Underground Environment Research Articles

Creep behavior of BFRP-confined coal gangue concrete under sulfate and high stress conditions

To recycle the coal gangue more economically and effectively, the basalt fiber reinforced polymer (BFRP) and coal gangue concrete (CGC) are combined to form a novel supporting structure in the underground mines, BFRP-confined CGC (FCGC). However, the underground mine environment is rich in sulfate corrosive ions, and meanwhile, the structure is subjected to a high sustained load. Thus, this paper presents an experimental and theoretical investigation on the long-term deformation properties of FCGC under sulfate and high stress conditions. The effects of inner concrete, FRP layer, stress-to-strength ratio, sulfate concentration and load duration time on the shrinkage and creep behavior were clarified in detail. The findings indicate that the shrinkage of FCGC is relatively lower as the BFRP confinement weakens the moisture exchange between inner CGC and external environment. In addition, under sulfate and high stress conditions, the creep strain and specific creep increase continuously in the early stage, while the growth rate gradually slows down in the later stage, where the high stress-to-strength ratio is the key factor. Moreover, when compared with a single high stress condition, the creep strain of FCGC is relatively larger under sulfate and high stress coupling conditions, attributed to the sulfate corrosion on the BFRP wraps. Theoretically, the ACI 209 was modified to predict the nonlinear creep of CGC, by introducing the coal gangue influence coefficient and the increasing coefficient of nonlinear creep. Furthermore, based on the creep model of CGC and BFRP, the nonlinear calculation model of FCGC was developed considering the creep of CGC under a triaxial-stress state and the interaction between inner CGC and BFRP confinement. Validations against the experimental results show that the nonlinear creep model of FCGC works well.

Experimental and numerical investigation of seepage erosion in sandy cobbles under coupling hydraulic and dynamic load

The internal structure of sandy cobbles strata is sensitive to disturbances in the urban underground environment, but the structural evolution process under coupling hydraulic and dynamic loads remains unexplored. This paper presents a detailed investigation into the migration patterns and mechanisms of fine particles in sandy cobbles induced by coupled hydraulic and dynamic loading. A sandy cobble specimen with a typical particle size distribution (PSD) was designed and tested using an apparatus that included a constant inlet water head control system and an eccentric-vibrator-based dynamic loading system. Based on physical modeling tests, a numerical model was constructed to replicate the internal structural evolution under hydraulic and dynamic loading by calibrating the time history of local permeability. The test results indicate that the application of dynamic load can instantly disrupt the stable internal structure of sandy cobbles under static seepage, imparting kinetic energy to fine particles that detach from the skeleton structure and migrate along the seepage direction. Significant fine particle loss occurs near the seepage outlet, but due to energy loss during migration, fine particles far from the seepage outlet are recaptured by the skeleton pore throats and clogged again in the migration path. As the intensity of the dynamic loading increases, the migration path for fine particles becomes longer, and the amount and size of fine particles lost significantly increase. The changes in the internal structure of the soil are reflected in hydraulic parameters as a transient increase in local flow velocity, an increase in local pore water pressure due to clogging, and a decrease in the overall permeability coefficient with the loss of fine particles. The findings of this study will be useful for future geotechnical engineering design and analysis.

IOT Based Mining, Tracking and Worker Safety Helmet

Abstract: This project addresses a cost-effective, flexible solution of underground mine workers safety. A module of MEMS and temperature-based sensors are used for underground environment monitoring and automating progression of measurement data through IOT communication technique is proposed with high accuracy, smooth control and reliability. Arduino microcontroller is used for collecting data and making decision, based on which the mine worker is informed through server. Sensor data transforms into digital signal and effectively communicate wirelessly with the ground control centre computer.

Internet of Things Long-Range-Wide-Area-Network-Based Wireless Sensors Network for Underground Mine Monitoring: Planning an Efficient, Safe, and Sustainable Labor Environment.

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions.

Study on the Identification Method of Planar Geological Structures in Coal Mines Using Ground-Penetrating Radar

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations.

