NAGPURRitesh DLokhandeMining Engineering, Associate Professor, VNIT, Nagpur, IndiaSiddharthaRoyUnderground Mining Division, CMPDI, Ranchi, IndiaHemantAgrawalNaCCER, CMPDI, Ranchi, IndiaJournal Article20250513Subsidence associated with underground coal mining is a significant geotechnical concern in many coal-producing regions. The extraction of coal over large areas from underground often leads to the collapse of overlying strata into the goaf, subsequently causing surface subsidence. The extent of this subsidence varies widely across mines, depending on several factors, including mine geometry, geological discontinuities, physico-mechanical properties of the overlying strata, extraction method, seam thickness, and depth of working. Among these, the angle of draw (AoD) plays a critical role in delineating the subsidence-affected zone, particularly in underground coal mining. Accurate prediction of AoD is essential for safe mine planning and the mitigation of subsidence-related hazards. In the present study, a comprehensive field investigation was conducted to collect mine operational parameters from various underground coal mines. Using this dataset, Genetic Programming (GP) was employed to model the relationship between AoD and key mining and geological parameters. The developed GP model demonstrated a strong correlation between predicted and measured AoD values, with a coefficient of determination (R) = 0.7921, highlighting the model’s predictive capability. Additionally, a sensitivity analysis (SA) was performed to identify the most influential input parameters affecting AoD. The analysis indicated that, while all five input variables significantly impact AoD, the compressive strength of overlying strata exhibited the highest influence (sensitivity score = 0.98). The findings of this study provide a data-driven approach to predict the angle of draw in underground coal mines, offering valuable insights for improved mine design, extraction strategies, and surface infrastructure protection.https://jme.shahroodut.ac.ir/article_3749_3a1871b07491e45e1e0566fc3d613cdf.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701An innovative approach to mineral resource classification based on Riemannian clustering and machine learning in a copper deposit11751193359210.22044/jme.2025.16320.3174ENMarco AntonioCotrina TeatinoDepartment of Mining Engineering, Faculty of Engineering, National University of TrujilloJairo JhonatanMarquina-AraujoDepartment of Mining Engineering, Faculty of Engineering, National University of TrujilloJose NestorMamani-QuispeFaculty of Chemical Engineering, National University of the Altiplano of PunoSolio MarinoArango-RetamozoDepartment of Mining Engineering, Faculty of Engineering, National University of Trujillo0000-0003-3594-0329Joe AlexisGonzalez-VasquezDepartment of Industrial Engineering, Faculty of Engineering, National University of TrujilloKevin DanielRondo-JalcaDepartment of Mining Engineering, Faculty of Engineering, National University of TrujilloJournal Article20250528The classification of mineral resources significantly impacts mine planning, economic feasibility, and regulatory compliance. Despite its importance, such classification frequently depends on the subjective judgment of the Qualified Person (QP), owing to the absence of internationally standardized technical criteria for delineating resource categories. To mitigate this limitation, an innovative methodology integrating clustering based on Riemannian geometry with machine learning techniques was developed for mineral resource classification. A database of 5,654 composited samples from 185 diamond drill holes in a copper deposit in central Peru was utilized to classify 318,443 blocks. Copper grades were estimated through Ordinary Kriging (RMSE = 0.102; MAE = 0.069), generating geostatistical variables kriging variance, average distance to samples, and number of samples that served as input features for the classification. Clustering was performed using both classical KMeans and Riemannian KMeans, followed by spatial smoothing via XGBoost and Random Forest algorithms. Absolute coordinates were incorporated to address spatial discontinuities in classification outputs. The combination of the Riemannian model with Random Forest produced the highest classification performance, with a Silhouette index of 0.26 and a Davies-Bouldin index of 0.72. The resulting metal content was estimated at 4.24 Mt of copper at 0.44% grade (measured), 6.49 Mt at 0.34% Cu (indicated), and 7.68 Mt at 0.32% Cu (inferred), demonstrating close alignment with QP estimates while exhibiting improved spatial coherence. In summary, the Riemannian-based approach outperformed classical KMeans and conventional classification methods, providing a more robust, objective, and globally consistent alternative.https://jme.shahroodut.ac.ir/article_3592_3ed47597da91dc4901d196578e84746d.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Structural Control of Cobalt Mineralization in the Aït Ahmane Region – Bou Azzer El Grara Inlier (Central Anti-Atlas, Morocco): Contribution of remote sensing and underground geological mapping11951213378510.22044/jme.2026.16825.3302ENAbdelhamidBajadiScience and Engineering Research Laboratory (LaRSI), Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez, MoroccoDrissEl AzzabScience and Engineering Research Laboratory (LaRSI), Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez, MoroccoAnasDriouchLaboratory of Applied Geophysics, Geotechnics, Engineering Geology and Environment, Mohammadia School of Engineers, Mohammed V University in Rabat, 10090, MoroccoMohammedOuchchenDepartment of Earth Sciences, Faculty of Sciences, Ibn Zohr University, Agadir, MoroccoMohammed JalalTAZIScience and Engineering Research Laboratory (LaRSI), Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez, MoroccoJournal