Coal is an important chemical energy source, and it is still the basic energy source for human beings in the short term. At the present stage, human demand for coal resources is increasing, so coal mine production is of great significance. Among them, roadway excavation is an important part of coal mine production, and advanced support equipment is the key link to ensure roadway excavation. This study aims to provide a comprehensive overview of the classification, characteristics, and development of existing coal mine roadway advanced support. In this study, we review the existing coal mine roadway advanced support technologies. According to the relevant patents, we analyze and summarize the characteristics of each patent, to predict the development trend of coal mine roadway advanced support in the coming period. This paper collates and analyses the existing advanced support methods, and summarizes four types and their typical characteristics. It analyses the main problems in the advanced support used at the present stage, collects and researches the relevant patents, and discusses the research status and future development trends of various advanced support methods. According to our research, advanced support equipment can be divided into independent advanced support equipment, advanced support equipment in conjunction with a tunnel boring machine, advanced support equipment in conjunction with an anchor drilling machine, and intelligent advanced support equipment. Independent advanced support has the advantages of simple structure and convenient operation. The advanced support equipment with a tunnel boring machine and the advanced support equipment with an anchor drilling machine can work more efficiently through cooperation with the drilling rig and other equipment. With the progress of science and technology, intelligent advanced support equipment has become the mainstream direction of future development.

Rock fracture behavior under stress is vital for risk evaluation in underground engineering excavation because the presence of water can significantly increase the extent of cracks and fractures in rock, leading to structural damage. This can result in catastrophic failures, including rock bursts, coal bursts, and water inrush. Hence, reliable prediction of rock damage and fracture processes is still lacking, which, in turn, enables the safe and efficient conduct of engineering projects in rock-mass environments. Thus, this study examines both dry and saturated sandstone samples under loading using Infrared Radiation (IR), Acoustic Emission (AE) monitoring, and Particle Flow Computation (PFC) techniques to effectively evaluate the fracture process in rocks under loading. Additionally, seven different artificial intelligence techniques, such as Gene Expression Programming (GEP), Gradient Boost Regression (GBR), Extreme Gradient Boosting (XGB), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LGBM), Categorical Boosting (CatBoost), were employed along with Explainable Machine Learning (XML) to predict the rock damage and fracture process. These models helped in the development of early warning signals to prevent catastrophic accidents. Both the experimental and simulation results have shown that the fracture density measured in terms of PFC and AE cumulative energy is significant in the saturated conditions compared to the dry conditions. Also, stress levels of 0.72 and 0.75 were found to be the warning signs in both dry and saturated conditions, based on the IR index (Average Infrared Radiation Temperature, AIRT) and AE characteristics. The comparison showed that the prediction accuracy of the XGB algorithm was the highest, followed by GBR, CatBoost, LGBM, GEP, and AdaBoost. However, GEP expressed its output in the form of an empirical equation owing to its grey-box nature, and thus, the law of fracture estimation in the form of an empirical equation was developed. The XML methods were added in order to enhance the interpretability of the high-performing, but black-box, XGB model. Such methods, along with a user-friendly Graphical User Interface (GUI), improved the model transparency and facilitated the integration of data-driven decision-making. XML and GUI tools may be instrumental in improving the safety measures adopted in coal mines and tunnels by reducing the risks and increasing operational safety.

Addressing the dual challenges of solid and liquid waste management and carbon emissions in the coal industry, this study developed a novel CO 2 -carbonated backfill material (CCB) by synergistically utilizing high salinity mine water (HSW), coal-based solid wastes, and a multi-scale modifier system. The modifiers-rick husk biochar (RHB), natural clinoptilolite (NCP), sepiolite (SEP), and γ-nano Al 2 O 3 (NA)-were incorporated to enhance CO 2 sequestration and engineering performance. Results demonstrate that the composite modifiers significantly improved the carbonation efficiency, achieving a CO 2 uptake of 9.33 mg-CO 2 /g-CCB in the optimal group (CCB-RNn), representing a 25.9% increase over the control. Rheological analysis confirmed the modified slurry retained suitable shear-thinning behavior for transport, while mechanical strength reached 7.48 MPa due to synergistic pore-filling and fiber-reinforcement effects. Microstructural characterization revealed that the modifiers promoted the formation of dense gel networks and finely dispersed carbonate crystals (calcite/magnesite), with NA effectively densifying the matrix and serving as nucleation sites. This work provides a feasible strategy for integrated management of mine water streams and carbon emission, contributing to the development of high-performance, carbon negative backfill technology.

