Search Results (225)

Underground gas storage (UGS) in heterogeneous carbonate reservoirs is crucial for energy security but frequently faces wellbore instability challenges, which traditional static methods struggle to address due to dynamic full life cycle changes. This study systematically analyzes the dynamic evolution of wellbore stress in the Bs8 well (Qianmiqiao carbonate UGS) during drilling, acidizing, and injection-production operations, establishing a quantitative risk assessment model based on the Mohr–Coulomb criterion. Results indicate a significantly higher wellbore instability risk during drilling and initial gas injection stages, primarily manifested as shear failure, with greater severity observed in deeper well sections (e.g., 4277 m) due to higher in situ stresses. During acidizing, while the wellbore acid column pressure can reduce principal stress differences, the process also significantly weakens rock strength (e.g., by approximately 30%), inherently increasing the risk of wellbore instability, though the primary collapse mode remains shallow shear breakout. In the injection-production phase, increasing formation pressure is identified as the dominant factor, shifting the collapse mode from initial shallow shear failure to predominant wide shear collapse, notably at 90°/270° from the maximum horizontal stress direction, thereby significantly expanding the unstable zone. This dynamic assessment method provides crucial theoretical support for full life cycle integrity management and optimizing safe operation strategies for carbonate gas storage wells. Full article

Rock bolts are crucial components in the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low-light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium- to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments. Full article

Resource-exhausted cities have long played a key role in national energy development. Urban renewal projects, such as the renovation of old underground commercial spaces, can improve urban vitality and promote sustainable development. However, in resource-based cities, traditional industries dominate, while new industries such as modern commerce develop slowly. This results in low economic dynamism and weak motivation for urban development. To address this issue, we propose a systematic method for analyzing construction risks during the decision-making stage of renovation projects. The method includes three steps: risk value assessment, risk factor identification, and risk weight calculation. First, unlike previous studies that only used SWOT for risk factor analysis, we also applied it for project value assessment. Then, using the Work Breakdown Structure–Risk Breakdown Structure framework method (WBS-RBS), we identified specific risk sources by analyzing key construction technologies throughout the entire lifecycle of the renovation project. Finally, to enhance expert consensus, we proposed an improved Delphi–Analytic Hierarchy Process method (Delphi–AHP) to calculate risk indicator weights for different construction phases. The risk analysis covered all lifecycle stages of the renovation and upgrading project. The results show that in the Fushun city renovation case study, the established framework—consisting of five first-level indicators and twenty s-level indicators—enables analysis of renovation projects. Among these, management factors and human factors were identified as the most critical, with weights of 0.3608 and 0.2017, respectively. The proposed method provides a structured approach to evaluating renovation risks, taking into account the specific characteristics of construction work. This can serve as a useful reference for ensuring safe and efficient implementation of underground commercial space renovation projects in resource-exhausted cities. Full article

Occupational heat stress represents an increasing challenge to safety and operational performance in underground mining, where elevated temperatures, humidity, and limited ventilation are common. This study proposes an integrated framework to analyze the systemic impact of heat stress on human reliability in mining operations. We conducted a systematic literature review to identify empirical studies addressing thermal exposure, extracting key operational functions for modeling. These functions were structured using the Functional Resonance Analysis Method (FRAM) to reveal interdependencies and performance variability. Human reliability was evaluated using Fuzzy CREAM, which quantified the degree of contextual control associated with each function. Finally, we applied the Gaussian Analytic Hierarchy Process (AHP) to prioritize functions based on thermal impact, contextual reliability, and systemic connectivity. The results showed that functions involving subjective or complex judgment, such as assessing thermal stress or identifying psychophysiological indicators, exhibited lower reliability and higher vulnerability. In contrast, monitoring and control functions based on standardized procedures were more stable and resilient. This combined approach identified critical points of systemic fragility and offers a robust decision-support tool for prioritizing thermal risk mitigation. The findings contribute to advancing the scientific understanding of heat stress impacts in mining and support the development of targeted interventions to enhance human performance and safety in extreme environments. Full article