Article20250916The Bou Azzer–El Graara inlier, located in Morocco’s central Anti-Atlas, is well known for its significant cobalt mineralization, genetically associated with a Pan-African serpentinized ultrabasic ophiolitic massif. In this context, a structural study was conducted in the Aït Ahmane area, situated at the eastern end of the Bou Azzer mining district, with the aim of analyzing structural lineaments, which constitute a fundamental tool in geological mapping and mineral exploration. The methodological approach is based on the interpretation of multispectral remote sensing data to map surface lineaments and compare them with structures observed underground. The processing applied to the Landsat 8 OLI imagery includes radiometric and atmospheric corrections, followed by principal component analysis (PCA), which enhances the discrimination of linear structures and allows the production of reliable lineament maps. In parallel, underground geological mapping was carried out in the F53 vein deposit, at two lower exploitation levels, to characterize mineralized structures at depth. The integration of surface and subsurface datasets highlights two main structural families. The first, trending N–S to NE–SW, is associated with cobalt-bearing structures hosted within diorites. The second, oriented NW–SE to WNW–ESE, corresponds to cobalt-mineralized tectono-lithological contacts between serpentinites, basic rocks, and diorites. The correlation between surface-mapped lineaments and deep-seated structures is significant, emphasizing the structural continuity between the surface and subsurface domains.https://jme.shahroodut.ac.ir/article_3785_76a28cc2c7747a78c51e45441679678d.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Parametric Optimization of Gas drainage via Cross-measure boreholes in Floor Strata in a Coal Mine: A Multiphysics Simulation Study12151235370210.22044/jme.2026.16664.3273ENTingzeLiFaculty of Geosciences and Engineering, Southwest Jiaotong UniversityYuWangSchool of Resources and Geosciences, China University of Mining and TechnologyGenyuanTanCollege of Safety Engineering, China University of Mining and TechnologyJournal Article20250819Effective gas drainage in coal mines necessitates the precise optimization of borehole parameters to reduce gas pressure and prevent gas outbursts. However, current drilling designs predominantly rely on field experience rather than site-specific quantitative analysis of geological conditions, leading to limitations in adaptability. This study establishes a COMSOL-based multiphysics coupling model that integrates stress-permeability interactions, gas adsorption-desorption kinetics, and fracture-induced permeability evolution to evaluate the gas drainage performance of cross-measure boreholes in floor strata. Simulation results indicate that directional borehole spacing is the most influential factor: reducing the spacing from 25 m to 20 m significantly increases gas drainage efficiency by 31.4%, while extending the drainage duration from 90 days to 270 days expands the influence radius by more than 35%. In contrast, variations in borehole diameter (75-115 mm) and negative pressure (10-90 kPa) exert a negligible impact on gas pressure (with a variation of less than 5%), reflecting limited sensitivity. The optimal borehole location is determined to be at the lower boundary of the mining-induced fracture zone. A gradient layer analysis further confirms that the perforation depth should match the range of the plastic deformation zone (15-25 m). The proposed parametric optimization strategy provides a quantitative framework for directional drilling design, enabling the matching of borehole layout with the scale of fracture development. These findings contribute to enhancing the accuracy of gas control and the engineering adaptability of gas drainage systems under complex geological conditions.https://jme.shahroodut.ac.ir/article_3702_82c710378a2c4fec88b369f6f3421386.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Mangement and developement of Mining waste from the dry Process of the Djebel Onk Phosphate Ore Benefication Plant (Somophos)-Tébessa.12371248389010.22044/jme.2026.16738.3283ENChouroukMENIAIMINES, SCIENCES DE LA TERRE, BADJI MOKHTAR UNIVERSITYMohamedBOUNOUALAMINES, SCIENCES DE LA TERRE, BADJI MOKHTAR UNIVERSITYRaoufCHAABIAMINES, SCIENCES DE LA TERRE, BADJI MOKHTAR UNIVERSITYCHAHRASIFIBadji Mokhtar UniversityRimAMATAMINES, SCIENCES DE LA TERRE, BADJI MOKHTAR UNIVERSITYJournal Article20250827Phosphate mining at Djebel Onk (Tébessa, Algeria) generates large volumes of solid wastes that occupy land, release dust, and contaminate surrounding environments. This study aims to characterize and valorize two types of dry tailings, namely +15 mm screening rejects and fines from pneumatic separation (TSV), in order to reduce their environmental impact and recover residual phosphate. Samples were analyzed using particle size distribution, atomic absorption spectrometry (AAS), X-ray diffraction (XRD), and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS). Treatment tests included calcination (laboratory furnace, 700–1000 °C, 15–30 min) and direct flotation (Denver-type cell, with oleic acid and phosphoric acid as reagents).<br />The results show that the +15 mm rejects and TSV fines contain 21.42 % and 23 % P₂O₅, respectively, while the richest fraction (-0.5 + 0.25 mm) reaches 28.99 % P₂O₅ with 1.01 % MgO. Calcination at 1000 °C for 30 minutes produced concentrates with 33.51 % P₂O₅ and near-complete CO₂ removal, whereas direct flotation yielded concentrates of ≈ 30 % P₂O₅ with MgO < 1 %. The novelty of this work lies in the combined application of calcination and flotation, which demonstrates their complementarity for efficient recovery of phosphate from dry tailings. These results confirm the potential for phosphate valorization while reducing environmental impacts and supporting more sustainable mining practices.