The sustainable management of coal-based solid waste and effective CO2 sequestration are critical challenges for the mining industry. To address this, a novel aluminum nanoparticle-modified CO2-carbonated backfill (ANCB) material was developed that synergistically enhances mechanical properties with carbon capture functionality. The effects of varying aluminum nanoparticles (Al-NPs) concentrations (0.02wt

Aiming at the multi-objective and multi-criteria decision-making challenges in the process of coal gangue resource utilization, this study takes the Xinjie Taigemiao mining area as a case and proposes an integrated evaluation model combining the analytic hierarchy process (AHP) with min-max normalization. The model is structured around the sustainable goal of coal gangue resource utilization and establishes a multi-dimensional evaluation index system that includes technical feasibility, economic efficiency, environmental impact, resource-use efficiency, management complexity, and social acceptability. The weights of the indicators are determined by AHP, and each alternative is quantitatively assessed using min-max normalization, thereby achieving a systematic ranking and optimization among multiple schemes. The results indicate that low-level grouting technology performs best in terms of resource-use efficiency, environmental impact, and economic performance, and is identified as the optimal technical pathway for coal gangue resource utilization in this mining area. This study provides systematic and quantifiable methodological support for decision-making in coal gangue resource utilization in mining areas, with a certain degree of universality and potential for broader application.

The petroleum industry faces a serious problem of gas hydrate formation in pipelines and process equipment, particularly in low-temperature marine environments. It is necessary to understand the chemical thermodynamics of gas hydrate formation so that it can be conveniently avoided. Although research has been conducted to develop correlations or to use Artificial Intelligence (AI) to determine hydrate temperature, to the authors' knowledge, none have compared Machine Learning (ML) models' performance in hydrate formation temperature prediction, especially Web User Interface (WUI) development based on the best-performing model. To achieve precision, this work compares the performance of four ML models, such as Artificial Neural Networks (ANNs), Decision Tree Regressor (DTR), Support Vector Regressor (SVR) and Random Forest Regressor (RFR) in terms of hydrate formation temperature prediction using operating pressure and specific gravity as features. Python was employed for this work, as it supports open-source libraries such as Keras with TensorFlow and scikit-learn, among others. Results showed that the coefficient of determination (R-2), Root Mean Square Error (RMSE), and a20-index on the overall dataset are (0.9994, 0.3120, 1), (0.9992, 0.3716, 1), (0.9991, 0.3880, 1) and (0.9910, 1.2545, 0.9972) for DTR, ANNs, RFR, and SVR, respectively. Although all models delivered strong results, they differ sharply in the time required to train. ANNs required 974.9498 s, RFR needed 15.9753 s, and SVR took 294.4242 s. In contrast, DTR completed the task in only 0.2727 s. Based on performance and computational efficiency, the models rank as follows: DTR>RFR>SVR>ANNs. Eventually, Web User Interface (WUI) was developed based on the bestperforming model (DTR). The optimal activation function for the ANN is tanh, while the Support Vector Regressor model performs best with the Radial Basis Function (RBF) kernel. We are optimistic that this research will open novel avenues in natural gas engineering. It is recommended that the models' lower and upper bounds be broadened by training the model on additional experimental data across different operating conditions.

Fire exposure followed by water extinguishing severely compromises the structural integrity of granitic building stones, yet the underlying thermo-mechanical mechanisms remain inadequately understood due to experimental challenges in replicating realistic thermal gradients and fracture evolution. This study overcomes these limitations by combining high-speed induction heating experiments with grain-based numerical simulations to investigate the superposition effects of realistic fire-induced temperatures followed by either rapid water cooling or gradual air cooling on crack development and residual mechanical properties in granite. Using an integrated approach, we demonstrate that rapid heating induces fewer, larger thermal cracks, partially preserving peak strength despite internal damage, while rising temperature progressively reduces strength and stiffness through pervasive micro-damage, highlighting the importance of thermal history. Crucially, the cooling method governs post-fire integrity. Natural air cooling facilitates partial recovery of strength and stiffness while limiting macro-crack development. Conversely, rapid water quenching superimposes intense thermal shock, significantly exacerbating damage: it elevates crack density and propagation, increases deformation, and causes severe mechanical degradation compared to air cooling. These results provide clear evidence that the choice of extinguishing strategy critically determines residual performance, with water cooling causing profoundly worse structural degradation. Consequently, the cooling phase is as critical as the heating exposure in determining final damage. These findings necessitate considering both realistic thermal loading profiles and cooling strategies in post-fire structural evaluations, safety assessments, and conservation of granite masonry, especially for heritage structures.

