Near-seam open-pit bench blasting in conglomerate-bearing overburden must break the rock mass while limiting coal-roof damage. Peak stress is often used as a screening variable, but it cannot describe the pressure-dependent response of coal. This study treats coal-roof damage as a stress-path problem using a two-dimensional LS-DYNA model with a Johnson–Holmquist-II coal model. Eight controlled cases varied charge configuration and the position, thickness and orientation of a conglomerate band within a field-based modelling envelope. Damage was dominated by the overburden in all cases: the overburden damage area was 28.9–35.3 m 2 , whereas coal-seam damage remained below 1 m 2 . Peak compressive Y-stress at the coal roof did not predict the coal response. The three highest-compression cases (118–159 MPa) produced the least coal damage (0–0.31 m 2 ), whereas lower-compression cases produced 0.74–0.99 m 2 . The stress histories resolved this mismatch. A continuous charge kept the coal roof in confined compression and suppressed damage. The double-deck case produced rapid unloading with a brief negative-pressure excursion. Inclined and vertical conglomerate bands drove low-confinement deviatoric paths. Stress-path diagnosis can screen coal-roof risk before field trials, but fragmentation remains a separate acceptance criterion.
Metal streaming has become an important alternative mining finance mechanism, yet comparative evidence remains limited on how streaming investors can be evaluated when firm-level financial strength, contractual structure, and host-country project exposure are considered simultaneously. This study develops and applies a general multicriteria assessment framework to evaluate metal streaming investors, using documented exposure to Peruvian mining projects as an empirical case of analysis. The framework integrates ordinal rubrics (1–5), Analytic Hierarchy Process weights, normalized composite indices (0–100), and the general electric–McKinsey matrix. Competitiveness was evaluated through financial strength, experience and network breadth, and contractual efficiency, while attractiveness was assessed through reputation and brand risk, economic performance, and growth and strategy factors. The results show a polarized strategic structure. Wheaton Precious Metals achieved the strongest overall position, while Franco-Nevada Corporation and Triple Flag Precious Metals also showed favorable profiles supported by greater capital scale, diversified portfolios, resilient margins, and strong counterparties. In contrast, Empress Royalty and especially RIVI Capital LLC ranked lower due to weaker liquidity, limited contractual diversification, and lower revenue sustainability. Sensitivity analysis (±20%) suggested local stability of the ranking under moderate changes in factor weights. The study provides a replicable framework and extends mining finance literature.
This paper explores how Greece can enhance its geopolitical role and potential by strategically managing its mineral and legacy mining resources. An artificial intelligence (AI)-powered methodology, based on the fuzzy analytical hierarchy process (FAHP), is applied to evaluate national strategies. The decision-making assessment was conducted by properly simulated domain-specific artificial intelligence agents (AGs), integrated with multilayer perceptrons (MLPs) architecture and generative pre-trained transformer (GPT) analytics. The simulation results were validated by human experts. Three alternative geopolitical strategies were evaluated, of which a hybrid strategy that combines strong regulatory frameworks and innovation with active participation in EU and regional initiatives was shown to be the most geopolitically advantageous and sustainable. It offers a synergistic, multilaterally stabilized model for the exploitation of Greece's mineral resources, along with geopolitical benefits. It is also demonstrated how AI can support complex decision-making problems in natural resource geopolitics, offering a replicable and effective model for sustainable strategy selection and development in post-mining global contexts and dynamics.
Occupational safety in open-pit mining requires tools capable of anticipating risk changes before accidents occur. This study developed a predictive framework for safety risk management in an open-pit gold mine in Peru by integrating Bird's pyramid, Bayesian inference and Markov chains using daily reports of substandard acts and conditions. Events were classified by severity, risk probabilities were estimated from cause-consequence relationships, a daily risk index was constructed and temporal transitions were modelled through a first-order Markov chain. The results showed a structure dominated by precursor events, with 15,010 substandard acts and conditions, 42 incidents and 4 minor accidents. Bayesian inference indicated that medium risk was predominant, representing 57.56% of observations, while the combination 'unauthorised spills + environmental impact' reached a posterior high-risk probability of 1.000. The Markov matrix showed moderate persistence of the high-risk state, with a transition probability of 0.4144. Short-term forecasting identified the low-risk state as the most likely outcome, although the probability of high risk increased progressively. The proposed framework transforms daily preventive reports into probabilistic information to prioritise controls and strengthen preventive risk management.