<br /><br />Keywords: Phosphate, mining wastes, calcination, flotation, environmental management, Djebel Onkhttps://jme.shahroodut.ac.ir/article_3890_52c5e3461072d2a20b62a71e196caf4e.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Interactions between cultivated grasses and culturable microbial populations during the phytoremediation of mine tailings12491261375010.22044/jme.2026.16918.3338ENSnežanaBrajevićInstitute for the Application of Nuclear Energy-INEPAleksandarSimićFaculty of Agriculture, University of BelgradeVeraKarličićFaculty of Agriculture, University of BelgradeNikolaMilanovićInstitute for the Application of Nuclear Energy-INEP, University of BelgradeMonikaStojanova3Open Science, Association for Scientific Research, Educational and Cultural Activities, Jane Sandanski, OhridBlažoLalevićFaculty of Agriculture, University of BelgradeŽeljkoDželetovićInstitute for the Application of Nuclear Energy-INEP, University of BelgradeJournal Article20251012Permanent mining generates substantial amounts of flotation tailings with highly unfavourable physical and chemical properties, often devoid of vegetation. Their stabilization relies on phytoremediation, particularly through the establishment of grass cover. Successful revegetation requires sufficient nutrient availability and the activity of soil microorganisms that transform nutrients into plant-accessible forms. However, the interactions between plants, nutrients, and microflora during this process remain poorly understood. This study aimed to investigate the temporal dynamics and interrelationships within the plant–nutrient–microorganism system during the revegetation of flotation waste using four grass species—tall fescue, red fescue, meadow fescue, and perennial ryegrass. Plants were grown under controlled conditions on flotation tailings with different fertilizer treatments: organic (NPK 4:4:4) and mineral (NPK 20:20:20) fertilizers at varying concentrations (1% and 2% O; 0.25% and 0.5% M) and irrigation levels (50% and 75% of field water capacity). Microbial diversity (culturable bacteria, ammonifiers, fungi, and actinomycetes) was used as an indicator of remediation efficiency. Organic fertilization had the most pronounced effect, improving plant height, biomass yield, and microbial activity, particularly in tall fescue. Bacteria and ammonifiers responded positively to mineral fertilization under higher irrigation in red fescue and to organo-mineral treatment under lower irrigation in perennial ryegrass. The highest abundance of actinomycetes occurred under reduced irrigation in red fescue and perennial ryegrass. Overall, perennial ryegrass demonstrated the strongest correlation between cultivation conditions, microbial activity, and phytoremediation potential, highlighting its suitability for the ecological rehabilitation of flotation tailings.https://jme.shahroodut.ac.ir/article_3750_4036672cf096cd1514229af2bf6755dd.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Co-activation mechanism of lead nitrate and sodium thioglycolate in smithsonite sulfidization flotation12631283383810.22044/jme.2026.17042.3355ENAymanM. IbrahimState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, Yunnan 650093, PR ChinaHanWangState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Yunnan Key Laboratory of Green Separation and Enrichment of Strategic Metal Resources, Faculty of Land Resources Engineering, Kunming University of ScienceDianwenLiuState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Yunnan Key Laboratory of Green Separation and Enrichment of Strategic Metal Resources, Faculty of Land Resources Engineering, Kunming University of SciencePeilunShenState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Yunnan Key Laboratory of Green Separation and Enrichment of Strategic Metal Resources, Faculty of Land Resources Engineering, Kunming University of ScienceHassanA. OsmanDepartment of Chemistry, Faculty of Education, University of Nyala, Nyala 63311, SudanMuhannadAlhajDepartment of Mining Engineering, Faculty of Engineering Science, Omdurman Islamic University, Khartoum 14411, SudanMohamedA. IbrahimDepartment of Mining Engineering, Faculty of Engineering Science, Omdurman Islamic University, Khartoum 14411, SudanMisbahKhalilDepartment of Mining Engineering, Faculty of Engineering Science, Omdurman Islamic University, Khartoum 14411, SudanJournal Article20251027In this study, Pb²⁺ and STG were used as effective co-activation reagents before sodium sulfide treatment to assess their effect on smithsonite floatability. The flotation test results showed that sodium sulfide nonahydrate (SSN) and sodium diethyl dithiocarbamate (DDTC) concentrations significantly impacted floatability, achieving maximum recoveries of 89.32% and 93.19%, respectively. XPS analysis confirmed that Na₂S treatment introduced sulfur species onto the smithsonite surface, enhancing collector attachment and facilitating the formation of PbS and ZnS. FTIR analysis further substantiated that co-activation enhances DDTC adsorption via C=S vibrations, thereby increasing the number of active sites available for interaction with the collector relative to direct sulfidation. FESEM-EDS and AFM analysis at pH 9 confirmed that co-activation resulted in the formation of denser, cloud-like layers of PbS and ZnS on the surface. These layers improved flotation efficiency, increased hydrophobicity, and strengthened DDTC interaction, thus promoting flotation. Additionally, ToF-SIMS and EPMA analyses indicated higher Pb⁺ and S⁻ intensities in the co-activation system, confirming enhanced surface reactivity and substantiating the increased activity and diversity of sulfidation products. This study offers an effective approach to enhancing smithsonite flotation recovery by optimizing surface chemistry and collector attachment.https://jme.shahroodut.ac.ir/article_3838_f3388dc9af5aa0b3399ffbbb0f254290.