Abstract Geological CO 2 storage (CO 2 S) is a critical strategy for mitigating greenhouse gas emissions and addressing global climate change. To overcome the challenges associated with complex coupled processes and the difficulty of accurately assessing leakage risks in depleted sandstone gas reservoirs, this study develops fully coupled hydro‐mechanical numerical models based on the discrete element particle‐flow method under two distinct injection scenarios. The effects of natural fracture (NF) cementation strength, in‐situ stress difference, and injection rate on the evolution of the CO 2 seepage field and the induced stress field are systematically investigated. Four key characterization parameters—including leakage flux and average fracture aperture—are employed to quantitatively evaluate leakage risks associated with NF activation. When the cementation strength ratio ( CSR ) exceeds approximately 0.5, the probability of leakage path formation tends to decrease to below about 2%, accompanied by a near‐zero CO 2 leakage flux (CLF). The results show that: (1) Higher in‐situ stress differences and injection rates accelerate fracture propagation and increase the probability of forming connected leakage paths, with the injection rate exerting the more significant influence. In contrast, under high injection rates, the CLF can reach the order of 10 −2 –10 −1 kg/(m 2 ·s). (2) Weakly cemented NFs are more susceptible to activation, leading to larger fracture apertures and elevated CO 2 flux, whereas CSR > 0.5 markedly suppresses fracture connectivity and leakage flux. (3) Hydraulic‐fracture‐assisted injection enlarges the CO 2 coverage area and inhibits NF activation near primary fractures, but excessive injection rates may trigger secondary fracture propagation and elevate leakage risks. Optimizing reservoir selection and injection parameters effectively mitigates leakage, providing a mechanistic and quantitative foundation for safe and efficient CO 2 S.
Abstract This study examines the evolution of carbon dioxide (CO 2 ) injectivity during intermittent cold injection at the Aquistore site, a Canadian CO 2 capture and storage demonstration project. Continuous monitoring over 5 years of CO 2 injection, supported by two highly instrumented injection and observation wells, reveals a general improvement in injectivity performance with time. Bottomhole temperature records indicate persistent cooling near the injector, with injectivity performance inversely correlated to downhole temperature. A non‐isothermal modeling and monitoring framework is applied to interpret these trends through thermo‐hydro‐mechanical (THM) processes. Analysis of injection data using the injectivity index suggests that stress‐dependent non‐isothermal mechanisms and semi‐reversible changes in near‐wellbore permeability govern the observed behavior. During cold injection episodes, minimum effective stresses may exceed the tensile strength of the host rock, leading to aseismic pore deformation, tensile micro‐cracking, and reactivation of critically stressed fractures. Processes such as CO 2 /brine chemical interaction, rate‐dependent pore flow, and CO 2 phase behavior are not expected to enhance injectivity. While stress‐induced permeability changes may improve injectivity performance, non‐isothermal stress redistribution could also create flow pathways through low‐permeability formations, including caprock units. These findings highlight the importance of THM processes in injectivity modeling and underscore non‐isothermal effects as a critical consideration for long‐term CO 2 containment and conformance in deep saline aquifers.
Abstract Coal mining faces, as high‐risk operational environments, present a severe threat to miners' occupational health due to the complex, dynamic coupling of multiple factors such as dust, temperature, humidity, noise, and harmful gases. This paper systematically reviews research advances in environmental parameter monitoring and personnel vital‐sign detection methods. It synthesizes the mechanisms underlying the nonlinear impacts of environmental stressors—including dust exposure and high‐temperature, high‐humidity conditions—on miners' physiological systems (e.g., cardiovascular and respiratory functions). Furthermore, it consolidates technical pathways for multi‐source data fusion‐based early‐warning models. Current research demonstrates that data preprocessing methods leveraging adaptive threshold filtering and GANs significantly enhance data quality. The synergistic application of statistical thresholds, machine learning, and digital‐twin technologies offers novel approaches for dynamic environment–physiology early warning. However, persistent challenges remain, including insufficient standardization of environment–physiology data, weak cross‐scenario model generalizability, and difficulties in quantifying multi‐factor coupling effects. Future research should establish interdisciplinary frameworks integrating occupational medicine and engineering principles, develop context‐adaptive early‐warning systems, and advance coal mine safety management toward a closed‐loop “monitoring–warning–intervention” paradigm. Through a critical synthesis of existing achievements and limitations, this study provides theoretical insights for mitigating occupational health risks in coal mines and facilitates the evolution of safety monitoring from single‐hazard alerts toward multidimensional health surveillance.