A burnt rock aquifer threatens the safe mining of underlying coal seams. Understanding the height and evolution mechanisms of the water-conducting fractured zone (WFZ) is critical for preventing water inrush disasters and protecting water resources. Focusing on the S1233 panel at the Ningtiaota Coal Mine, where an overlying burnt rock aquifer jeopardizes mining safety, this study comprehensively estimates the WFZ height under completed extraction conditions using empirical and theoretical methods. With two theoretical methods applied based on the key stratum theory, the predicted WFZ height indicated that mining of the S1233 panel had a serious risk of water inrush. Then, a similar material model was established. The characteristics of strata failure, movement, and fracture evolution during working face advancement were systematically analyzed through experimental results. In addition, a critical threshold for water inrush disasters was determined by coupling the relationship between the advance distance and aquifer pressure. Finally, specific water hazard control measures were proposed based on the evolution features of water-conducting fractures. The results of this study provide valuable insights for roof water hazard management in coal mines.

This study investigates the effects of polydimethylsiloxane (PDMS) treatment and CO2 carbonation on the wettability, mechanical properties, pore structure, and CO2 adsorption behavior of hydrophobic backfill (HBF) materials. The results indicate that PDMS significantly enhances hydrophobicity, with the contact angle exceeding 90 degrees at 2.88% PDMS and reaching a peak of 136.5 degrees However, excessive PDMS (>2.88%) inhibited alkali activation, leading to microstructural loosening and reduced compressive strength. CO2 carbonation effectively counteracted this strength loss, filling voids and densifying the matrix, resulting in up to a 43.5% strength increase at 8.64% PDMS. The CO2 adsorption process followed a Langmuir-like model, with rapid adsorption in the initial phase, followed by saturation. CO2 uptake increased from 42.3 mg/g (untreated) to 60.5 mg/g (8.64% PDMS-treated), attributed to the indirect role of PDMS in creating additional CO2 interaction sites. Pore structure analysis revealed that PDMS initially reduced porosity but promoted pore coarsening at higher concentrations, while CO2 carbonation refined the pore network, decreasing total porosity and threshold diameters. Micromorphological and elemental analyses confirmed the formation of carbonation products, which reinforced the hydrated gel structure. The results demonstrate a compensatory mechanism where CO2 carbonation remediates the strength loss typically caused by hydrophobic modification, resulting in a material that maintains high mechanical integrity while achieving superior water resistance.

In the mining of inclined coal seams, coal is subjected to combined compression and shear loading, leading to mechanical responses and damage mechanisms markedly different from those under uniaxial compression. This study investigated the mechanical behavior of coal through laboratory compression-shear tests. A predictive equation for peak strength was derived, and the synchronous evolution of normal and shear strain was analyzed. The shear modulus increased with strain before the crack developed point, with its evolution transitioning to a decrease thereafter. Acoustic emission (AE) energy events and dissipated strain energy were predominantly concentrated in the nonlinear deformation stage. Furthermore, an increase in normal stress promoted the development of branched fractures around main penetrating crack, resulting in progressively more complex failure morphologies. Finally, a three-stage constitutive model for the shear stress-strain relationship was established, with the crack closure stress and crack developed stress serving as critical demarcation points. A damage variable, defined in terms of shear strain, was introduced to quantify the degradation process. The proposed model demonstrates close agreement with experimental results, yielding a correlation coefficient exceeding 0.9. These findings provide a theoretical foundation for assessing the mechanical properties and stability of coal in inclined seam mining.