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701A Marble-Specific Rock Mass Classification Framework Integrating Empirical Parameters and Machine Learning Validation12851306378810.22044/jme.2026.17326.3432ENShambhaviSinhaNational Institute of Technology KarnatakaAnupTripathiNational Institute of Technology KarnatakaAkhilAvcharNational Institute of Technology KarnatakaMritunjayKumarNITK SurathkalJournal Article20251215Accurate assessment of rock mass quality in marble quarries remains challenging because conventional empirical classification systems are largely strength-dominated and insufficiently sensitive to discontinuity-controlled block instability. This study proposes a quarry-specific empirical framework, termed the Marble Rock System (MRS), designed to explicitly capture structural, hydro-mechanical, and alteration-driven controls governing bench-scale stability in dimension stone marble quarries. The primary objective was to develop and validate an empirically grounded classification system using machine learning as an independent diagnostic tool rather than as a black-box predictor.<br />A comprehensive geomechanical database comprising 85 quarry-scale records was developed from three active marble quarries in southern Rajasthan, India. Six physically interpretable parameters intact strength, weathering or serpentinization, joint frequency, joint surface condition, groundwater influence, and block stability were incorporated into the MRS framework. Supervised machine learning models, including artificial neural networks, support vector machines, and linear regression, were trained to predict independently derived factors of safety for validation. Model performance was evaluated using coefficient of determination, root mean square error, cross-validation, and classification metrics.<br />Results show that MRS-based models achieved consistently higher predictive accuracy, improved class separability, and more stable generalization than models trained using conventional Rock Mass Rating inputs. Sensitivity analysis revealed that block stability and joint characteristics dominate stability prediction, while intact strength plays a secondary role. These findings confirm that marble quarry slope behaviour is primarily discontinuity-controlled. The proposed MRS provides a physically interpretable, empirically validated framework for quarry-scale stability assessment and offers a robust alternative to conventional classification systems for operational decision-making.https://jme.shahroodut.ac.ir/article_3788_d12f1b20691847e0eca054443beef1f0.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Evaluation of Rock Characteristics and Degradation Induced by Cyclic Freezing-Thawing Action of some Igneous Rocks under Climate Change Conditions in Saint Katherine Arid Mountain Region, South Sinai, Egypt13071323387910.22044/jme.2026.17427.3461ENB.G.Mousa1Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi 830011, China 2Department of Mining and Petroleum Engineering, Al-Azhar University, Cairo, Egypt.Marzouk Mohamed AlyAbdelhamidNasr city, Cairo, EgyptMohamed ElgharibGomahDepartment of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, EgyptM. M.ZakiDepartment of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, EgyptH.A.FaragDepartment of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, Egypt0000-0002-1303-8759W.M.DrazDepartment of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, EgyptRemondaDimianXinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi 830011, ChinaYousef A.Al-MasnayXinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi 830011, ChinaJournal Article20260206Rock damage due to severe environmental conditions may affect durability over time. Therefore, this study aims to investigate the degradation of igneous rocks triggered by freezing-thawing weathering and to predict their loss of integrity. Four types of igneous rocks were collected from the Saint Katherine mountain area, Sinai, Egypt, and subjected to 100 cycles of the freeze-thaw method. The land surface temperature variation was analyzed using remote sensing data as an indicator for selecting the freeze-thaw temperature for our experiments. The physical and mechanical characteristics of the studied rocks, such as dry bulk density, effective porosity, ultrasound pulse velocity, abrasion loss, unconfined compressive strength, and point load strength, were measured after 0, 20, 40, 60, 80, and 100 cycles of the freeze-thaw process and evaluated using deterioration ratios and statistical models. Exponential and linear decay function models were established to statistically assess the integrity loss rate of the mechanical strength properties of the selected rocks. Two critical factors, decay constant and half-life, were derived from the studied models and used to predict rock durability. This evaluation suggests that the behavior of rock decay is very similar across different types of igneous rocks, exhibiting a strong correlation and high precision. Therefore, these models are reliable and valid for predicting the durability of the studied igneous rocks. Results also showed that the tested igneous rocks can be used as construction stones in regions exposed to repeated cyclic freezing-thawing weathering and harsh environmental conditions over long periods without disintegration.https://jme.shahroodut.ac.ir/article_3879_6abf8bea8b8b48901d9af814a70c38e6.