Tunnel Engineering Research Articles

Analysis of Different Noise Reduction Effects of Ground Penetrating Radar Image of Cavity Behind Tunnel Primary Support Based on Singular Value Decomposition and Wavelet Transform

The cavity behind the lining structure is a typical disease in tunnel engineering. Cavity seriously affects the interaction between lining and surrounding rock, resulting in uneven bearing of lining structure and stress concentration, which can easily induce lining concrete cracking, water leakage and other diseases. Accurate identification of lining structure cavities is the key to ensure the normal operation of the tunnel. However, the ground penetrating radar detection images of lining cavities often contain interference signals such as background noise, which seriously affects the accuracy and clarity of cavity detection. The application effects of wavelet transform and singular value decomposition in image denoising and resolution of tunnel lining cavity ground penetrating radar are studied. Under this background, the model test of regular and irregular cavity detection behind tunnel lining is carried out, in which the irregular cavity is located in the surrounding rock environment of filling soil to simulate the real situation of tunnel. The measured images of lining cavity under different working conditions are obtained, and the image denoising analysis is carried out. The results show that singular value decomposition can effectively suppress noise. The ground penetrating radar image after singular value decomposition denoising is clearer, and the image resolution is effectively improved, thus realizing the accurate identification of cavity diseases behind the primary support of the tunnel

Interfacial bond properties between polypropylene fiber mortar and rock with different roughness

Abstract Abstract: Effective reinforcement measures for the surrounding rock of the tunnel are crucial to ensure the safety of tunnel engineering. Fibers are very effective in improving the quality of the interface; however, to promote the application of fiber mortar (FM) in tunnel lining reinforcement, it is necessary to study the bonding performance between FM and rock. The mechanical properties of ordinary mortar (OM) and FM were investigated, and bonding performance tests were conducted. The effect of polypropylene (PP) fibers on the mechanical properties and the influence of PP fibers and surface with different roughness on the bond properties were analyzed in detail. A 3D printer was used to print various structural boards to characterize different roughness levels, and the shape of the printed model was effectively controlled, thereby ensuring the consistency and repeatability of the structural board. The test results indicated that the splitting tensile strength and volumetric strain of the mortar were improved significantly with the addition of PP fibers, in which the splitting tensile strength increased by 38%, the elastic modulus and Poisson’s ratio increased, and the deformation and energy absorption capacity were enhanced; however, there was little effect on its compressive strength. The bonding performance between mortar and rock surface were enhanced with the increase in roughness, and the direct shear strength recorded the highest value while the bond specimens with a surface roughness of joint roughness coefficient 18-20, also the PP fibers improved the bond properties. This study will be helpful for promoting the application of fiber mortar in tunnel surface lining engineering.

Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics

In tunnel engineering, joint shear slip caused by external disturbances is a key factor contributing to landslides, instability of surrounding rock masses, and related hazards. Therefore, accurately characterizing the macromechanical properties of joints is essential for ensuring engineering safety. Given the significant influence of rock joint morphology on mechanical behavior, this study employs the frequency spectrum fractal dimension (D) and the frequency domain amplitude integral (Rq) as quantitative descriptors of joint morphology. Using Fourier transform techniques, a reconstruction method is developed to model joints with arbitrary shape characteristics. The numerical model is calibrated through 3D printing and direct shear tests. Systematic parameter analysis validates the selected quantitative indices as effective descriptors of joint morphology. Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. Machine learning, recognized as a rapidly advancing field, plays a pivotal role in data-driven engineering applications due to its powerful analytical capabilities. In this study, six algorithms—Random Forest (RF), Support Vector Regression (SVR), BP Neural Network, GA-BP Neural Network, Genetic Programming (GP), and ANN-based MCD—are evaluated using 300 samples. The performance of each algorithm is assessed through comparative analysis of their predictive accuracy based on correlation coefficients. The results demonstrate that all six algorithms achieve satisfactory predictive performance. Notably, the Random Forest (RF) algorithm excels in rapid and accurate predictions when handling similar training data, while the ANN-based MCD algorithm consistently delivers stable and precise results across diverse datasets.