Abstract This study evaluates the site‐specific CO 2 storage capacity and large‐scale injectivity performance of a potential saline aquifer CO 2 storage site in the German North Sea sector. A high‐resolution 3D geological model of the Triassic Volpriehausen sandstone is used to estimate static storage capacity via a spill‐point analysis and to refine these estimates through dynamic reservoir simulation. The injection scenario targets 300 Mt CO 2 over a 30‐year period at a rate of 10 Mt/year, followed by a 100‐year postinjection phase. The static assessment indicates a total storage capacity of approximately 900 Mt at the site, but the dynamic simulations show that only a fraction of this capacity is actually achievable under these high‐rate requirements. Dynamic simulations confirm that the 10 Mt/year target can be sustained using five vertical wells positioned near the structural spill depth. The injection rate could also be achieved using two horizontal wells with two or three open sections, furthermore allowing for steering of the injected CO 2 phase towards individual subtraps. Injection strategies are tested under realistic site‐specific boundary conditions, accounting for pressure dissipation, fracture limits, and plume confinement. Pressure build‐up is found to extend across tens of square kilometers. CO 2 may partially remain in the mobile phase for 100 years after the end of the injection.
India needs to focus on permanent CO2 storage in the depleted hydrocarbon reservoirs to meet the net-zero emission goals. This study evaluates the CO2 storage capacity of mature fields within the Cambay Basin using the case of the Gandhar-Hazad reservoir. A composite method that involves source-sink matching, production depletion history matching, and compositional reservoir simulation is used. Significant CO2-emitting industries in Gujarat are filtered and equated with appropriate sinks to reduce the transportation distance and enhance the viability of the project. Field-specific petrophysical properties and data on reservoir pressure are used to construct a three-dimensional geological model. The General Equation-of-state model Multicomponent (GEM) reservoir simulator (from Computer Modeling Group, CMG-GEM) is used to simulate continuous and water-alternating-gas (WAG) CO2 injection under geomechanical and capillary constraints, explicitly incorporating hysteresis, CO2-brine solubility, and long-term trapping mechanisms. The findings suggest that WAG injection offers high sweep efficiency and plume migration. It increases residual and solubility trapping processes. The percentage of CO2 immobilized is around 45%-55% during the simulation period. The first water saturation, porosity, and injection parameters are found to be the most influential trapping performance controls when using CMOST-AI optimization and Morris sensitivity analysis. The suggested workflow decreases geological and operational uncertainty. It shows that the scalable storage of CO2 in the intricate mature Cambay Basin reservoirs is technically feasible.
Abstract With underground engineering projects becoming deeper and more complex, the associated safety problems, especially rockburst, have increasingly increased. Despite decades of research, effective management of rockburst continues to be a formidable challenge in underground excavations. This study presents a scientometric visualization analysis of 2449 papers and conducts a comprehensive review of 336 key studies to explore the state‐of‐the‐art developments in rockburst research. With a primary focus on the prediction and prevention of rockburst, this review identifies existing research gaps and proposes a novel framework aimed at addressing these challenges in underground excavations. The results underscore a critical disconnect between advanced prediction methods and engineering practices, which limits the ability of engineers to carry out reliable assessments of rockburst potential. This disconnection prevents the prompt development of targeted prevention strategies, further aggravated by inadequate data sharing across large‐scale projects. The review also describes the limitations of relying solely on data‐driven methodologies to address the complex challenges in the lifecycle management of underground excavations. To overcome these challenges, this study proposes an innovative framework based on an ontological knowledge base. This framework is designed to integrate multisource data and diverse analysis techniques, exploring the means toward better decision‐making in future digital underground projects.