Research on visual features and identification of coal and gangue in simulated underground mine environments

ABSTRACT In underground mine environments, factors such as low light intensity, diffusion of dust particles, and coal slurry coverage interfere with coal gangue identification. To further realize coal-gangue sorting underground, we analyzed the coal-gangue visual characteristics coupled with the above factors. The analysis reveals that both smoke environments and coal slurry coverage tend to make the coal-gangue visual features more consistent. There are still differences in coal-gangue visual features under smoke environments, while coal slurry coverage leads to gangue exhibiting coal-related feature information. To improve the detection capability of coal gangue in underground environments, this study adopts a coal gangue recognition method based on the DG module and YOLOX-PSB network: Utilize the DG module to achieve dehazing and then use the YOLOX-PSB model for object detection. The proposed method was validated through ablation experiments, and the results showed that the detection accuracy reached 99.6%, with an FPS of 98. The proposed method enables real-time detection and exhibits the best robustness in complex environments, realizing reliable and efficient performance.

Research on High-Frequency Isolated NPC Three-Level Inverter for Frequency Conversion and Speed Regulation

Mining frequency converters are the primary means for achieving variable frequency speed regulation of electromechanical equipment in coal mines, offering energy-saving benefits for coal mining enterprises. The common power supply method involves converting high voltage to low voltage using power frequency transformers before supplying equipment. However, this integration of power frequency transformers with supply devices occupies significant space, making it unsuitable for confined underground environments. Additionally, they suffer from poor output waveform quality and high harmonic content. To tackle these challenges, this paper presents a three-stage topology for high-frequency isolated frequency conversion and speed regulation, utilizing three-phase uncontrolled rectification, a single active isolated DC/DC converter, and an NPC three-level inverter. The control strategies for each stage are discussed in detail. Simulations and experimental results confirm the validity and feasibility of the proposed design, demonstrating enhanced stability and dynamic performance of the three-stage high-frequency isolated frequency converter.

Underground rescue path planning based on a comprehensive risk assessment approach

Fire incidents in underground environments, such as subway stations and shopping malls, pose significant hazards due to restricted ventilation and confined spaces. These conditions complicate rescue operations, particularly given the unpredictable nature of fires. Effective integration of fire risk assessment into rescue path planning is essential for ensuring both safety and operational efficiency. However, fire risk is inherently complex, varying across both temporal and spatial dimensions, and accurate assessment depends on precise fire situation inference. Despite advancements in fire simulation technologies, inconsistencies in geometric structures between computational units limit seamless integration with path planning models. Consequently, many existing studies rely on simplistic and less reliable linear fire inference models, compromising the safety of rescue operations. This paper addresses these challenges by proposing an underground rescue path planning method based on a comprehensive fire risk assessment, aimed at enhancing both safety and operational efficiency. A fire risk assessment approach, driven by fire situation inference, is introduced, employing a novel grid-matching transformation to capture the spatio-temporal dynamics of fire conditions using high-precision simulation software. Additionally, an improved A* algorithm is developed for real-time rescue path optimization, minimizing path risk based on the results of the risk assessment. The proposed method is validated through a detailed case study, demonstrating its effectiveness and reliability.

A LiDAR SLAM Algorithm Considering Dynamic Extraction of Feature Points in Underground Coal Mine

Abstract. To address the absence of GNSS signals in underground coal mines and the susceptibility of mainstream LiDAR SLAM to degradation due to insufficient feature constraints, this paper proposes a tightly-coupled SLAM algorithm incorporating LiDAR and IMU for use in such environments. The paper initially introduces a dynamic feature point extraction strategy, where the number of corner feature points can be dynamically adjusted based on the occurrence of degradation in the underground coal mine environment. This approach constructs a constraint matrix with rich and robust feature information, enhancing pose estimation accuracy in environments with inadequate feature constraints, mitigating degradation effects, and reducing global cumulative error. Subsequently, the consistent building of the underground coal mine environment is achieved through back-end factor graph optimization. Finally, to validate the effectiveness of the method, experimental analysis is conducted in an underground coal mine. The results demonstrate that LeGO_LOAM exhibits poor robustness in underground coal mines and fails to construct a globally consistent pose estimation and map. Conversely, the pose estimation error of the proposed method in this paper is 50.93% lower in the plane direction and 42.13% lower in the elevation direction compared to LIO_SAM. This underscores the method's significance as crucial technical support for the intelligent perception and positioning of robots in underground coal mines.