Study on Mechanical Behavior of a New Three-Dimensional Sensor Under Rock Rheology

Aiming at the problems of the complex in situ stress measurement process, difficult measurement and long-term monitoring, it is particularly important to design a three-dimensional borehole full stress monitoring method based on the principle of rock mass rheological stress recovery, which lays a foundation for underground construction, such as roadway engineering and tunnel engineering. Through theoretical calculation and mechanical analysis, the magnitude, direction and inclination of the stress at any point in the borehole of the geological body are analyzed. Based on this, a three-dimensional borehole full stress monitor is constructed. At the same time, the rheological stress change process of rock mass around the borehole is analyzed by numerical simulation, the rationality of the three-dimensional borehole full stress monitor is determined, and a new method of borehole surrounding rock stress monitoring is proposed. The results show that: (1) the stress monitoring of the surrounding rock can be realized by the stress recovery principle of a rock mass rheological borehole, and it can be monitored for a long time; (2) the three-dimensional borehole total stress monitor can reflect the size and direction of the six principal stresses of the surrounding rock stress through eight measuring points; (3) the design structure and mechanical properties of the three-dimensional borehole full stress monitor are reasonable, and the linearity and sensitivity of the hydraulic membrane material are reasonable, which can meet the standards of long-term monitoring.

Rock-Breaking Mechanism and Application of Combined Long and Short Holes in Parallel Holes Cut in Small-Section Tunnels

In order to address the issue of limited excavation footage in the drilling and blasting of a water diversion tunnel with a cross-section of approximately 10 m2, which is unable to meet the demands of rapid construction, a blasting method combining long and short straight-hole cutting was proposed based on the theories of elastic mechanics, blasting craters, explosive gas and stress waves. A mechanical model was established to elucidate the parameter design method and cavity formation principle of the combined cutting. Numerical simulation and field tests were employed to analyze the rock-breaking process of combined cutting, with a view to comparing the blasting effect differences between the traditional inclined cutting method and the combined cutting method. The research results indicate that during the blasting process with combined long and short straight-hole cutting, the uncharged portion of the deep hole can serve as an empty hole during the subsequent blasting of the shallow hole. The concentration of stress at the wall of the empty hole and the superposition of reflected and incident waves serve to enhance the rock-breaking effect of the shallow hole, with the enhancement being influenced by the diameter of the hole and the distance between it and the empty hole. The preferential detonation of the shallow hole can provide a smaller resistance line and free surface distance for deep hole detonation, creating favorable conditions for rock fragmentation in deep hole blasting, making it easier for the rock in the cutting area to be thrown out and increasing the utilization rate of the blast holes. The shape of the formed cavity is a long strip-shaped cube, with its volume being influenced by the spacing between each group of deep and shallow holes. The rock mass damage is most severe in the vertical direction, while the rock mass damage at the center of the upper and lower edges is relatively weaker. In order to optimize the utilization of blasting energy, it is essential to select an appropriate spacing between each group of blast holes. In comparison to the utilization of traditional inclined cuts, the implementation of combined long and short holes has been observed to result in a greater extent of blasting footage and relatively lower explosive consumption. These research findings provide a reference point for the rapid and efficient construction of small-section tunnel engineering, as well as the design of straight-hole cut blasting with reduced consumption.

Study on Public Perceptions and Disaster Prevention Framework of Tunnel Fires Based on Social Media and Artificial Intelligence

To investigate public perceptions regarding tunnel fire disasters and optimize the tunnel fire disaster prevention framework, this study takes the emerging social media platform Douyin as a case study, conducting an in-depth analysis of 2133 short videos related to tunnel fires on the platform. A computational communication method was used for analysis, Latent Dirichlet Allocation was used to cluster the discussion topics of these tunnel fire short videos, and a spatiotemporal evolution analysis of the number of videos posted, user comments, and emotional inclinations across different topics was performed. The findings reveal that there is a noticeable divergence in public opinion regarding emergency decision making in tunnel fires, related to the complexity of tunnel fire incidents, ethical dilemmas in tunnel fire escape scenarios, and insufficient knowledge popularization of fire safety practices. The study elucidates the public’s actual needs during tunnel fire incidents, and a dynamic disaster prevention framework for tunnel fires based on social media and artificial intelligence is proposed on this basis to enhance emergency response capabilities. Utilizing short videos on social media, the study constructs a critical target dataset under real tunnel fire scenarios. It proposes a computer vision-based model for identifying critical targets in tunnel fires. This model can accurately and in real-time identify key targets such as fires, smoke, vehicles, emergency exits, and people in real tunnel fire environments, achieving an average detection precision of 77.3%. This research bridges the cognitive differences between the general public and professionally knowledgeable tunnel engineers regarding tunnel fire evacuation, guiding tunnel fire emergency responses and personnel evacuation.