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Effects of Input Log Selection on Permeability Accuracy in Heterogeneous Sirt Basin Reservoirs, Libya13251342365910.22044/jme.2025.16192.3128ENMohammedA.AmirUniversity of Benghazi , Department of earth science , Benghazi , Libya .HamzahS. AmirDepartment Petroleum Engineering, Faculty of Engineering, Bright Star University, Elbrega, Libya.MokhtarFarkashArabian Gulf Oil Company (AGOCO)Journal Article20250508Permeability estimation is an essential phase in assessing the hydrocarbon potential within porous media and designing reservoir management methods. Recently, machine learning (ML) methodologies have gained prominence in the prediction of permeability. The initial stage in constructing highly reliable ML models is to identify the optimum combinations of input logs, as permeability is a highly sensitive parameter; this step is essential and can influence model accuracy. While feature engineering methods provide valuable insights in selecting suitable input logs, the effectiveness of these logs or their combinations remains underexplored, particularly in the context of high-heterogeneity reservoirs. The current study intends to save time by evaluating the effectiveness of twelve distinct models, each constructed using a Multi-Layer Perceptron (MLP), based on various combinations of input logs using data from the Nubian reservoir, Sirt Basin, Libya. The methodology involved several steps, including preprocessing, splitting, optimization, and validation. The findings demonstrate that single-input logs, mainly the Gamma-ray (GR), bulk density (RHOB), and sonic logs (DT), exhibited higher correlation coefficients compared to the multiple log combinations. The GR model attained the best R² of 0.994, indicating its sensitivity in capturing non-linear relationships. On the other hand, multi-log models achieved variable accuracy, resulting in increased learning complexity. The study highlights the efficiency of selecting the optimal combination of input logs, providing practical guidance for ML-based permeability prediction in heterogeneous reservoirs.https://jme.shahroodut.ac.ir/article_3659_d1de95553626094b4f8731721570a72d.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Exploring Crack Growth Patterns of Anisotropic Materials in Bi-Material Notched Discs: Insights from a Combined Experimental and Numerical Approach13431369386610.22044/jme.2026.17350.3438ENVahabSarfaraziDepartment of Mining Engineering, Hamedan University of Technology, Hamedan, Iran,HadiHaeriDepartment of Mining Engineering, Higher Education Complex of Zarand, Shahid Bahonar University of Kerman, Kerman, Iran.JinweiFuSchool of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou, 450046, ChinaShadmanMohammadi BolbanabadDepartment of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, IranAlirezaShakerDepartment of Geology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran,Journal Article20251222In this research, samples of bi-material notch discs were created and examined in the laboratory to investigate their mechanisms of indirect tensile failure. Brazilian tensile strength tests were conducted to determine the mechanical properties of these samples under a loading rate of 0.016 mm/s. The discs were designed with a central notch and were made from two materials: concrete and gypsum. Gypsum (Material 1) was placed at the center (core), while concrete (Material 2) was used to form a peripheral strip (ring) around this core. The core diameter and ring thickness were varied, resulting in a total of 21 specimens that were prepared and subjected to axial loading during testing. To support the experimental results, the Particle Flow Code (PFC) was utilized to simulate the tests. It was observed that both the notch dimensions and the choice of materials significantly influenced the mechanical behavior and fracture characteristics of anisotropic rocks. Furthermore, based on the results obtained from the laboratory samples and the modeling of these samples using PFC software, strong agreement was achieved regarding crack growth behavior. In addition, a comprehensive study was conducted on the samples through numerical modeling, which included the distribution of bond forces and Rosette diagrams, as well as crack growth patterns.https://jme.shahroodut.ac.ir/article_3866_0870c30d72dca3de9e7d9f172bba4365.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Assessment of Ground Surface Settlement and Excavation Volume Loss in Mechanized Tunneling Using Field Data and Numerical Modelling: A Case Study of Tabriz Metro Line 213711388349510.22044/jme.2025.16120.3113ENHosseinAzadDepartment of Mining Engineering, Sahand University of technology, Tabriz, Iran.0000-0002-6087-9892HamidChakeriAssociate Professor, Department of Mining Engineering, Sahand University of technology, Tabriz, IranHadiShakeriDepartment of Mining Engineering, Sahand University of technology, Tabriz, Iran.Journal Article20250427Mechanized tunnelling in soft soils often results in ground settlement both around the tunnel and at the surface, which can potentially damage urban infrastructure and surrounding buildings. Several geological and operational factors influence the extent of ground settlement. This paper investigates the actual ground settlement caused by over 10 kilometers of tunnelling along Tabriz Metro Line 2, with a particular focus on the materials and positions of the tunnelling machine. The results show that 55-60% of the total settlements occur behind the shield of the tunnelling machine, which is consistent with Thewes’ (2009) diagram. The surrounding soil was categorized, and using data from settlement pins, the actual Volume Loss (VL) was analyzed across three geological sections consisting of sandy, clayey, and mixed materials. The findings reveal that volume loss in sandy materials is greater than in clayey and mixed soils, at approximately 1.02%. Additionally, the volume loss in mixed soils was calculated to be 0.82%, while in clay soils, it was 0.53%. To assess the impact of different materials on surface settlement, numerical modeling was carried out using Plaxis 3D software. The numerical results, considering volume losses of 1.05% for sandy materials, 0.8% for mixed materials, and 0.5% for clay materials, closely matched the actual settlement data.https://jme.shahroodut.ac.ir/article_3495_07d0e91742be0d06b22d24d0f9cdf39b.