Deep formations are subject to long-term interactions among high temperature, pore pressure, and stress, and rock mechanical properties are fundamentally governed by these coupled in situ thermo-hydro-mechanical (THM) fields. However, most porous-medium models are developed for non-in-situ settings and do not capture the inherent equilibrium state of rocks in geological environments. This study investigates the mechanical properties of 3000 m-depth sandstone under reconstructed in situ THM conditions and evaluates the implications for CO2 injection scenarios. Within a coupled porous-medium formulation, we establish an elastic porosity equation and decompose the contributions of in situ temperature, pore pressure, and stress to mechanical properties. Calibration using data from wells FS7 and SS2 sandstones in the Songliao Basin, combined with finite-element simulations, enables quantification of THM-driven changes in porosity, permeability, and elastic-plastic parameters. The results show that restoring in situ temperature leads to matrix thermal expansion, while in situ stress induces volumetric compaction, thereby reducing porosity and permeability, and strengthening the material. In contrast, increasing pore pressure promotes pore dilation and mechanical weakening. In situ stress exerts the strongest control on mechanical properties, followed by temperature, with pore pressure effects being comparatively minor. The sensitivities of these responses depend on the initial porosity: the high-porosity, low-strength FS7 sandstone is more sensitive to THM perturbations, whereas the lower-porosity, higher-strength SS2 sandstone remains more stable. In addition, the model is applied to an illustrative near-field CO2 injection scenario to examine parameter evolution under reconstructed in situ THM conditions. The simulations are conducted within a partially coupled THM formulation, demonstrating coherent multi-physics responses and consistent evolution of mechanical parameters. Overall, this study establishes a mechanically consistent in situ baseline framework for analyzing sandstone mechanical properties under reconstructed multi-physics conditions.
Pipe-roof support serves as a critical prereinforcement technique for ensuring face stability and controlling surface settlement in shallow tunneling through soft ground. However, existing numerical simulations and monitoring techniques often fail to adequately capture the complex three-dimensional internal response of pipe-roof-reinforced soil. Capturing this internal behavior is essential for elucidating the invisible failure mechanisms and the spatial interaction between the support structure and the surrounding soil. To address this limitation, this study develops an integrated research framework utilizing fused quartz sand and pore fluid with matching refractive indices to synthesize transparent soil. A reduced-scale shield tunneling model test was conducted, combining digital image correlation, 3D reconstruction algorithms, and comparative finite element analysis. This framework enables full-field visualization and quantitative analysis of pipe-soil interaction mechanisms under varying burial depths. The results reveal that face instability follows a deformation evolution pattern characterized by distinct zones and stages. The strengthening effect of pipe-roof in tunneling is attributed to the "soil-arching barrier effect," as the pipe-roof would restrain failure wedge development by extending the stress transfer path. Unlike previous studies, this work captures the spatial characteristics of the deformation field and provides direct visual evidence of the support mechanism. These findings hold significant implications for safe construction practices in urban tunneling.
The effective detection and early warning of freezing pipe fracture accidents remain critical challenges in the application of artificial freezing technology. Using the fluid-structure interaction module in analysis system finite element software, this study simulates the generation and propagation of vibration signals during freezing pipe fractures. A systematic investigation is conducted on the effects of key parameters, including frozen soil elastic modulus, brine inlet pressure, and external pipe pressure, on signal propagation and attenuation characteristics. Additionally, the influence of excitation force magnitude and loading duration on signal frequency characteristics is explored. The results indicate that guided wave propagation characteristics in freezing pipes remain stable, with low attenuation rates for low-frequency signals. The frequency-domain characteristics of these signals can serve as critical indicators for pipeline condition diagnosis. The relative influence of frozen soil elastic modulus and brine inlet pressure on signal attenuation is limited. Although increasing the elastic modulus intensifies signal attenuation, the overall impact remains minor, with only a 7.8% reduction in terminal signal amplitude when the modulus reaches 900 MPa. Higher brine inlet pressure further increases attenuation, with an approximately 6.2% reduction in terminal signal amplitude at 8 MPa pressure. External pipe pressure significantly influences signal propagation by amplifying attenuation and dispersion effects. Increased external pressure leads to waveform broadening, waveform superposition, and intensified dispersion, resulting in much higher signal attenuation than that caused by elastic modulus and brine inlet pressure. At a propagation distance of 144 m, the echo amplitude decreases significantly, indicating that external pressure plays a decisive role in signal attenuation. Increasing the excitation force enhances signal amplitude, improves resistance to external interference, and facilitates long-distance propagation. A 5 kN excitation force generates primary frequency signals ranging from 110 to 799 Hz, with longer loading durations leading to lower primary frequencies. The findings of this study provide essential technical support for the safety management, real-time monitoring, and early warning systems of artificial freezing construction. Additionally, they offer a theoretical foundation for the integrated development of frozen soil engineering, underground engineering, and signal processing technology.