Effect of Lateral Pressure on the Stability of Inclined Layered Rock Tunnel: A 3D-printed Model Investigation by Cement-based Materials

The inclined layered rock tunnel engineering in the alpine canyon area subject to tectonic stress will be influenced by high lateral stress. Currently, there are scarce reports on the impact of the lateral pressure coefficient on the stability of inclined layered rock tunnels. Hence, the mechanical response of the inclined layered tunnel model was investigated based on the 3D-printed model test, supplemented by acoustic emission (AE) and digital image correlation (DIC) monitoring methods under three cases of lateral pressure coefficients of 1.2, 1.5 and 1.8. The results reveal that for the tunnel model with a 60° bedding inclination, the bearing capacity is negatively correlated with the lateral pressure coefficient. The normal deformation of the bedding of the tunnel model is prominent, exhibiting significant asymmetric deformation characteristics. When the lateral pressure coefficient k increases from 1.2 to 1.5, the failure of the tunnel model changes from shear-slip failure along the layer to bedding cracking and buckling failure of the sidewall. The test results have enlightenment significance for the stability evaluation of inclined layered surrounding rock tunnels in the alpine canyon area.

Field Experiments of Distributed Acoustic Sensing Measurements

Modern, large bridges and tunnels represent important nodes in transportation arteries and have a significant impact on the development of transportation. The health and safety monitoring of these structures has always been a significant concern and is reliant on various types of sensors. Distributed acoustic sensing (DAS) with telecommunication fibers is an emerging technology in the research areas of sensing and communication. DAS provides an effective and low-cost approach for the detection of various resources and seismic activities. In this study, field experiments are elucidated, using DAS for the Hong Kong–Zhuhai–Macao Bridge, and for studying vehicle trajectories, earthquakes, and other activities. The basic signal-processing methods of filtering and normalization are adopted for analyzing the data obtained with DAS. With the proposed DAS technology, the activities on shore, vehicle trajectories on bridges and in tunnels during both day and night, and microseisms within 200 km were successfully detected. Enabled by DAS technology and mass fiber networks, more studies on sensing and communication systems for the monitoring of bridge and tunnel engineering are expected to provide future insights.

Enhancing β-Galactosidase Performance for Galactooligosaccharides Preparation via Strategic Glucose Re-Tunneling.

This study focuses on the characterization and re-engineering of glucose transport in β-galactosidase (BglD) to enhance its catalytic efficiency. Computational prediction methods were employed to identify key residues constituting access tunnels for lactose and glucose, revealing distinct pockets for both substrates. In silico simulated saturation mutagenesis of residues T215 and T473 led to the identification of eight mutant variants exhibiting potential enhancements in glucose transport. Site-directed mutagenesis at T215 and T473 resulted in mutants with consistently enhanced specific activities, turnover rates, and catalytic efficiencies. These mutants also demonstrated improved galactooligosaccharide (GOS) synthesis, yielding an 8.1-10.6% enhancement over wild-type BglD yield. Structural analysis revealed that the mutants exhibited transformed configurations and localizations of glucose conduits, facilitating expedited glucose release. This study's findings suggest that the re-engineered mutants offer promising avenues for enhancing BglD's catalytic efficiency and glucose translocation, thereby improving GOS synthesis. By-product (glucose) re-tunneling is a viable approach for enzyme tunnel engineering and holds significant promise for the molecular evolution of enzymes.

Failure mechanism analysis and treatment of tunnels built in karst fissure strata: A case study

With the rapid increase of tunnels built in karst fissure strata, much more problems appeared during the construction process. Based on the Liangwangshan Tunnel, this article presents a case of tunnel failure by karst fissure, and the failure mechanism and treatment were analyzed. Firstly, when the karst fissure was close to the excavation face, A large area of cavity appeared above the right spandrel, which was distributed diagonally with a narrow shape. However, the pressure on the left side was greater than it on the right side near the karst fissure by on-site monitoring. Secondly, laboratory tests were carried out to determine that the karst fissure weakened the strength of surroundings, especially in shear and tensile strength. The results of theoretical analysis and discrete element method considering the joints indicated that karst fissure will exacerbate the uneven stress distribution and prevent the transmission of stress. The failure mode can be manifested as bending fracture and shear fracture, and the tensile shear composite failure mainly occurred on tunnel near the fissure. Finally, the radial grouting and advanced pipes have been implemented on site, and the on-site monitoring showed that the treatment measures were effective to control the failure when tunnel was excavating. The research can provide guidance for the similar tunnel engineering built in karst fissure strata.