With the increasing depth of underground engineering, research on the fracture mechanisms of rocks containing nonlinear fissures has attracted significant attention. This study systematically investigates the crack-strength-acoustic emission (AE) coupling effects in nonlinearly fractured sandstone through uniaxial compression-AE tests and particle flow code (PFC) numerical simulations. The experimental design incorporates specimens with filled/unfilled fissures of varying dip angles (0 degrees-90 degrees) and lengths (16-48 mm), combining AE parameters (energy, counts) and mechanical strength (MS) data to reveal the controlling mechanisms of fissure geometry on failure behavior. The results demonstrate that: Increasing fissure dip angle (>45 degrees) and decreasing length enhance peak stress by 4.91-8.32 MPa, while gypsum filling further increases strength by 3.58 %-22.02 % and suppresses crack quantity (W-a) by up to 18.7 %; AE cumulative energy shows a strong correlation with W-a (grey relational grade > 0.83); A multivariate quadratic regression model based on response surface methodology (RSM) and least squares fitting achieves optimal W-a prediction accuracy (MRE = 0.0298) by integrating wave velocity (xi), MS, and AE parameters; The competition of tribes and cooperation of members (CTCM) further optimizes the exponential model, reducing the mean prediction error by 2.65 %. This study provides a novel quantitative crack prediction method for stability assessment in deep rock mass engineering. However, future work should integrate cross-scale observations and multi-field coupling models to improve applicability in complex environments.

Liquid-phase mineralization of CO2 using coal fly ash (CFA) is an efficient approach to permanent CO2 sequestration. To address the low leaching efficiency of calcium ions (Ca2+) in carbon mineralization, this study systematically investigates the leaching performance and leaching mechanism of calcium ions from CFA by using a sequential alkaline-acid processing (i.e., alkaline activation followed by acid leaching). The effects of NaOH concentration, acid concentration, acid type (HCl/CH3COOH), reaction time, and grinding duration on leaching efficiency are studied. The reaction products are characterized by X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). A kinetic model is proposed to analyze the reaction dynamics and leaching mechanisms. The results show that the maximum Ca2+ leaching efficiency for untreated CFA is 43.7% after 40-min acid leaching with 7 mol/L HCl and 1:1.5 S/L ratio. The leaching efficiency can be enhanced to 72.1% after 50-min alkaline activation with 11 mol/L NaOH. Grinding the CFA can further increase the leaching performance of Ca2+. It is shown that the leaching efficiency can be enhanced to 58.75% and 82.3% after 90-min grinding, respectively, for cases without and with 50-min alkaline activation using 9 mol/L NaOH. It is also shown that a peak leaching efficiency of 86.51% can be obtained when 8 mol/L CH3COOH is used for the acid system. The mechanism for the enhancement of leaching efficiency is that both NaOH activation and mechanical grinding can break down the calcium and aluminum silicate vitreous matrix of CFA, facilitating calcium release. Ca2+ leaching performance exhibits two regimes. The leaching efficiency is significantly time-dependent in the first regime, and it remains almost constant in the second regime after the efficiency reaches a pseudo-maximum value. The contribution of this study is that a theoretical foundation is provided for enhancing the Ca2+ recovery from CFA, which makes it practical for large-scale CFA utilization and permanent CO2 sequestration in industry applications.

The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing [Formula: see text] of 0.998 followed by BR having [Formula: see text] values equal to 0.946 while MEP had the lowest testing [Formula: see text] of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn't produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry.

Water–sand inrush poses a hidden threat to coal mining safety in China. Understanding the two-phase flow of water–sand mixtures in fracture and caving zones is critical for mitigating these risks. Numerical simulations using the particle flow code (PFC3D) were conducted to analyze variations in water–sand displacement, flow velocity, flow rate, energy loss, porosity, and effective permeability in fracture and caving zones. Simultaneously, a test model was designed and a series of laboratory experiments were conducted to investigate the evolution patterns of water–sand discharges. The results show that: a large fracture inclination angle greatly influences the duration of water and sand flow in the fracture zone, heightening safety hazards in the coal mining faces after passing through the caving zone. Fracture roughness markedly affects the routing of sand flow, with the overall flow rate markedly decreasing from 5.986E−4 m3/h to 2.150E−4 m3/h. The loss of fine particles is essential for sand routing, with particle size negatively correlated with porosity, which decreases from 0.784 to 0.664 as particle size reduces. These findings provide crucial insights into preventing and addressing safety issues by water–sand inrush, enhancing the understanding of water and sand flow dynamics in fractures and pores, and contributing to broader coal mining safety strategies.