Accurate prediction of hard rock pillar stability is vital for ensuring safety in underground mining operations. This study presents a novel hybrid stacking ensemble framework that integrates six machine learning models optimized by the Sparrow Search Algorithm (SSA) at both base and meta levels. A comprehensive dataset of 331 pillar cases was compiled from published case histories reported in 10 underground mines, characterized by five key geotechnical features. Seventy percent of the data were used for model training and the remaining 30% for testing. Performance was evaluated using five classification metrics, including accuracy, precision, recall, F 1-score, and kappa. Statistical comparisons using the Friedman test and Nemenyi post-hoc test confirmed the LightGBM-meta stacking model as the top-performing approach, achieving 93.0% accuracy, 0.937 F 1-score, and a kappa of 0.858. SHapley Additive exPlanations (SHAP) identified pillar stress and uniaxial compressive strength (UCS) as the most influential features, enhancing model interpretability and engineering insight. A user-friendly graphical user interface (GUI) was developed to enable practical deployment by mining engineers. The model was further validated on 11 field pillar cases from the Sanshandao Gold Mine, with parameters obtained via 3D laser scanning, in-situ stress measurement, and laboratory UCS testing. All 11 field cases were correctly classified. Overall, the proposed SSA-optimized stacking framework bridges data-driven prediction and field deployment, offering practical support for pillar design and stability risk management.
The dynamic compressive strength () of rocks exhibits sensitivity to strain rate (), which is critical for understanding failure mechanisms in blasting, tunneling, and seismic response. Conventional empirical and deterministic models often struggle to generalize across diverse lithologies and loading conditions. This study develops an interpretable and probabilistic ensemble learning framework to predict using a curated dataset covering common rock types and experimental conditions incorporating static compressive strength (), , P-wave velocity, bulk density, grain size, and specimen geometry. Ensemble models including random forest, extra tree, and extreme gradient boosting were benchmarked against uncertainty-aware methods namely natural gradient boosting (NGBoost) and Gaussian process regression, with hyperparameters optimized via Bayesian optimization. NGBoost with decision tree base learner achieved the best test performance (Coefficient of determination = 0.954, root mean square error = 13.29 MPa, and mean absolute error = 8.59 MPa), demonstrating strong generalization. Model interpretability analyses (shapley additive explanations, permutation importance, and partial dependence) identified and as the most influential features, while geometric factors had minimal impact. The proposed framework provides a robust and interpretable tool for modeling rate-dependent rock strength in dynamic geomechanical applications.
Faults and periodic weighting pose a serious threat to the stability of the surrounding rock in deep coal mine roadways. To reveal the evolution of failure in the surrounding rock in the deep cross-fault roadway during excavation support and periodic weighting, this research employs a systematic analysis integrating physical model testing and numerical simulation. First, we built a physical model replicating field conditions, with digital image correlation (DIC), strain, and acoustic emission (AE) systems to monitor displacement, strain, and rock failure. Then, a numerical model of the actual mining area was established to further investigate the response characteristics of the surrounding rock stress field, displacement field, plastic zone, and support structure. The results during the excavation support stage show that the surrounding rock displacement primarily follows a quasi-hyperbolic distribution, with tensile failure predominating. Displacement and compressive stress in the roof increase while the sidewalls' compressive stress decreases. The axial stress of anchor cables increases at the fault plane. The results during the periodic weighting stage show that: Roof z-displacement propagates horizontally downward, with strain exhibiting step-like jumps. The extent of damage to the roof of the hanging wall, the floor, and the sidewalls of the foot wall is intensified. The location of AE events is influenced by the strike and dip of the fault. Shear failure dominates in sandstone at the fault plane, while tensile failure prevails in mudstone. Moreover, the axial stress in both bolts and cables generally increases, but locally decreases in the region where roof cables intersect the fault plane.
Disparities in oil production among wells within the same oilfield often arise from differences in geological structures, production modes, and environmental conditions. These inconsistencies present major challenges for accurate and robust modeling and forecasting of multi-well productivity. To address this issue, this paper proposed a deep time series modeling framework called temporal and adaptive-frequency network with MixStyle (TAMNet), which combines transformer and long short-term memory (LSTM) architectures for comprehensive temporal feature learning. First, a temporal gate unit (TGU) is introduced to enhance the model's ability to capture dynamic production trends. By adopting time gating, TGU selectively extracts informative time-step features, improving sensitivity to temporal fluctuations and enabling more precise short-term modeling. Second, an adaptive frequency transform (AFT) module is designed to incorporate frequency-domain information into temporal representations. This allows the model to effectively capture periodic patterns commonly observed in oil production signals, thereby enhancing its perception of long-term production behaviors. Finally, to improve generalization across heterogeneous well domains, we incorporate a MixStyle-based domain generalization mechanism. By perturbing intermediate feature statistics during training, this technique encourages the model to learn domain-invariant representations, reducing overfitting and improving adaptability to unseen conditions. Through extensive experiments on real-field datasets, TAMNet shows strong predictive performance in multi-well productivity prediction compared to the state-of-the-art baseline models. Whether in the same oilfield area or different oilfield areas, the prediction results of TAMNet can effectively fit the changing trend of production, showing good stability and cross-domain generalization ability.