Study on the compressive performance of hyperelastic seismic isolation materials under passive confinement of surrounding rock simulated by CFRP tubes

Tunnel engineering has played a significant role in the development of human society. However, due to geological constraints, tunnels are prone to losing stability and safety under strong seismic forces. High-elasticity, super-tough polymer-inorganic composite tunnel isolation materials (PTIM), based on polyurethane elastomers, can meet the requirements for tunnel isolation, but research on their passive confinement mechanical properties remains insufficient. This study designed an experimental method to simulate the passive confinement state of tunnel isolation materials. The method uses CFRP tube confinement and core loading to replicate the actual stress conditions within a confined tunnel space. Based on the experimental data, a constitutive model for PTIM under passive confinement was established. The results show that the initial elastic modulus (Ec0) of PTIM and the CFRP confinement stiffness (K) are significantly correlated with the material's stress-strain curve, axial peak stress and strain, circumferential strain, compressive performance, and elastic modulus. Based on these mechanical properties as characteristic values, a tri-linear constitutive model for PTIM with different mix ratios under various confinement stress conditions was established. The high degree of agreement between the predicted data and the experimental data validates the effectiveness of the model.

GeoPredict-LLM: Intelligent tunnel advanced geological prediction by reprogramming large language models

With the improvement of multisource information sensing and data acquisition capabilities inside tunnels, the availability of multimodal data in tunnel engineering has significantly increased. However, due to structural differences in multimodal data, traditional intelligent advanced geological prediction models have limited capacity for data fusion. Furthermore, the lack of pre-trained models makes it difficult for neural networks trained from scratch to deeply explore the features of multimodal data. To address these challenges, we utilize the fusion capability of knowledge graph for multimodal data and the pre-trained knowledge of large language models (LLMs) to establish an intelligent advanced geological prediction model (GeoPredict-LLM). First, we develop an advanced geological prediction ontology model, forming a knowledge graph database. Using knowledge graph embeddings, multisource and multimodal data are transformed into low-dimensional vectors with a unified structure. Secondly, pre-trained LLMs, through reprogramming, reconstruct these low-dimensional vectors, imparting linguistic characteristics to the data. This transformation effectively reframes the complex task of advanced geological prediction as a "language-based" problem, enabling the model to approach the task from a linguistic perspective. Moreover, we propose the prompt-as-prefix method, which enables output generation, while freezing the core of the LLM, thereby significantly reduces the number of training parameters. Finally, evaluations show that compared to neural network models without pre-trained models, GeoPredict-LLM significantly improves prediction accuracy. It is worth noting that as long as a knowledge graph database can be established, GeoPredict-LLM can be adapted to multimodal data mining tasks with minimal modifications.

The application of GATEM in tunnel geologic risk survey based on complex terrain

Geologic risk assessment and prediction are vital processes preceding the design and construction of tunnels, offering crucial insights for ensuring the safe and efficient execution of tunnel projects. The geologic challenges posed by water inrush and mud bursts in tunnel engineering are often associated with low-resistance karst fracture zones and karst fissures controlled by structural and stratigraphic trends, providing a robust geophysical foundation for the application of electromagnetic methods. In recent years, the rise of numerous deep, lengthy, and challenging tunnel projects in mountainous regions with intricate terrains has significantly heightened the complexity of tunnel exploration. Traditional geophysical detection methods and geologic survey techniques are constrained by site limitations and complex terrains, falling short in effectively detecting geologic disasters within operational tunnels. The flexibility and convenience of the ground-airborne transient electromagnetic method (GATEM) make it particularly suitable for operations in complex terrain areas. Taking the application of GATEM in the Daji Tunnel in Guangxi, China as an example, a background investigation of the topography and lithology of the tunnel area was conducted. A series of data preprocessing, full-field apparent resistivity definition, terrain correction, depth definition, and terrain offset processes were applied to the GATEM field data to obtain the electrical distribution in the tunnel area. By analyzing the electrical variations on the elevation surface above and below the tunnel, we inferred the potential water inflow paths, and the karst positions above the tunnel were mapped. Also, suggestions are made for the complex situations that may occur during the tunnel construction process. Ultimately, the findings are highly congruent with the actual excavation outcomes of the construction unit, which ensured the safe and efficient construction of the tunnel. Thus, the reliability and superiority of the GATEM in the geologic risk investigation of complex terrain tunnel engineering are validated.