This study presents an innovative dual CO2 sequestration (DCS) method by integrating gaseous storage in mined-out areas with mineralized storage in backfill materials. The objects are to maximize the CO2 storage and FA disposal capacity in the premise of ensuring caprock stability and ecological water table preservation during on-site industrial practice. The indoor experiments were employed to analyze the workability of CO2 mineralized filling materials (CMFM) containing 50–90

Due to the challenging conditions associated with coal resource extraction, mine water hazards continue to pose a prominent threat. Accurate prediction and monitoring of precursor information related to water inrush events are crucial for ensuring mine safety. However, conventional monitoring techniques, such as microseismicity and acoustic emission, currently possess limitations in providing effective early warning. Therefore, this study aims to explore a novel method for monitoring mine water hazard precursors based on infrared thermal imaging technology. To achieve this, we conducted laboratory experiments to simulate the processes of rock fracture and water intrusion in coal mines. Sandstone specimens were selected for investigation under water pressures of 0 MPa, 0.2 MPa, 0.4 MPa, and 0.6 MPa. The research findings reveal that during the hydraulic coupling fracture process in sandstone specimens, the infrared thermographic parameters, including infrared thermal images, Average infrared radiation temperature (AIRT), the Variance of Original Infrared Image Temperature (VOIIT), and Variance of Successive Minus Infrared Image Temperature (VSMIT), demonstrate significant and distinct phase-dependent variations. Before a water inrush, infrared thermal images display low-temperature patches that gradually extend and expand. The AIRT shows a decline, with some instances falling by more than 0.5 degrees C. The VOIIT transitions from stable growth to significant fluctuations or rapid increases, reaching a maximum value of 0.776 under a water pressure of 0.4 MPa. In addition, the VSMIT exhibits abrupt changes, with the maximum value at a water pressure of 0.2 MPa increasing 10.43 times compared to the minimum value. These variations provide crucial precursor information for coal rock fracturing and water inrush events. Additionally, this study innovatively applies the Critical Slow down Theory (CSDT) to the analysis of infrared indicator parameters. The characteristics of critical transitions in the coal-rock dynamic system further enhance the precursory warning, allowing for an advanced warning time for the infrared indicators. This allows for the earliest prediction of a peak stress ratio of 51.2 %. This study successfully validates the effectiveness and timeliness of infrared thermography in capturing the infrared signals associated with rock fracturing and water inrush. Therefore, Author recommend deploying multiple infrared cameras for 24-hour unmanned monitoring in engineering projects at risk of water hazards, such as coal mine excavations and underground tunnels. Additionally, setting alarm thresholds for specific infrared indicators at the ground control station via a ring network will enable real-time monitoring and preventive measures to mitigate the risk of water inrush disasters.

The weak anchoring force and poor stability of the anchoring system in fractured surrounding rock roadway often lead to anchor rod slip failure, which seriously affects the safe and efficient mining of the mine. Therefore, this article intends to use theoretical analysis, indoor experiments, numerical simulations, and industrial experiments to study the migration law of wedge-shaped borehole grout and the reinforcement characteristics of surrounding rock grouting, the axial anchoring force reinforcement characteristics of the expanded bottom backfill anchoring technology and self-expanding enlarged head anchor rods, and the anchoring reinforcement mechanism of expanded bottom backfill anchoring technology and self-expanding enlarged head anchor rods. Firstly, The dimensions of the numerical simulation model are 2000 mm × 2000 mm. The study of the migration law of wedge-shaped borehole grout and the reinforcement characteristics of surrounding rock grouting reveals the diffusion law of wedge-shaped borehole grout and the mechanical changes in surrounding rock modification and replacement; Secondly, indoor anchor rod pull-out tests were conducted on three different anchoring forms, and the anchoring force changes of the three anchoring tests were compared and analyzed, as well as the interaction results between the filling body and the surrounding rock interface in the comparison group. The reinforcement characteristics of the expanded bottom filling anchoring using self expanding head anchor rods were obtained; Simultaneously, establish a mechanical model for expanding bottom backfill anchorage through theoretical analysis; Finally, on-site industrial inspections were carried out in Roadway 050001-07, the roadway is a 2.6 m × 2.6 m three arch roadway. It was found that after using self expanding head anchor rods for bottom filling and anchoring support, the roadway surrounding rock deformation shifted from axial-dominant to transverse-dominant. In comparison with the original support scheme, the maximum roof-to-floor displacement of the roadway decreased by 94%, and the maximum displacement of the two sides decreased by 88%. The stable time of the roadway was reduced from the original 300 days to 150 days, with the stable time advanced by 150 days. Further evidence shows that this anchoring support technology can achieve a significant increase in anchoring force with a small increase in engineering investment, effectively ensuring the stability of the roadway.