This study investigates ground deformations in the southeastern Po Plain (northern Italy), focusing on the Bologna area—a densely populated region affected by natural and anthropogenic subsidence. Ground deformations in the area result from geological processes (e.g., sediment compaction and tectonic activity) and human activities (e.g., ground water production and underground gas storage—UGS). We apply a multidisciplinary approach integrating subsurface geology, ground water production, advanced differential interferometry synthetic aperture radar—DInSAR, gas storage data, and land use information to characterize and analyze the spatial and temporal variations in vertical ground deformations. Seasonal and trend decomposition using loess (STL) and cluster analysis techniques are applied to historical DInSAR vertical time series, targeting three representatives areas close to the city of Bologna. The main contribution of the study is the attempt to correlate the lateral extension of ground water bodies with seasonal ground deformations and water production data; the results are validated via knowledge of the geological characteristics of the uppermost part of the Po Plain area. Distinct seasonal patterns are identified and correlated with ground water production withdrawal and UGS operations. The results highlight the influence of superficial aquifer characteristics—particularly the geometry, lateral extent, and hydraulic properties of sedimentary bodies—on the ground movements behavior. This case study outlines an effective multidisciplinary approach for subsidence characterization providing critical insights for risk assessment and mitigation strategies, relevant for the future development of CO₂ and hydrogen storage in depleted reservoirs and saline aquifers. Full article

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article

Under the frequent occurrence of urban waterlogging disasters globally, underground spaces, due to their unique environmental conditions and structural vulnerabilities, are facing growing flood pressure, resulting in substantial economic losses that hinder sustainable urban development. This study focused on a high-density urban area in China, investigating surface waterlogging conditions under rainfall characteristics as the primary driver of flooding. Focusing on the main nodes—entrances and exits—within the waterlogging disaster chain of underground garages, a risk assessment framework was constructed that encompasses three key dimensions: the attributes of extreme rainfall, the structural characteristics of entrances/exits, and emergency response capacities. Subsequently, a waterlogging risk assessment was conducted for selected underground garages in the study area under a 100-year return period extreme rainfall scenario. The results revealed that the flood depth at entrances/exits and the structural height of entrances/exits are the primary factors influencing flood risk in urban underground garages. Under this simulation scenario, 37.5% of the entrances and exits exhibited varying degrees of flood risk. The assessment framework and indicator system developed in this study provide valuable insights for flood risk evaluation in underground garage systems and offer decision-makers a more scientific and robust foundation for formulating improvement measures. Full article

This paper explores the implementation of battery electric vehicles (BEVs) in underground mining operations, focusing on their benefits, challenges, and safety considerations. The study examines the shift from traditional diesel-powered machinery to BEVs in response to increasing environmental concerns and stricter emission regulations. It discusses various lithium-ion battery chemistries used in BEVs, particularly lithium–iron–phosphate (LFP) and nickel–manganese–cobalt (NMC), comparing their performance, safety, and suitability for underground mining applications. The research highlights the significant benefits of BEVs, including reduced greenhouse gas emissions, improved air quality in confined spaces, and potential ventilation cost savings. However, it also addresses critical safety concerns, such as fire risks associated with lithium-ion batteries and the emission of toxic gases during thermal runaway events. The manuscript emphasises the importance of comprehensive risk assessment and mitigation strategies when introducing BEVs to underground mining environments. It concludes that while BEVs offer promising solutions for more sustainable and environmentally friendly mining operations, further research is needed to ensure their safe integration into underground mining practices. This study contributes valuable insights to the ongoing discussion on the future of mining technology and its environmental impact. Full article

Underground metro stations are integral to urban transit infrastructure, and ensuring their safety during fire emergencies is crucial. This study proposes a novel evacuation strategy for underground metro stations wherein a segment of evacuees descends to the platform level via train, while the remaining individuals evacuate upward to the ground level through station exits. A novel safety assessment methodology is established to evaluate fire evacuation efficacy, incorporating the cumulative effects of smoke, elevated temperatures, carbon dioxide, and reduced oxygen levels. Employing an actual underground metro station in Guangzhou, China, as a case study, fire and evacuation models were developed to compare the traditional upward evacuation method with the proposed partial downward evacuation strategy. The analysis reveals that both evacuation strategies are effective under the assessed fire scenario. However, the partial downward evacuation is completed more swiftly—in 385.5 s compared to 494.8 s for upward evacuation—thereby mitigating smoke inhalation risks, as the smoke height remains above the critical threshold of 1.8 m for a longer duration than observed in the upward evacuation scenario. Simulations further indicate that neither high temperatures nor carbon monoxide concentrations reach hazardous levels in either evacuation mode, ensuring evacuee safety. The study concludes that, with appropriate training arrangements and under specific fire and evacuation conditions, the partial downward evacuation strategy is safer and more efficient than upward evacuation. Full article