This article presents an orepass geotechnical rating evaluator (OGRE) developed to help geotechnical and mine engineers assess the geotechnical risks of proposed orepass locations. Orepasses (near-vertical excavations that transport ore by gravity) are integral to many underground stope mining methods that rely on centralized haulage levels. Despite their importance, orepass designs are often not finalized during planning stages, and their longevity can be compromised by geotechnical conditions. The evaluation method presented is adapted from an existing orepass longevity calculator. Operational considerations, such as material size, blasting practices, cushion guidelines, and support or liner use, were removed, while new geotechnical parameters (sulfide-generating potential, orientation of finger raises, intersection angle of finger raises, and rock quality clarifications) were incorporated based on insights gathered from a survey on orepass planning, design, and construction practices in Canada and the United States. This allows the OGRE to consider stress regime, rock quality, geological structures, sulfide-generating potential, orepass orientation, and finger raise or knuckle use. The resulting tool enables comparison of orepass location options based on geotechnical risk. The value of each of the six aforementioned parameters individually ranges from as low as 0.05 up to 1.00, and they are multiplied together to produce a final rating between approximately 0 and 1. If an orepass location option has a number closer to 1.00, it would indicate less geotechnical risk. Furthermore, this article also identifies methods of managing geotechnical risk through design modifications for orepass locations identified as high risk.
The supply chain plays a critical role in the resource industry due to its strategic position in the upstream sector of various industries. This importance spans resource optimisation, cost reduction, quality assurance, social responsibility and sustainability. The concept of supply chain resilience emerged around two decades ago, prompting extensive research on assessing and enhancing resilience. Evaluating resilience requires specific performance indicators, which in turn depend on identifying the key elements of a resilient supply chain. Despite this, comprehensive studies on determining these elements within natural resource supply chains are limited. This article conducts a state-of-the-art review to examine resilience performance indicators, with particular focus on the mining industry as a representative case of resource supply chains. By delineating the current boundaries of knowledge, the study identifies seven key factors contributing to supply chain resilience and extracts 40 performance indicators from the literature. Foundational concepts, including natural resource supply chain management, resilience and the main influencing factors, are presented in a systematic hierarchical framework, enhancing both clarity and practical relevance. The identified factors and performance indicators provide a structured foundation for future research and offer managers actionable insights to effectively assess and improve resilience in various resource supply chains.
High in-situ stress is a primary driver of coalburst-related engineering failures in deep coal mining, and borehole destressing is widely applied as a practical mitigation measure. Coalbursts represent a stress-driven engineering failure caused by excessive accumulation of elastic strain energy in coal seams. However, many existing studies describe stress relief qualitatively or rely on geometric indicators that do not explicitly characterise failure initiation, failure-zone evolution, or failure-suppression capacity under varying geological and stress conditions. This study develops a two-dimensional plane-strain numerical modelling framework to quantitatively investigate borehole-induced stress redistribution, failure development, and destress radius in high-stress coal seams. The model is verified against published field observations by comparing the destress radius across multiple borehole diameters, showing good agreement within acceptable engineering error limits. A systematic parametric analysis is conducted to evaluate the influence of borehole diameter, coal seam strength, burial depth, horizontal-to-vertical stress ratio, and seam dip angle on stress redistribution and the evolution of the failure zone. Results demonstrate that borehole diameter exerts the strongest control on vertical stress unloading, failure initiation and outward propagation of the failure zone, whereas horizontal stress response is comparatively less sensitive. Higher stress magnitude and lower coal strength accelerate failure development and enlarge the stress-relief zone, whereas increasing stress ratio and seam dip create asymmetric failure methods that reduce effective relief in fundamental directions. From an energy-based perspective, the observed reduction in stress concentration corresponds to a decrease in stored strain energy surrounding the borehole, indicating suppression of coalburst-related failure mechanisms. The findings provide design-relevant guidance for borehole destressing as a failure-mitigation strategy in high-stress coal mine layouts, where control of failure extent and burst potential is a primary engineering objective.