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Introduction of the Fibonacci Transformation Method for Separating Geochemical Anomalous Zones on the Scale of Supplementary Explorations13891408354210.22044/jme.2025.16261.3157ENMohammadrezaAgharezaeiAmirkabir University of TechnologyArdeshirHezarkhaniAmirkabir university of Technology0000-0002-1149-3440Journal Article20250518Geochemical exploration as an advantageous exploration method mostly deals with anomaly separation and related endeavors. Many experts have suggested various types of anomaly identification methods. The intention of this research is introduction of a new method for separating geochemical anomalies based on the Fibonacci sequence for the first time. The Fibonacci features of datasets were clarified and the method was introduced and applied on a dataset of bore-hole samples of Hired gold deposit located in southern Khorasan province, Iran. The main result of this study is the successfully establishing of a Fibonacci-based procedure that leads to separate geochemical anomalies. The determined thresholds by this method were compared with U-statistics and Concentration-Volume fractal modeling. Evaluation of the results revealed high consistence between the outcomes of the methods. The U-Statistics threshold for background was 105 ppb for gold and the Fibonacci Transformation method’s threshold was 109 ppb. This new method specified 170 ppb for gold moderate anomaly and the C-V fractal determined 169 ppb which are almost the same. The performance of the new method was assessed by calculating misclassification errors. The average total misclassification error was 0.023 which is acceptable and quite reasonable since the methods are fundamentally different. As the other main results of this study, it is confirmed that one of the Fibonacci features which is defined as Fibonacci index (FI) fluctuates among element pairs in the same way that geochemical correlation does. The FI could be considered as a genetic-related factor in geochemical studies and ore evolution researches.https://jme.shahroodut.ac.ir/article_3542_16fff9866cb5a4bc25d17f7bb17babbf.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Investigating the relationship between Cerchar abrasivity index with physico-mechanical and petrographic properties of granitic building stones14091427354510.22044/jme.2025.16303.3167ENSinaGhavamiIsfahan University of Technology. Department of Mining Engineering. Isfahan 8415683111, IranEbrahimGhasemiIsfahan University of TechnologyMohammad HosseinKadkhodaeiIsfahan University of Technology. Department of Mining Engineering. Isfahan 8415683111, IranAliFarhadianUniversity of Jiroft, Faculty of Eng., Mining Group, Kerman, IranJournal Article20250527Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the stone abrasivity. One of the most commonly used methods is Cerchar abrasivity index (CAI). This study mainly focuses on investigating the relationship between CAI with petrographic and physico-mechanical properties of granitic building stones. For this purpose, 14 different types of commercial granitic building stones, collected from different regions of Iran, were subjected to laboratory investigations and the effect of the petrographic and physico-mechanical properties of these stones on CAI was examined using simple and multiple regression analysis. Meaningful and reasonable relationships were observed. According to the obtained results, equivalent quartz content (EQC) of granitic building stones was found to be the most effective parameter on CAI. Using linear and nonlinear regression analysis, two empirical correlations for CAI prediction based on EQC were developed. The results showed that both linear and nonlinear correlations have high performance with determination coefficients (R<sup>2</sup>) of 0.876 and 0.882, respectively. These correlations can determine the CAI with acceptable error, with root mean square error (RMSE) and mean absolute error (MAE) values of 0.135 and 0.105, respectively. Furthermore, the relationship between the diamond segment wear (SW) and CAI was investigated for the studied stones. The results showed that SW is directly related to the CAI, and there is a strong linear correlation between these two parameters with R<sup>2</sup> of 0.787. The proposed correlation can be applied for fast prediction of cutting tool wear for practical applications in building stone processing plants with circular sawing machine, which can lead to enhanced cutting efficiency and productivity.https://jme.shahroodut.ac.ir/article_3545_08b67f3f36ccd582217f954f38f751fa.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Improving the copper grade estimation at the Chehel Kureh copper mine using machine learning methods14291443356010.22044/jme.2025.16326.3177ENHamedNorouziDepartment of Mining Engineering, Isfahan University of Technology, IranAliakbarDayaUniversity of Sistan and BaluchestanJournal Article20250529Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting advanced technologies in mineral assessment has also surged. Modern spatial grade modeling techniques can play a pivotal role in decision-making processes. This study aims to compare the performance and capabilities of two popular machine learning methods, including Gaussian Process Regression (GPR) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) in spatial grade modeling of copper at the Chehel Kureh Copper deposit. The dataset comprises 42 drill holes with an average copper grade of 0.18%. Each core sample data point includes seven variables: three spatial coordinates (X, Y, and Depth), lead grade, zinc grade, lithology and copper grade, which serves as the target variable. The Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP-ANN) neural network were employed for copper grade estimation. To make a better assessment, the hyperparameters of both models were optimized using the Bayesian Optimization algorithm. The results showed that the Gaussian Process Regression outperformed MLP-ANN, achieving an RMSE of 0.04 and a coefficient of determination (R²) of 0.89 compared to an RMSE of 0.05 and a coefficient of determination (R²) of for MLP-ANN, suggest the superiority of the Gaussian Process Regression method in estimating copper grade spatial variability.https://jme.shahroodut.ac.ir/article_3560_4fa7da9b95fbcb576828e5465a6d95fc.