Unveiling heavy metal(loid) contamination and migration at an abandoned smelting site: Integrated geophysical and hydrological analyse

The heavy metal contamination at abandoned smelting sites is receiving increasing attention. This study focuses on an abandoned lead–zinc smelting site and uses several methods (such as soil sampling, geophysical exploration, and groundwater monitoring) to systematically investigate the contamination characteristics, distribution patterns, and migration mechanisms of heavy metal(loid) in soil and groundwater. A geological structure model of the study area was established through drilling data and geophysical techniques (Ground penetrating radar (GPR) and Electrical resistivity tomography (ERT)), revealing the relationships among soil resistivity, electromagnetic wave reflection characteristics, and geological structure. This study revealed that heavy metal contamination in soil was concentrated mainly northwest of the site and was closely related to historical smelting activities. The presence of heavy metal(loid)s in groundwater had a complex correlation with soil physicochemical properties and environmental elements, and its distribution was influenced by both hydrogeological conditions and soil properties. This study also investigated the effects of the soil water content (SWC) and soil hydraulic conductivity (Ks) on heavy metal(loid)s migration and reported that high SWC and Ks contribute to the dissolution and migration of heavy metals. In addition, a 3D visualization model of heavy metal contamination plumes was established via Voxler software. This model intuitively demonstrated the migration and diffusion of heavy metal(loid)s in the underground environment, offering a novel perspective on their subsurface behavior. The research results provide important information for understanding the migration of heavy metal(loid)s in soil-groundwater systems.

NeRF-based Localization and Meshing with Wearable Laser Scanning System: A Case Study in Underground Environment

Abstract. Due to the subterranean scene’s poor lighting conditions and variability of the environment, real-time localizing and meshing in complex underground scenes present a challenging task, with high-potential applications in mining and tunnel protection. In this work, we propose a method that combines SLAM and NeRF for mesh reconstruction in the underground environment with a wearable device. First, a LiDAR-Inertial odometry is used for pose estimation. The resulting poses and sequential laser frames are synchronized to generate a novel data structure, scan-block, which is crucial for improving efficiency and ensuring local precision. This structure is designed to enhance efficiency and ensure local precision. Finally, the generated scan-blocks are passed to the backend NeRF for real-time mesh reconstruction. The experiments are carried out in a complex underground space. The experimental results proved that this method achieved good results. The demo video can be found at https://youtu.be/_y1vbaW06EM?si= hFnAl12u-ffU933h.

Optimized Factor Graph for Tightly-Coupled LiDAR/IMU Localization in Underground Parking Garages

Abstract. To address the challenge of intelligent vehicle localization in underground parking structures due to the loss of GNSS signals, this paper introduces a method to address this issue by developing a novel localization framework known as GF-LIO, which denotes a tightly-coupled fusion of LiDAR and IMU data, innovatively combining the Interactive Extended State Kalman Filter (IESKF) with a factor graph to enhance the localization process and solve the problem of GNSS signal loss in underground parking lots. The GF-LIO model commences with a strategic feature selection process, facilitated by a greedy algorithm that prioritizes environmental cues within the point cloud data. This method effectively filters out redundant features, thereby enhancing the saliency of retained features and subsequently improving the robustness of the localization process. Following feature selection, the model integrates LiDAR and IMU measurements utilizing the IESKF algorithm, ensuring a cohesive fusion of sensor data and bolstering attitude estimation accuracy. The culmination of the GF-LIO framework involves factor graph optimization, a sophisticated technique that synthesizes LiDAR odometry, IMU pre-integration factors, and loop closure detection factors. This optimization step enhances the overall precision and consistency of the localization process, resulting in superior performance compared to existing methodologies. Experimental evaluations conducted within underground parking environments corroborate the efficacy of the GF-LIO model. Comparative analyses against established approaches such as A-LOAM, LeGO-LOAM, LIO-LOAM, and FAST-LIO demonstrate a notable performance improvement exceeding 12.53%. The proposed model adeptly integrates domain-specific environmental characteristics with multi-sensor data, thereby facilitating precise localization and map construction tasks for intelligent vehicle navigating within the intricate confines of subterranean parking structures.

Transport characteristics and dispersion evolution of CO-NOx mixture exhaust for shuttle movement emission in short-wall continuous mining face.