Assessment of dynamic responses and impact resistance of corrugated plate-reinforced concrete tunnels under irregular rockfall scenarios.

This article presents a composite tunnel rockfall protection structure (CPRC) employing galvanized corrugated steel plates as the inner formwork for reinforced concrete structures. It addresses the threats posed by frequent rockfall disasters in mountainous regions with complex geology. The research investigates impact damage from various rockfall shapes through numerical simulations and experiments on five representative forms, comparing traditional reinforced concrete structures RC with CPRC. The study highlights both structures' dynamic responses and damage characteristics across different impact scenarios, introducing the Residual Resistance Index RRI as a measure of post-impact safety performance. Results show that the numerical simulations align with laboratory tests, with discrepancies within 10%, validating the simulation method's accuracy in predicting impact resistance. Following impacts, both structures primarily displayed brittle concrete damage; however, the CPRC structure demonstrated a 79.46% reduction in concrete damage and an 86.31% decrease in reinforcement damage. The energy-dissipating properties of the corrugated plates significantly lowered rockfall penetration depth and impact energy transfer ratio, with an RRI exceeding 0.88 post-event. Additionally, the study reveals varying damage effects from different rockfall shapes, further supporting the CPRC structure's superior impact resistance and its effectiveness in preventing secondary disasters in tunnel engineering within mountainous terrains.

Deep learning-based acoustic emission source localization in heterogeneous rock media without prior wave velocity information

Abstract Acoustic emission (AE) source localization is crucial for monitoring but often relies on prior information, such as wave velocity and arrival time. This study introduces a novel method for locating AE sources in rocks without such information, addressing challenges posed by heterogeneous sensor arrays. Experiments involving pencil led break (PLB) tests on sandstone cubes collected AE waveforms and their coordinates. A ResNet-50 based deep learning model was developed to correlate the time-frequency spectra of AE with PLB locations, expressed as spatial Gaussian distributions. The model, achieved a 79% prediction accuracy for AE localization in complex environments. While there is room for improvement in training data quantity and diversity, the results validate the model’s effectiveness, particularly in coal mines and tunnel engineering.

Failure characteristics and pressure relief effectiveness of non-persistent jointed rock mass with holes

In tunnel engineering, the rock mass contains a significant number of irregularly distributed joints, and typically exhibits high energy accumulation, thereby posing a risk of rockburst occurrence. Therefore, it is of paramount importance to investigate the fracture propagation behavior in jointed rock masses and assess the impact of borehole pressure relief on mitigating rockburst occurrences for effective prevention and control measures. This paper focuses on failure characteristics and pressure relief effectiveness of non-persistent jointed rock mass with holes through laboratory testing and numerical simulation. In laboratory experiments, rock samples are prepared to include a range of crack dip angles and circular holes. Then, the crack propagation law of crack inclination and circular hole is studied by AE and DIC technology. The experimental results show that with the increase of fracture dip angle, the peak strength and energy change of the sample decrease first and then increase. Due to the existence of holes, the crack propagation direction of the original crack is changed. After drilling, the strain energy of the sample is obviously reduced, which shows that the drilling pressure relief effect is obvious, which can effectively reduce the energy accumulated inside the rock mass and reduce the risk of rockburst. Finally, the PFC numerical simulation software is used to analyze the micro-failure process and energy change law of the sample from three aspects: the relative position of cracks and holes, the diameter of boreholes and the spacing of boreholes. Further understanding of the energy dissipation law and mechanical behavior characteristics of jointed rock mass provides a reference for exploring the pressure relief effect of rock mass and preventing rockburst.

Bioinspired Photoluminescent "Spider Web" as Ultrafast and Ultrasensitive Airflow-Acoustic Bimodal Sensor for Human-Computer Interaction and Intelligent Recognition.