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Development of a Match Factor for Optimizing Haulage Fleet Efficiency in Open-Pit Mining Operations14451455360610.22044/jme.2025.16360.3188ENMehrnazMohtashamUniversity of MohaghDepartment of Mining Engineering, Faculty of Engineering and Technology, University of Mohaghegh Ardabili, Ardabil, Iranegh ArdabiliJournal Article20250604In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. The match factor, a metric that evaluates compatibility between loaders and trucks, is commonly used to enhance fleet efficiency. However, many existing approaches fail to account for practical mining conditions such as equipment downtime, accurate truck cycle times, and material fragmentation resulting from blasting. These omissions can lead to inaccurate fleet performance evaluations and higher operational costs. This study proposes an improved match factor method that incorporates these critical variables. It includes equipment downtime, truck cycle time estimates based on travel routes, and material fragmentation. The model applies to both homogeneous and heterogeneous fleet configurations and integrates the operational efficiency coefficient of each machine to reflect real conditions more accurately. The model was tested using data from the Sungun copper mine. The match factor values were calculated both with and without accounting for equipment downtime, and loader capacities were adjusted according to the size distribution of blasted material. Results showed that in heterogeneous fleet operations, the match factor increased from 0.74 to 0.85 when operational efficiency was included. Subsystem analyses also revealed match factor values below 1, indicating a need for additional trucks. Overall, the enhanced model enables more efficient equipment use, reduces loader idle time, and contributes to substantial operating-cost savings.https://jme.shahroodut.ac.ir/article_3606_c7377f11a34516f83256f57c948b239f.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701A Practical Model for Short-Term Open-Pit Mine Production Scheduling Based on Goal Programming Approach14571474357010.22044/jme.2025.16402.3200ENSaeidehQaedrahmatDepartment of Mining and Metallurgical Engineering, Yazd University, Yazd, IranJavadGholamnejadYazd University0000-0003-3480-9644AliDabaghDepartment of Mining and Metallurgical Engineering, Yazd University, Yazd, IranJournal Article20250617The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, and slope constraints. One of the key objectives of Short-Term Production Scheduling (STPS) is to ensure a stable and continuous supply of ore to the processing plant, while minimizing operating costs through measures such as reducing unnecessary equipment movements and variation in feed quality. However, one of the major obstacles to the operational feasibility of STPS is the limited working space available for equipment, as well as the excessive equipment movement between benches within each scheduling period. To tackle these challenges, this paper employs an Integer Goal Programming (IGP) with a new constraint that limits active benches per period, enhancing the practicality of production schedules. Unlike previous GP-based STPS models, it improves operational feasibility by ensuring extraction continuity and minimizing equipment movement. The model was tested on a copper deposit using GAMS software. The results show that by applying this new constraint, the average number of active benches per month was reduced from 14 to 10 )36% reduction) and the number of extraction periods per bench from 6 to 4 (33% reduction) without violating the existing constraints such as ore grade, tonnage, or slope. This approach improves equipment efficiency, reduces fuel consumption, reducing equipment relocation costs, promoting operational continuity of extraction and enhances operational feasibility in real conditions.https://jme.shahroodut.ac.ir/article_3570_3f76c5eca0a32a2c4ed443fa75194903.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Efficiency evaluation of rock cutting by TBM disc cutter in closely jointed rock mass14751491363610.22044/jme.2025.16405.3201ENNavidAfrasiabiFaculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of TechnologyMehdiNorooziFaculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood,IranAhmadRamezanzadehFaculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, IranJournal Article20250617In this research, the effect of geometric parameters of closely joints on rock cutting efficiency by TBM disc cutter is studied using PFC3D software. A validated numerical model of linear cutting machine test is developed and the efficiency of disc cutter is investigated on rock mass specimens with different joint configurations (possible combination of dip angles of 30, 60, 90 degrees with joint spacings of 3, 5, 10, 15, 20 cm). Numerical modeling results reveal that in general, the joint spacing has a greater effect on rock cutting efficiency than joint orientation. If the joint spacing is less than 10 cm, the role of the joint angle is reduced and the distances between the joints control