To investigate the evolution of CO and NOx pollution in the exhaust gas from diesel-powered shuttles under the double lane ventilation system of short-walled continuous mining face, this article uses a combination of numerical simulation and field tests to numerically simulate and analyze the pollution transport law of CO and NOx emitted from the shuttles at three locations: heading tunnel, contact alley and supporting tunnel. The results show that when the shuttle car in heading tunnel, the CO and NOx discharged from the shuttle discharge port move toward the exit of the tunnel at an average speed of 1.25m/s. The high CO and NOx concentration area appears at the discharge port, with the highest CO volume fraction of 82.5ppm and the highest NOx volume fraction of 28.25ppm. When the shuttle car at the contact alley, by the two walls of the alleyway on the gas diffusion space extrusion effect, CO and NOx in X = 37.5m ~ 42.5m range gathered, appear high CO and NOx concentration band. When the shuttle car in supporting tunnel, it is hindered by the belt conveyor within the range of X = 63m ~ 64.5m, so CO and NOx fail to spread to the outlet of the roadway in time and pile up at the bottom of the roadway. The highest CO volume fraction is 76.25ppm and the highest NOx volume fraction is 28.28ppm.According to the different positions of the car, the pollution areas of CO and NOx exceeding the specified threshold (24ppm, 2.5ppm) were divided, and the distribution characteristics of CO and NOx pollution were obtained by quantitative analysis through curve fitting, which provided a theoretical basis for the prevention and control of harmful substances emitted by fuel power equipment in underground environment.

Ultrasonic Array-Based Multi-Source Fusion Indoor Positioning Technology

Underground mining involves numerous risks, such as collapses, gas leaks, and explosions, posing significant threats to worker safety. In this work, we develop an indoor localization system that uses Bluetooth for coarse positioning and ultrasonic arrays for precision calibration. This system is particularly useful for automated mining operations in underground environments where satellite positioning signals are unavailable. The indoor localization system consists of ultrasonic receiver arrays and an improved multi-transmitter-multi-receiver algorithm, enabling accurate localization within the mining environment. Geometric Dilution of Precision (GDOP) analysis is incorporated to optimize the network layout, and an inertial navigation module is integrated to track the posture of moving objects, enabling precise trajectory determination over large areas, such as coal mines. In the experiment, three traditional methods were compared, and the proposed tracking approach demonstrated a positioning accuracy within 10 cm, reducing error by 20% compared to conventional techniques. This high-precision indoor localization method proves beneficial for underground mining applications.

Research on coal-rock boundary identification based on the morphological sobel algorithm

Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation; to a certain extent, subsequent intelligent mining is limited. A morphological Sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological Sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological Sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the Sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

Multi-segmented tube design and multi-objective optimization of deep coaxial borehole heat exchanger

Deep coaxial borehole heat exchanger (DCBHE) is a key component to extract medium-deep geothermal energy for low-carbon building heating. It is a convention that both center tube and annuals tube are respectively using a single material. However, a basic idea behind this study is using non-linear design to ask for performance improvement. It is assumed that non-uniform tube design pattern can meet different heat exchanger demand at different depth under a geothermal gradient dominated underground environment. The whole study is to prove this conjecture and make it useful for engineering application. A new numerical model is put forward to considered multi-segmented structure in heat transfer computation and a nondominated sorting genetic algorithm (NSGA-Ⅱ) is adopted in optimization process. The results of this study show that the optimized designed case can increase outlet temperature by 3 °C while reducing investment of 10000RMB. In addition, a home-made software is developed to perform the thermal and economic evaluation of DCBHE as well as automatically optimization of this multi-segmented structure. This study can provide a new model, algorithm, results and software tool for better utilizations of deep geothermal energy.

Research on visible light communication channel model in underground mines

Abstract In order to effectively model the Visible Light Communication (VLC) channel in underground mines, this paper delves into the challenges of the underground environment and develops a mathematical model by analyzing the propagation characteristics of visible light. The objective is to deepen our understanding of light signal behavior in this unique setting. Most existing research focuses on visible light channel modeling in indoor environments, with limited studies on underground mines. Our model takes into account the irregular surfaces of mine walls, as well as the effects of factors such as high dust concentration, extreme temperatures and pressures, light absorption by mine walls, and obstruction caused by large machinery on the propagation of visible light signals. This paper introduces refined optical propagation models that incorporate both scattering and light absorption considerations, resulting in a maximum received power of -46.12 dBm, which is significantly lower than that in indoor environments. These models enhance our ability to analyze and improve channel performance, thereby optimizing communication efficiency.