This study analyzed the feasibility of using titanium (Ti) tailings as a backfilling material in an underground mine. The backfilling ratio tests were performed using Ti tailings as the backfilling aggregate and flotation tailings, fly ash, and phosphogypsum as auxiliary materials. The uniaxial compressive strength (UCS) of the backfill body under different curing times was measured. Ninety datasets, including UCS, ratio parameters, and curing time, were collected. According to these datasets, extremely randomized trees (ERT) and four optimization algorithms were used to build four hybrid models to predict the UCS of the backfill body. A five-fold cross-validation was adopted to verify the validity of the model. The testing R 2 values of the four hybrid models were greater than 0.950. The slime mold algorithm-ERT (SMA-ERT) model achieved the best performance (R 2 = 0.970). To analyze the superiority of these hybrid models, eight machine learning (ML) models (random forest, support vector machine, etc.) were also developed. Four hybrid models outperformed these eight ML models on the testing datasets. In addition, an intelligent optimization design system was developed to ensure strength compliance in mix proportioning. This indicated that the proposed hybrid models were effective tools for predicting the UCS of the backfill body. Finally, the SMA-ERT model was used to determine the optimal backfilling ratio parameters for achieving a backfill body with a UCS of 2.0 MPa after 28 days of curing. The rationality of the ratio parameters was verified by tests. The testing results showed that the backfill body prepared using the ratio parameters provided by the SMA-ERT model can achieve a UCS of 2.02 MPa. This result suggested that the SMA-ERT model is the most effective tool for UCS prediction, and can reliably guide the design of the backfill ratio parameters.
This study proposes an upper-bound limit analysis framework to evaluate the stability of coral reef limestone cavern roofs. However, existing roof stability analyses are predominantly based on pure-shear failure mechanisms, which may be inadequate for coral reef limestone with exceptionally low tensile strength; therefore, a tension-shear composite failure mechanism is required. The framework introduces a composite tension-shear failure mechanism incorporating a tensile-strength cut-off modification to the classical Hoek-Brown failure criterion. To address the Hoek-Brown criterion's tendency to overestimate tensile capacity, a tensile-strength cut-off coefficient is applied in the tensile regime, remedying this discrepancy while preserving the original formulation for shear-dominated conditions. In the proposed mechanism, regions under tensile normal stress are governed by a tensile failure law, whereas regions under compressive normal stress follow a shear failure law. This combined tension-shear failure mechanism accurately captures the progressive collapse behavior of coral reef limestone cavern roofs, which exhibit exceptionally low tensile strength. A hybrid optimization algorithm is employed to compute the required supporting force for four distinct tension crack models, considering both continuous and discontinuous failure modes. The analytical predictions are validated through comparisons with numerical simulations and published results from the literature. A systematic parametric analysis is then conducted to examine the influence of rock mass strength, crack morphology, and the tensile-strength cut-off coefficient on the failure mechanism of coral reef limestone cavern roofs. The results indicate that, for coral reef limestone cavern roofs, the proposed tension-shear composite failure approach yields systematically more conservative outcomes supporting pressure predictions than the pure-shear failure mechanism typically used for terrestrial rocks. Among the investigated crack models, the "Crack 1" model-characterized by a straight tension crack aligned with the shear failure surface-produces the most conservative support requirements and is recommended for the design of coral reef limestone caverns.
Among different kinds of infrastructures, tunnels are responsible for a significant proportion of carbon emissions; therefore, the carbon emission reduction of infrastructure is crucial for achieving global carbon neutrality. Previous studies often focused on a particular type of tunnel only, without considering and comparing the carbon emissions of different types of tunnels over the construction and operation phases. This paper compares the carbon emissions across the four common types of tunnels, namely, shield tunnels, New Austrian Tunnelling Method (NATM) tunnels, cut-and-cover tunnels, and immersed tunnels, during the construction, operation, and maintenance (O&M) phases. A consistent carbon accounting framework is established, and a case study of a subsea tunnel project in China is conducted to quantify emissions and perform sensitivity analysis. The results show that the construction phase accounts for approximately 60.3% of the total, while the O&M phases contribute around 39.7%. Among the three feasible schemes, the shield tunnel exhibits the lowest unit-length carbon emissions (33.59 t CO2/m), followed by the NATM tunnel (35.89 t CO2/m) and the immersed tunnel (90.99 t CO2/m). Sensitivity analysis identifies the concrete and electricity emission factor and tunnel segment length as the most influential parameters. Finally, this paper further suggests several key carbon reduction measures to contribute to the sustainability of low-carbon tunnels.