Nature provides massive biomimetic design inspiration for constructing structural materials with desired performances. Spider webs can perceive vibrations generated by airflow and acoustic waves from prey and transfer the corresponding information to spiders. Herein, by mimicking the perception capability and structure of a spider web, an ultrafast and ultrasensitive airflow-acoustic bimodal sensor (HOF-TCPB@SF) is developed based on the postfunctionalization of hydrogen-bonded organic framework (HOF-TCPB) on silk film (SF) through hydrogen bonds. The "spider web-like" HOF-TCPB@SF possesses light weight and high elasticity, endowing this airflow sensor with superior properties including an ultralow detection limit (DL, 0.0076 m s-1), and excellent repeatability (480 cycles). As an acoustic sensor, HOF-TCPB@SF exhibits ultrahigh sensitivity (105140.77 cps Pa-1 cm-2) and ultralow DL (0.2980 dB), with the greatest response frequency of 375 Hz and the ability to identify multiple sounds. Moreover, both airflow and acoustic sensing processes show an ultrafast response speed (40 ms) and multiangle recognition response (0-180°). The perception mechanisms of airflow and acoustic stimuli are analyzed through finite element simulation. This bimodal sensor also achieves real-time airflow monitoring, speech recognition, and airflow-acoustic interoperability based on human-computer interaction, which holds great promise for applications in health care, tunnel engineering, weather forecasting, and intelligent textiles.

International Distribution and Development of Rock Mass Classification: A Review

AbstractSince the 1950s, numerous rock mass classification systems (RMCS) have emerged for applications such as tunneling, mining, and slope engineering. The continued proliferation of these systems reflects the complexity of the issue and the absence of a universally accepted approach within the rock engineering community. This review offers an up-to-date examination of the global utilization of RMCS across applications such as tunnels, caverns, mining slope stability, and more. A “family tree of rock mass classification systems” is presented that traces their evolution over the decades. The family tree illustrates a prolific period of system development between 1970 and 2000, followed by a decline in progress and increasing specialization of systems. Based on surveys within the rock engineering community carried out between 2022 and 2024, the dominant systems for underground engineering and slope-related tasks were found to be GSI, RMR, and the Q-system, which have demonstrated their practical efficacy over 5 decades. Based on the surveys, two “world maps of rock mass classification” have been produced that visualize the international RMCS distribution. However, the survey shows that only some new or derived systems developed in the last 25 years have gained global acceptance. The paper closes with an outlook to possible future developments in rock mass classification and characterization.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells.

Cells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non-heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2's catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2's gas tunnel. Using kinetic stopped-flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5-fold enhancement in its association rate to the iron center of tunnel-engineered mutants. Our most effectively designed mutant displays 9-fold enhanced catalytic efficiency (kcat/KM=830±40 M-1 s-1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF-1α, as compared to WT PHD2 (kcat/KM=90±9 M-1 s-1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK-293T mammalian cells reveal significant reduction of HIF-1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams

This document introduces a novel equivalent algorithm for forecasting gas content within deep coal seams, which is subject to constraints stemming from the advancements and precision achieved in well and roadway engineering endeavors. This algorithm meticulously acknowledges that coal seam gas content comprises three fundamental components: the inherent gas emission rate of the equivalent stratum, the residual gas content retained within the coal seam itself, and the influence imparted by the gas content within the coal seam. Furthermore, the approach thoroughly considers variations in the level of porosity development within the coal seam and its surrounding rock formations, as well as the occurrence of gas within these structures. The equivalent layer is classified into two distinct groups: the sandstone zone and the clay zone. The sandstone zone utilizes pertinent parameters pertaining to fine sandstone, whereas the clay zone distinguishes between clay rock and thick mudstone. The influencing factor considerations solely encompass natural elements, such as the coal seam’s occurrence and geological structure. The residual gas content employs either existing measured parameters or acknowledged experimental parameters specific to the coal seam. Based on this predictive approach, an intelligent auxiliary software (V1.0) for mine gas forecasting was devised. The software calculates the gas content of deep coal seams within the mine at intervals of 100 m × 100 m, subsequently fitting the contour lines of gas content across the entire area. The gas content predictions derived from this equivalent algorithm demonstrate robust adaptability to variations in gas content caused by construction activities, and the prediction results exhibit an acceptable level of error on-site. Notably, the prediction process is not constrained by the progress of tunnel engineering, ensuring that the prediction outcomes can accurately represent the distribution characteristics of deep coal seam gas content. After a year of application, the prediction results have consistently met on-site requirements, providing a scientific foundation for the implementation of effective gas prevention and control measures in the mining area. Furthermore, this approach can effectively guide the formulation of medium- and long-term gas prevention and control plans for mines.