Mining involves daily descents underground and enduring dangerous and difficult conditions. Hence, it is very important to use solutions that will reduce the risk in miners’ work and ensure the greater safety and comfort of work in accordance with the goals of sustainable development. One way is training using virtual reality. Virtual reality provides greater safety (safe training conditions, the possibility of making a mistake without health consequences, practicing emergency scenarios, etc.) and aligns with the Sustainable Development Goals—particularly SDG 3 (health), SDG 8 (decent work), SDG 9 (innovation), and SDG 12 (sustainable production). However, it is also a technology that has its weaknesses (occurrence of contraindications, side effects, etc.). Therefore, the use of VR-based training should be examined in terms of the well-being and health of training employees. Due to this, this article examines the occurrence of psychophysical complaints during VR training; the tolerance and adequacy of the duration of a 50 min training session in VR was assessed; and the average time needed to adapt to the virtual environment was determined. The VR training was developed as a result of a research project conducted by JSW Nowe Projekty S.A. (ul. Ignacego Paderewskiego 41, 40-282 Katowice, Poland), Główny Instytut Górnictwa—Państwowy Instytut Badawczy (plac Gwarków 1, 40-160 Katowice, Poland), JSW Szkolenie i Górnictwo Sp. z o.o. at Jastrzębska Spółka Węglowa Capital Group (ul. Górnicza 1, 44-335 Jastrzębie-Zdrój, Poland) on the development and implementation of innovative training using VR for miners. The solution was developed in the context of mining’s striving for sustainable development in the area of improving working conditions and human safety. The first method used in the study is a survey completed by participants of training courses using virtual reality. The second method is the analysis of trainer observation sheets, which contain observations from training courses. The results revealed that for over 70% of respondents, the need to carry out activities in VR was not associated with fatigue. No average score for psychophysical symptoms assessed by respondents on a scale of 1 to 6 (including disorientation, blurred vision, dizziness, confusion, etc.) exceeded 1.4. The vast majority (85.5%) did not take off the goggles before the end of the training—the training lasted 50 min. This research contributes to the discussion on sustainable industrial transformation by demonstrating that VR training not only improves worker safety and preparedness but also supports development goals through human-centered innovation in the mining sector. Full article

The artificial ground freezing method (AGF) is widely used in underground construction to reinforce the ground and ensure construction safety. This study systematically evaluates the implementation of the artificial ground freezing method in the construction of a metro tunnel cross-passage, with a focus on analyzing the soil’s thermo-mechanical behavior and assessing safety performance throughout the construction process. A combined approach integrating field monitoring and refined three-dimensional numerical simulation using FLAC3D is adopted, considering critical factors, such as freezing pipe inclination, thermo-mechanical coupling, and ice–water phase transitions. Both field data and simulation results demonstrate that increasing the density of freezing pipes accelerates temperature reduction and intensifies frost heave-induced displacements near the pipes. After 45 days of active freezing, the freezing curtain reaches a thickness of 3.7 m with an average temperature below −10 °C. Extending the freezing duration beyond this period yields negligible improvement in curtain performance. Frost heave deformation develops rapidly during the initial phase and stabilizes after approximately 25 days, with maximum vertical displacements reaching 12 cm. Significant stress concentrations occur in the soil adjacent to the freezing pipes, with shield tunnel segments experiencing up to 5 MPa of stress. Thaw settlement is primarily concentrated in areas previously affected by frost heave, with a maximum settlement of 3 cm. Even after 45 days of natural thawing, a frozen curtain approximately 3.3 m thick remains intact, maintaining sufficient structural strength. The refined numerical model accurately captures the mechanical response of soil during the freezing and thawing processes under realistic engineering conditions, with field monitoring data validating its effectiveness. This research provides valuable guidance for managing construction risks and ensuring safety in similar cross-passage and cross-river tunnel projects, with broader implications for underground engineering requiring precise control of frost heave and thaw settlement. Full article

Geometallurgy is an approach that utilizes predictive models that can support business decisions, mitigate risks, and enhance production efficiency. To develop an accurate geometallurgical model, it is essential to understand the behavior of each lithology within the ore body through geometallurgical testing. In this context, the present study aims to evaluate the performance of bench-scale tests conducted on the main mining fronts of a zinc mine operation located in Brazil. The mineral processing plant was designed to process lead and zinc sulfide ores without material stockpiling, where all ores extracted from the underground mine are immediately processed. The geometallurgical characterization was conducted through the following steps: sampling, crushing, grinding, and flotation. The recovery, concentrate, and tailing contents during the flotation stages of galena and sphalerite were analyzed. A mineralogical characterization using a Mineral Liberation Analyzer (MLA) was performed to assess the degree of particle liberation and mineral associations within the studied mining fronts. The results indicate that a higher degree of pyrite liberation leads to greater metallurgical recovery of mineralized bodies A (breccia-hosted orebody), B (sphalerite-rich doloarenite orebody), and C (upper replaced stratiform orebody). Among these, mineralized body C presents the highest recovery in the zinc and lead stages, with 99.5% and 86.2%, respectively. Full article