This article aims to investigate the use of plurigaussian simulation as a practical tool to quantify geological uncertainty and internal dilution risk in a nickel laterite deposit. In laterite operations, sharp and highly variable contacts between weathering layers may directly affect selective mining, ore control, and the definition of ore and waste volumes. The proposed workflow integrates weathering surface modelling, unfolding, vertical proportion curves, lithotype rule construction and conditional plurigaussian simulation to reproduce the vertical organisation and lateral variability of the lateritic profile and to convert geological uncertainty into operational risk indicators. The methodology was applied to the Brejo Seco Deposit, a nickel laterite deposit developed over a mafic-ultramafic complex in Brazil. A database comprising drillholes and composite intervals was grouped into four main weathering layers: limonite, saprolite, boulder and bedrock. Ten equiprobable plurigaussian realisations were generated and validated through comparisons with original layer proportions, vertical proportion curves and vertical sections. The simulation results were then used to calculate node-based layer probabilities, generate accumulated two-dimensional risk maps, and estimate potential dilution volumes. The results show that unfolding improved the spatial continuity of the categorical variables and that plurigaussian simulation was effective at distinguishing areas where the deterministic geological model is strongly supported from sectors with higher contact uncertainty. For the limonite horizon, the estimated local dilution risk reached up to 90,000 t, demonstrating that deterministic modelling uncertainty may significantly impact mining performance. The proposed workflow provides a practical basis for identifying priority areas for model review, infill drilling, and geological control, and constitutes a useful decision-support tool for resource modelling and dilution risk management in nickel laterite mining.
Control over extraction and destination of waste can be just as important as it is for ore. Waste haulage is a major operating cost, especially as open-pit mines deepen and waste dumps expand, increasing strip ratios and haul distances. Moreover, potentially acid-generating (PAG) waste rock can lead to substantial rehabilitation liabilities through acid rock drainage (ARD). Progressive reclamation can be integrated into production scheduling optimisation to reduce PAG exposure during operations, thereby mitigating ARD and lowering environmental risk. Furthermore, traditional production planning approaches overlook geological uncertainty, limiting their ability to generate schedules robust to waste misclassification and metal grade variability. This study incorporates haulage management into a simultaneous stochastic optimisation framework for mining complexes to create a production schedule and waste placement plan that reduces haulage costs and mitigates ARD risk through progressive reclamation. A case study at a copper-gold mining complex compares performance when waste placement is optimised sequentially rather than simultaneously. The simultaneous case deferred waste haulage costs, reducing them by 38.7% in the first year, contributing to an 8.3% increase in net present value. At the end of operation, the sequential optimisation failed to encapsulate 16% of total PAG material extracted, compared with 22% in the simultaneous case.
This study investigates the influence of ore particle size on gravity-driven flow behaviour and dilution at the drawpoint in sublevel caving operations using a particle-based modelling framework implemented in the Blender rigid body physics engine. Two representative case studies with non-uniform particle-size distributions are analysed: the Ernest Henry Mine, supported by ring marker trial data, and the DeGagn & eacute; dilution curve derived from discrete element simulations calibrated against Ridgeway mine data. For both case studies, a systematic calibration procedure is conducted to identify suitable modelling configurations and evaluate the sensitivity of key physics-engine parameters. Half-geometry simulations are adopted to overcome hardware limitations. The results show that particle linear damping is the dominant parameter controlling flow behaviour, while particle friction and geometry also significantly influence flow responses. Simulations using purely spherical particles underestimate inter-particle contact effects and produce less realistic behaviour. The most representative results are obtained using lower damping for ore relative to waste particles and non-spherical dodecahedral particles. Following calibration, increasing ore particle size by 10% and 20% leads to higher dilution and lower ore recovery, particularly at intermediate extraction levels, due to reduced ore mobility and earlier waste breakthrough.