the efficiency. When the joints are close together and have a spacing of less than 10 cm, particularly 3 to 5 cm, the best cutting efficiency can be achieved for a joint angle of 90 degrees. The cutting coefficient is decreased by increasing the joint spacing and the maximum CC occurs at a joint spacing of 5 cm. For joint spacing more than 10 cm, the joints with a 90 degrees dip angle have the greatest impact on the specific energy and reduce cutting efficiency. The best disc cutter efficiency and the minimum required normal force is achieved when joint spacing is more than 10 cm and the angle between the joints and advance direction of the disc cutter is 60 degrees. In the tunnel excavation process, with increasing joint spacing, the TBM machine thrust is more important than its torque. The findings of this research provide a basis for predicting TBM efficiency through joint characteristics.https://jme.shahroodut.ac.ir/article_3636_5bf2360b63b03a236db4cf45cd3f0346.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Achieving the optimal blasting pattern using multi-criteria decision-making models (Case study: Miduk copper mine)14931528360010.22044/jme.2025.16486.3223ENMajidNoorian-BidgoliDepartment of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, IranMohammad RezaRezaeiDepartment of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, IranJournal Article20250708Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling and blasting patterns at the Miduk copper mine using Multi-Criteria Decision-Making (MCDM) methods. The primary objectives were to achieve optimal fragmentation, minimize specific charge and drilling costs, and reduce undesirable phenomena like backbreak and flyrock caused by blasting. A total of 52 blasting patterns implemented at the mine were evaluated using various MCDM techniques, including TOPSIS, ELECTRE, VIKOR, and COCOSO. By constructing decision matrices and ranking the alternatives in each method, the most suitable blasting pattern was identified. The Copeland method was further applied to integrate the results from the decision models and establish a consensus on the final ranking of blasting patterns based on the criteria. The study's innovation lies in the application of advanced MCDM techniques to optimize drilling and blasting patterns, as well as the integration of results to enhance the decision-making process's accuracy. The optimal blasting pattern (M_Patt_03) was found to feature a burden of 6.5 meters, a spacing of 8 meters, and a borehole diameter of 150 millimetres, offering the best balance of fragmentation, charge efficiency, and drilling costs, while minimizing backbreak and flyrock. This study demonstrates the effectiveness of MCDM methods in optimizing complex engineering challenges in surface mining, providing a comprehensive framework for evaluating multiple criteria simultaneously and enabling more informed and balanced decision-making.https://jme.shahroodut.ac.ir/article_3600_665f25c3cf68e34059c9c4957d93a9b5.pdfShahrood University of TechnologyJournal of Mining and Environment2251-859217420260701Automatic identification of salt dome geobody in 2D seismic data using metaheuristic-based clustering of textural attributes15291545369910.22044/jme.2025.16491.3224ENPoorandokhtSoltaniFaculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of TechnologyAminRoshandel KahooShahrood University of TechnologyHamidHassanpourFaculty of Computer Engineering, Shahrood University of TechnologyJournal Article20250709Seismic methods are among the primary and most effective techniques for hydrocarbon exploration, as they enable comprehensive imaging and interpretation of the Earth's subsurface. However, accurate interpretation of seismic data requires detailed analysis of geological structures, often involving complex and subjective decision-making processes. Constructing an initial geological model that aligns with seismic observations is a critical first step, but it is inherently non-unique and heavily influenced by the interpreter’s experience and preferences. Among various subsurface structures, salt domes are of particular interest due to their unique physical characteristics and their critical role in hydrocarbon entrapment, drilling risk management, and subsurface storage applications. Their distinct seismic textures, compared to surrounding sediments, make them identifiable using seismic texture attributes. Nevertheless, the manual delineation of salt dome geobody is a time-consuming and potentially error-prone task, especially given the volume, redundancy, and complexity of the seismic attributes used. To overcome these challenges, we propose a novel unsupervised framework for automatically identifying salt dome geobody in 2D seismic sections. The method begins by extracting a diverse set of seismic texture attributes, including both conventional attributes and novel texture descriptors derived from advanced image analysis techniques. Following attribute extraction, a attribute selection phase using techniques such as Laplacian Score is employed to eliminate redundant, irrelevant, or highly correlated attributes, thereby enhancing model efficiency and interpretability. The reduced set of relevant attributes is then used as input for clustering algorithms based on metaheuristic optimization techniques. These algorithms aim to partition the seismic data into meaningful clusters that correspond to geological attributes, particularly salt domes. Validation against multiple expert interpretations demonstrates the robustness and high accuracy of the proposed method. Results emphasize the capability of unsupervised clustering approaches especially those guided by metaheuristic strategies—in reducing interpretation uncertainty and improving segmentation quality.https://jme.shahroodut.ac.ir/article_3699_7afd92b3e984397b4b549fbde91b5577.pdf