Internal soil erosion caused by water infiltration around defective buried pipes poses a significant threat to the long-term stability of underground infrastructures such as pipelines and highway culverts. This study employs a coupled computational fluid dynamics–discrete element method (CFD–DEM) framework to simulate the detachment, transport, and redistribution of soil particles under varying infiltration pressures and pipe defect geometries. Using ANSYS Fluent (CFD) and Rocky (DEM), the simulation resolves both the fluid flow field and granular particle dynamics, capturing erosion cavity formation, void evolution, and soil particle transport in three dimensions. The results reveal that increased infiltration pressure and defect size in the buried pipe significantly accelerate the process of erosion and sinkhole formation, leading to potentially unstable subsurface conditions. Visualization of particle migration, sinkhole development, and soil velocity distributions provides insight into the mechanisms driving localized failure. The findings highlight the importance of considering fluid–particle interactions and defect characteristics in the design and maintenance of buried structures, offering a predictive basis for assessing erosion risk and infrastructure vulnerability. Full article

Karst aquifer systems are highly vulnerable due to their unique underground water flow characteristics, making them prone to contamination and abandonment. This study compares an active karst water source (Ljubija) with a previously abandoned one (Rečica) to assess freshwater quality and water protection risks, especially as water scarcity becomes a concern during dry summer periods. The Ljubija and Rečica catchments, designated as water protection areas (WPAs), were monitored over a year (January–December 2020). Groundwater (GW) and surface water (SW) were analyzed twice a month during both dry and wet periods, adhering to European and national guidelines. An interdisciplinary approach integrated natural and human impact indicators, linking water quality to precipitation, hydrogeography, and landscape characteristics. After Slovene regulation standards (50 mg/L), the Ljubija source demonstrated stable water quality, with low nitrate levels (average 2.6 mg/L) and minimal human impact. In contrast, the Rečica catchment was more vulnerable, with its GW excluded from drinking use since the 1990s due to organic contamination, worsened by the area’s karst hydrogeology. In 2020, its nitrate concentration averaged 6.0 mg/L. These findings highlight the need for improved monitoring regulations, particularly for vulnerable karst water sources, to safeguard water quality and ensure sustainable use. Full article

To ensure the green, safe, and efficient extraction of mineral resources and promote sustainability, the stability of mined-out areas has become a critical factor affecting safe production and ecological restoration in underground metal mines. The instability of underground goafs poses a significant threat to mine safety, especially when irregular excavation patterns interact with high ground stress, exacerbating instability risks. Most existing studies lack a systematic and multidisciplinary integrated framework for comprehensive evaluation and management. This paper proposes a trinity research system of “assessment–optimization–governance”, integrating theoretical analysis, three-dimensional fluid–solid coupling numerical simulation, and a filling sequence optimization method based on genetic algorithms. An analysis of data measured from 243 pillars and 49 goafs indicates that approximately 20–30% of the pillars have a factor of safety (FoS) below 1.0, signaling immediate instability risks; additionally, 58% do not meet the threshold for long-term stability (FoS ≥ 1.5). Statistical and spatial analyses highlight that pillar width-to-height ratio (W/H) and cross-sectional area significantly influence stability; when W/H exceeds 1.5, FoS typically surpasses 2.0. Numerical simulations reveal pore water pressures of 1.4–1.8 MPa in deeper goafs, substantially reducing effective stress and accelerating plastic zone expansion. Stability classification categorizes the 49 goafs into 7 “poor”, 37 “moderate”, and 5 “good” zones. A genetic algorithm-optimized filling sequence prioritizes high-risk area remediation, reducing maximum principal stress by 60.96% and pore pressure by 28.6%. Cemented waste rock filling applied in high-risk areas, complemented by general waste rock filling in moderate-risk areas, significantly enhances overall stability. This integrated method provides a scientific foundation for stability assessment and dynamic remediation planning under complex hydrogeological conditions, offering a risk-informed and scenario-specific application of existing tools that improves engineering applicability. Full article