Occupational heat stress in underground mines is a growing global concern. Miners working in such hot and humid conditions face a significant occupational health risk, affecting their health and productivity. Therefore, appropriate heat-stress management techniques are necessary. This study presents a systematic field monitoring of thermal environmental conditions in two mechanised underground metalliferous mines in India. Field measurements revealed high temperatures, elevated relative humidity, and inadequate airflow across various working areas in the investigated mines. The obtained parameters were also systematically compared against thermal exposure limits prescribed by national and international regulatory authorities and occupational health and safety organisations. The wet bulb globe temperature and effective temperature measurements showed repeated exceedances of the threshold limit. However, the wet bulb temperature values, assessed according to the Indian Standard, indicated that the miners were not exposed to heat stress. These findings collectively advocate adopting a comprehensive thermal index as the basis for mandatory heat-stress monitoring and call upon policymakers, statutory bodies and mining operators to reform existing regulatory frameworks to ensure scientifically robust and operationally effective heat-stress management in Indian underground metalliferous mines.
Compressed air handheld rockdrills have historically enabled efficient blasthole drilling in South African narrow reef gold mines. However, as operations matured and production moved further from shafts, their efficiency declined. Rising electricity tariffs have further reduced profitability, necessitating the adoption of alternative drilling technologies. Hydropower rockdrills present a viable solution, yet adoption has been limited due to uncertainty regarding their economic benefits. This study provides an impartial techno-economic assessment of two hydropower configurations, micro and localised systems, evaluated over 12 months against a compressed air baseline.Although compressed air systems require no additional capital, their high operating cost (US $14.23/t) makes them the least economical option. The localised hydropower system demonstrates clear advantages, with 47% lower capital cost than micro systems and the lowest operating cost (US $2.41/t). Over a 5-year period, the total cost of ownership is significantly reduced (US $215,700 vs US $983,520 for compressed air), yielding savings of approximately US $767,820. Productivity improvements are also notable, with localised systems achieving 1693 t/month and a superior ROI of 323%, with payback within one month.Overall, localised hydropower systems offer the most cost-effective, productive, and safer alternative to compressed air drilling.
The primary objective of modern mining endeavours in the twenty-first century is to safely and efficiently extract as much or as possible. Unstable slopes can result in fatalities and property damage, maintaining the stability of rock for economic sustainability and safety. This work innovative Xavier initialisation-based convolutional neural network (XI-CNN)-based model for detecting slope failures in mining operations. At first, the slope data is pre-processed in outlier removal using z-score, nominalisation, and normalisation using Min-Max. Then, the up-sampling is performed to improve the minority classes in the pre-processed data using adaptive synthetic sampling. After that, the slope features are extracted. Following this, important features are selected using good the bad and the ugly optimization to improve the classifier by reducing the dimensionality of the features. Lastly, the trained XI-CNN is fed the chosen features to classify the slope's stability. The proposed model is compared and analysed ##with the prevailing models and this demonstrates the higher detection accuracy (0.95) of the slope stability.
At the early stage of mine feasibility assessment, decision-making is constrained by high geological uncertainty despite the need for rapid evaluation of economic viability. This study proposes a morphological texture-based classification framework as a screening tool for early-stage mine design. Distinct textural variants within a single ore deposit are classified using field and laboratory data and evaluated across slope stability, excavation energy, and ore grade. A multi-criteria decision analysis (MCDA) approach is applied to quantify trade-offs among these factors. Results show that weakly banded ores, such as deposition type 1 (DT), are easier to excavate but less stable, whereas competent units, such as DT6 and DT4, allow steeper, more stable slopes. The intermediate unit, as in DT5, exhibits higher grades but moderate constraints. MCDA ranking identifies DT6 as the most balanced unit. The framework demonstrates that morphological texture can effectively link geology with engineering and economic considerations, providing early-stage guidance for slope design and prioritisation under uncertainty.