To address the issue of sudden water hazards encountered during coal mine roadway excavation, infrared monitoring experiments were conducted on coal seam walls at water pressures of 0 MPa, 0.2 MPa, 0.4 MPa, and 0.6 MPa. This analysis focused on the infrared radiation characteristics of the coal under varying water pressures, with the aim of providing early warning indicators for potential water burst incidents. The results indicate that during the excavation process, the stress on the coal and rock at each monitoring point in the tunnel exhibits a continuous cyclical fluctuation, with slightly higher stress observed in areas of greater water pressure. At a water pressure of 0 MPa, the infrared thermal characteristics exhibit significant alterations, with a "O"-shaped hightemperature zone appearing in the central region. At a water pressure of 0.2 MPa, a strip-shaped low-temperature radiation differentiation phenomenon is observed in the upper half, with relatively stable change characteristics. Conversely, at 0.4 MPa, an anomalously high temperature is detected in small regions of the upper half, while at 0.6 MPa, a large area of temperature decline emerges, indicating that greater water pressure corresponds to more pronounced changes in the infrared thermal imagery. At a water pressure of 0 MPa, the average infrared radiation temperature (Delta AIRT) exhibits an initial increase followed by stabilization, indicating the occurrence of local shear failure. In contrast, under water pressure conditions, the overall Delta AIRT demonstrates a downward trend, which becomes more pronounced with increasing water pressure, indicating the presence of local tensile failure. At a water pressure of 0 MPa., the temperature range R of the coal body shows a decreasing trend over time. In contrast, under water pressure conditions, the temperature range R of the coal body exhibits an overall increasing trend over time, with a faster rate of increase at higher water pressures. This indicates that greater water pressure leads to increased instability in the coal and rock, resulting in a higher degree of disintegration. The Variance of successive minus infrared image temperature (VSMIT) displays varying degrees of exceeding threshold levels at different water pressures, with higher frequencies of exceedance observed at elevated pressures, indicating a more severe degree of damage and fracturing in the coal and rock. The fractal dimension (D) of the coal and rock consistently increases over time across different water pressures, with a larger D observed at higher pressures, signifying a continuous exacerbation of damage and fracturing at each monitoring point throughout the monitoring period. In conclusion, the results of this study offer significant theoretical and practical value for employing infrared radiation technology to monitor water inrush from coal and rock in underground roadways.

This study introduces the Spatio-Temporal Attention Enhanced Encoder-Decoder Damage Prediction Network (STAE-EDDPNet), an innovative deep learning model designed to enhance the predictive capabilities of coal-rock damage infrared temperature fields, which is crucial for the safe production in rock engineering and mining engineering. STAE-EDDPNet integrates a spatio-temporal attention mechanism, significantly improving the capture of complex nonlinear spatio-temporal information in rock infrared radiation. Compared with baseline models such as 3DCNN, ConvLSTM, and EDDPNet, STAE-EDDPNet demonstrated superior performance in both single-step and multi-step forecasting tasks. Test set results show that its predictive accuracy is 25.56% higher than 3DCNN, 5.69% higher than ConvLSTM, and 0.19% higher than EDDPNet. The study also found that the characteristics of brittle failure rock data significantly affect model training and predictive performance, providing a direction for future data collection and experimental design improvements. The introduction of STAE-EDDPNet not only promotes the application of infrared monitoring technology in the field of safety monitoring but also provides valuable reference for rock damage early warning.