Permeability monitoring of underground concrete structures using elastic wave characteristics with modified Biot’s model

This study aims to develop a theoretical model for predicting the permeability of concrete in underground structures using compressive elastic waves. This research is motivated by the necessity of monitoring the permeability of concrete used in critical underground infrastructure, such as tunnels and radioactive waste disposal sites, to ensure their long-term safety. Increased permeability owing to crack generation can lead to groundwater inflow, undermining the structural integrity of these facilities. Traditional methods for permeability monitoring face challenges at depths of 500 m–1 km owing to high temperatures, high pressures, and limited space conditions. To address these issues, Biot’s model, which correlates the P-wave characteristics with the properties of porous media, was applied in this study. The P-wave velocity and attenuation were studied according to the permeability of concrete based on Biot’s model. Subsequently, concrete specimens were prepared to measure the permeability, P-wave velocity, and attenuation. The permeability results from the experiment were compared with those obtained from the model for validation. The findings indicate that the modified Biot’s model can effectively monitor permeability through elastic wave characteristics, offering a non-destructive and reliable method for assessing the condition of concrete structures in underground environments. This approach is expected to enhance the safety of underground infrastructure through accurate permeability monitoring.

Precipitation patterns strongly affect vertical migration and methylation of mercury in legacy contaminated sites

Legacy-contaminated sites act as significant sources of mercury (Hg) to their surrounding surface and underground environments. Intensified extreme precipitation is posing great threats to the environment and human health by changing the fate of pollutants, yet little is known about its effect on the vertical migration and methylation of Hg in contaminated sites. Here, we applied a range of simulated extreme precipitation patterns (frequency and intensity) to column leaching assays with soils collected near a contaminated site. We observed that precipitation with high frequency but low intensity resulted in more vertical migration of Hg through the soil profile than that with low frequency but high intensity. The majority (> 90%) of leached Hg was prone to migrate vertically within the top 10 cm of the soil profile. Furthermore, rainfall stimulated microbial Hg methylation, as demonstrated by enhanced production of methylmercury (MeHg) in both simulated and field-contaminated soils. We identified specific microbial taxa including Geobacteraceae, Desulfuromonadaceae, Syntrophaceae, Oscillospiraceae, and Methanomicrobiaceae as key predictors of MeHg production, which differed from those typically observed in overlying water of croplands. Particularly, the relative abundance of these dominant Hg methylators significantly increased during rainfall-induced leaching compared to that of the control, suggesting the crucial yet previously overlooked impacts of increased precipitation events on the process of microbial Hg methylation in industry-contaminated sites. Given the rising incidences of extreme precipitation events worldwide due to climate change, this study highlights the significance of assessing Hg mobility and microbial transformation in legacy contaminated sites.

Analysis of Scraper Conveyor Chain Dynamics under Falling Coal Impact Conditions

The scraper conveyor, essential for mechanized mining, operates in harsh underground environments and is subjected to severe impact loads from coal and rock falls. Such conditions can cause chain jamming, breakage, and other malfunctions, necessitating a detailed study of the system’s dynamic behavior under impact conditions. This study investigates the dynamic characteristics of a scraper conveyor’s chain drive system using a coupled ADAMS-EDEM simulation model. The model analyzes the effects of loaded coal piles on the conveyor’s dynamics during normal and impact conditions. Simulations show that loaded coal piles excite the scraper’s acceleration and sprocket rotation, with the greatest impact in the scraper’s running direction. Longitudinal impact and contact forces on the chain ring are more significant than in other directions under both no-load and loaded conditions. A strong linear relationship exists between the falling coal mass and longitudinal impact force. The coal pile causes prominent longitudinal vibration excitation while inhibiting the overall vibration of the chain drive system to some extent. The findings provide insights for identifying failure-prone areas under impact conditions and offer theoretical guidance for optimizing scraper conveyor design. This enhances mining efficiency and safety in coal operations.