The determination of uniaxial compressive strength (UCS) of cemented rock fill (CRF) in underground mining commonly depends on laboratory testing and curing periods, which delay mix-design decisions and quality control under operational constraints. This study evaluated and compared machine learning models combined with SHapley Additive exPlanations (SHAP) to predict CRF UCS at an underground mine in Peru. The experimental dataset included mix-design variables such as cement (C), water (W), waste rock (WR), screened aggregate (SCR) and derived parameters including cement content (C%), water-to-cement ratio (w/c), waste-rock-to-cement ratio (WR/c) and curing ages of 7, 14 and 28 days. Data preprocessing involved interquartile range capping and Yeo-Johnson transformation, followed by a 70/30 train-test split (n = 105/45). Five machine learning models NGBoost, Explainable Boosting Machine (EBM), CatBoost, LightGBM and Extra Trees were trained and optimised using 5-fold cross-validation. Among them, EBM achieved the best predictive performance on the test set (R2 = 0.98, RMSE = 0.07, MAE = 0.045, MAPE = 2.59%, sMAPE = 2.61%, VAF = 98.31%). SHAP analysis identified curing age as the most influential predictor. Overall, the proposed approach provides an accurate and interpretable tool for optimising CRF mix design and quality control.
In neutral in-situ leaching (CO2 + O2) of sandstone-hosted uranium deposits, oxygen injection, lixiviant injection, and hydrochemical conditions are mutually coupled, leading to nonlinear responses of daily uranium output to operating parameters. Meanwhile, field monitoring data are also noisy and incomplete, limiting empirical optimization. This study focuses on seven-spot leaching units in a mining area, constructs a daily-scale dataset including injection/production parameters, hydrochemical indicators of the produced solution, residual uranium inventory, and time-series derived features, and develops a multilayer perceptron prediction model (VAE-SEMLP) that integrates a variational autoencoder (VAE) with a Squeeze-and-Excitation (SE) attention mechanism. Across three representative unit tests, the proposed model achieves an average R2 of 0.95 and a normalized RMSE of 0.06, outperforming support vector regression (SVR), random forest (RF), and XGBoost overall. Ablation experiments further confirm the synergistic gain from combining the VAE and SE modules. Parameter scanning under typical static operating-condition slices reveals a pronounced unimodal response of daily uranium output to lixiviant injection volume, indicating an optimal injection interval that shifts with oxygen-injection level. This work provides data-driven support for daily uranium-output prediction and quantitative optimization of coupled oxygen-lixiviant injection schemes in neutral in-situ uranium leaching.
This study compares the accuracy of empirical and regression models for predicting the size distribution of blasted material at drawpoints in sublevel caving. Field data were sourced from Ernest Henry mine (EHM). At EHM, full-scale blasting trials were conducted under controlled conditions by varying explosive density and burden size, with fragmentation measured using laser scanning at different extraction tonnages. To minimise scanning inaccuracies, scan results were combined to represent the particle-size distribution for the EHM data. Five models were evaluated: Kuz-Ram, extended Kuz-Ram, Two Component Model (TCM), Kuznetsov-Cunningham-Ouchterlony, and a regression-based Underground Ring Blasting Model (URBM) developed in our previous work. Models were assessed for predicting P20, P50, and P80 passing sizes. Results show the extended Kuz-Ram and TCM perform better for finer fragment sizes, with Mean Absolute Percentage Error (MAPE) of 8%. URBM was the most accurate for median and coarse sizes (MAPE 5% for P50 and 3% for P80) and showed the least variability in errors. A SHapley Additive exPlanations-based sensitivity analysis of the three most accurate models identified key variables for median size prediction, highlighting the importance of rock properties and ring design parameters.