Influenced by the superposition of mining on double key strata, the gob-side entry in the fully mechanized top-coal caving mining of extra-thick coal seams is highly susceptible to severe ground pressure phenomena, such as rock bursts and significant roadway deformations. This area represents a critical focus for prevention and control during the mining process. Therefore, this article focuses on the fully mechanized caving mining of thick coal seams at the Caojiatan mine. It analyzes the distribution and migration characteristics of the key stratum in the overlying stratum and establishes a mechanical model of the transverse pressure bearing structure in the mining area. The analysis covers the transition of the transverse pressure bearing structure through four states, from a virtually stable state to an unstable state, filling the gap left by traditional S-R theory in analyzing the transverse pressure bearing structure in the mining area, and revealing the mechanism of strong mining pressure manifestation along the gob-side entry in the ultra-thick coal seam under the influence of double key strata. The results indicate that the main factors affecting the manifestation of strong mining pressure along the goaf are the pressure-boosting effect caused by the instability of the double-key strata, the excessively long cantilever length of the lateral pressure-bearing structure, and the underfilling of the goaf gangue. To this end, a combination of directional energy accumulation blasting and enhanced blasting for roof cutting and pressure relief technology was proposed to reduce the cantilever length of the pressure-bearing structure and increase the filling degree of collapsed gangue, thereby reducing the pressure of the roadway to control deformation. The effect of this technology was comprehensively studied using numerical simulation and on-site experiments, verifying the effectiveness of this technology in controlling the deformation of the roadway surrounding rock. The peak pressure decreased by a maximum of 18.5%, and the average step distance decreased by 41%. The maximum reduction in tunnel deformation is 81.9%. This study provides a scientific basis for the deformation control of the roadway under similar conditions.
To address the issues of reliance on static parameters and weak early warning in unmined areas in the risk prediction of water inrush from the bottom slab of deep ultra-wide working faces, a dynamic mulit-source monitoring-driven water inrush risk prediction situation awareness system was constructed, taking the 400 m ultra-wide working face of Shandong Binhu Coal Mine as the research object. A microseismic-electrical method coupling monitoring system was set up to collect data on microseismic depth, energy, and resistivity of the bottom rock strata, and training labels were created through spatio-temporal alignment and information entropy superposition. Meanwhile, 10 control factors were selected to construct a dynamic and static fusion feature matrix. The whale optimization algorithm-convolutional neural network model was adopted to optimize the hyperparameters and establish the mapping relationship between features and risks. The results show that when the model advances 370, 550, and 820 m in the working face, the root mean square error gradually decreases to 0.0586, the prediction accuracy of the unmined area reaches 91.54%, and the performance is stable when the data volume fluctuates. This system studies the precise identification of high-risk areas, guides on-site measures to ensure safe mining, realizes full-time and spatial dynamic inversion, and early warning of water inrush risks in deep and ultra-wide working faces, and provides technical support for the prevention and control of water hazards in deep mining and underground engineering.
Granite is commonly used as an engineered construction material and a natural geological barrier to prevent radionuclide migration in deep geological disposal of high-level radioactive waste (HLW). However, the exothermic decay of radionuclides during disposal may threaten the stability and integrity of natural geological barriers. In this study, the tensile strength and acoustic emission (AE) signal of Beishan granite were investigated through Brazilian splitting tests (BST) at various treatment temperatures. The results revealed a significant deterioration in Brazilian tensile strength (BTS) with increasing temperature, with an 88% reduction in BTS at 1000 degrees C. The most pronounced degradation occurred between 400 and 600 degrees C, where the damage variable (D t) increased by 0.3. As the treatment temperature increased, there was a general rise in the AE peak events, the cumulative number of AE events, and the AE b-value of Beishan granite during the BST, with tensile events being predominant through the rise time/amplitude ratio (RA)-average frequency (AF) distribution. Additionally, it was found that the orderliness of the scatter distribution of the AE energy during the loading process increased as the treatment temperature rose. These findings underscore the critical influence of thermally induced microcracking on the long-term performance and safety of granite as a geological barrier for HLW disposal.