Mining tyres, exceeding 4 m in diameter with operational lifespans of approximately seven months, present significant environmental and operational challenges through considerable waste accumulation. While substantial research addresses conventional tyre recycling, end-of-life mining tyres (ELMTs) require specialised management strategies due to their unique composition and operational constraints. This study develops a decision framework to assess and prioritise ELMT management solutions within Chile's mining sector, the world's leading copper producer. The hybrid methodology integrates Rogers' Technology Adoption Model with Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), utilising data from 17 mining industry experts. Results indicate that while pyrolysis demonstrates the highest adoption potential, retreading and shredding exhibit superior environmental performance. This choice is primarily driven by compatibility considerations, particularly operational safety and continuity, constituting approximately 52% of selection criterion weight. The findings highlight a critical trade-off between ecological benefits and operational dependability in mining environments, providing valuable insights for mining companies implementing circular economy practices, recycling technology suppliers and policymakers developing targeted mining waste management regulations.
To address multipath interference, sparse echoes, and motion distortion of millimetre-wave radar point clouds in underground unstructured environments, this paper proposes a tightly coupled millimetre-wave radar-inertial measurement unit (IMU) simultaneous localisation and mapping (SLAM) system for quadrotor unmanned aerial vehicles (UAVs), enabling real-time 3D reconstruction and high-precision pose estimation in complex roadways. Three-level point cloud pre-processing (statistical outlier removal [SOR], voxel grid downsampling [VGD], and density-based spatial clustering of applications with noise [DBSCAN]) is adopted to effectively suppress radar clutter and multipath interference. Within a sliding window optimisation framework, we build a tightly coupled graph-based SLAM system integrating radar point cloud registration error and IMU pre-integration constraints, and design an IMU pre-integration-based undistortion algorithm to correct radar point cloud motion distortion. Simulations show that in strong multipath environments, the proposed method reduces ATE RMSE by 35%-55% and RPE by 38% compared with LC-SLAM and IMU-only methods, with terminal drift below 0.3%. Undistortion processing cuts peak RMSE by 45% and lowers local median error to 0.016 m. The SLAM system costs 70-100 ms per frame, with the undistortion module taking 21-25 ms per frame, satisfying the 15 Hz radar real-time requirement. This method provides algorithmic support and engineering reference for positioning and 3D modelling in underground unstructured spaces.
As fuel consumption is a major operating cost in open-pit mining, identifying and quantifying controllable inefficiencies requires continuous monitoring under real production conditions. This study presents a comprehensive analysis of haul truck fuel consumption using an integrated onboard monitoring system installed on a Komatsu 785 truck at a copper mine. The system continuously measured fuel flow, payload, position and speed over 90 days and captured 1780 complete haul cycles over 150 shifts under varying operational, environmental and behavioural conditions. Specific fuel consumption (SFC) analysis revealed that road gradient was the dominant factor, with uphill waste-haul routes consuming 75-80 gr/t.km compared to 65-70 gr/t.km on crusher routes. An optimal payload range of 92-97 tonnes minimised the SFC, while both under-loading and over-loading reduced fuel efficiency. Spatial mapping identified specific high-consumption road segments that required maintenance. Driver behaviour significantly affected fuel use, with aggressive driving increasing the SFC by approximately 7-10% (75-77 gr/t.km) compared to normal driver behaviour (70-72 gr/t.km). Rainfall events increased SFC by 2-3 gr/t.km above the baseline due to elevated rolling resistance. Operational delays, particularly loading queues (45% of total idle time), contributed substantially to unproductive fuel consumption. The findings demonstrated that fuel inefficiencies were largely controllable through targeted operational improvements, including payload optimisation, road maintenance, driver training and dispatch co-ordination. The spatial mapping methodology provided a practical diagnostic tool transferable to other mine sites for identifying energy-intensive haulage segments and prioritising fuel-reduction interventions.
