International Journal of Rock Mechanics and Mining Sciences最新文献

Post-peak behaviour is crucial for the estimation of rock mass fracturing in cave mining operations where hard rocks can exhibit class-II or snap-back response when subjected to loading. Despite the rapid development of research into class-II rocks under compression, the corresponding behaviour in tensile tests has rarely been investigated, which is critical considering the complexity of rock mass fracturing under various stress states. The post-peak response of brittle rocks involves abrupt micro-fracturing, leading to brittle macro-scale behaviour. Controlling the fracture process using the Advanced Universal Snap-Back Indirect Tensile test (AUSBIT) allowed the acquisition of the complete macro-scale class-II behaviour in the post-peak regime, facilitating the use of advanced techniques for insights into both micro and macro-scale fracture. In this study, the AUSBIT tests with digital image correlation (DIC) and acoustic emission (AE) instrumentation were conducted to analyse the progressive failure in Calca granite and Gosford sandstone specimens. Post-test observations of the fracture surfaces were performed using a scanning electron microscope (SEM). From a macroscale viewpoint, the lateral strain control in AUSBIT enabled controlled cracking with significant lateral strain extension prior to failure accompanied by gradual energy dissipation and higher rates of AE activity as smaller magnitudes of energy are being released by each AE hit or microcrack compared to conventional Brazilian tests. The stable microcrack propagation was also identified from SEM observations with more uniform profiles of microcracks and less debris observed in AUSBIT specimens. These findings were more significant in Calca granite, which verified its extreme class-II behaviour while also demonstrating the efficiency of AUSBIT in controlling the violent failure of high-strength brittle rocks commonly encountered in deep mining projects, leading to the acquisition of more accurate material behaviour in terms of micro and macro-scale post-peak features which was unattainable from conventional indirect tensile tests.

In the geo-energy industry, fluid injection induces different slip behaviors of a rock fracture, from aseismic creep to dynamic slip. The transition from aseismic creep to dynamic slip is explained by the ratio of the stiffness of surrounding rock and the critical stiffness of the fracture. However, numerous studies suggest multiple controls affecting the slip behaviors, and their joint influences on the slip transition remain unclear. Here we trained a dual-stage attention-based recurrent neural network model using fluid injection experimental data to explore the dominant factor controlling the slip behaviors. Our results showed that the dominant factor changes during fluid injection, and the attention to shear stress dominates the occurrence of dynamic slip. We found that high fluctuations of the attentions to normal stress, shear stress, and water pressure gradient promote the slip transition. Our model was applied to explore the competing process between water pressure front and aseismic creep front while gradually increasing the injection pressure and to reveal the dynamic change in the dominant factor during the growth of cumulative moment release.

Sc-CO₂ fracturing would be a potential stimulation method for Hot Dry Rock. A series of Sc-CO₂ fracturing experiments were performed on granite under different temperature and stress conditions. Quantitative and qualitative analysis of injection pressure curves and cracks were conducted to explain the Sc-CO₂ fracturing mechanism under high temperature and high stress conditions. Under the same stress conditions, as the temperature increases, the breakdown pressure decreases. Concurrently, the volume and length of macro-cracks on the sample surface decrease, whereas the volume of micro-cracks within the sample increases. Under the same temperature conditions, as the stress increases, the breakdown pressure increases. However, this increasing trend is less noticeable at high temperatures. Compared with hydraulic fracturing, due to the lower density and viscosity of CO₂, Sc-CO₂ fracturing takes longer from injection to breakdown and has lower breakdown pressure. The effect of high temperature on fracturing mainly manifests in the generation of microscopic thermal cracks and a reduction in viscosity and density of Sc-CO₂. Low viscosity and low density CO₂ are more likely to penetrate into the thermal cracks of the sample, generating a diffuse micro-crack network, leading to an increase in pore pressure and a reduction in effective stress near the wellbore. Consequently, there is propagation of these micro-cracks, resulting in an increase in the volume of micro-cracks while the volume and length of macro-cracks decrease, ultimately leading to a decrease in breakdown pressure. High stress primarily influences the fracture process by reducing the opening width of microscopic thermal cracks. This reduction inhibits the diffusion of Sc-CO₂ through these cracks, ultimately leads to an increase in breakdown pressure. The findings of this experimental study provide a theoretical basis for efficient fracturing and crack creation in hot dry rock reservoirs.

Stress change in rock mass caused by human activities has the potential to cause the sliding and destruction of faults and joints, resulting in induced seismicity. Laboratory experiments are conducted on a simulated fault with various teeth numbers and undulation angles to uncover the mechanism of stress change-induced seismicity. The potential risk of induced seismicity is explained using three methods: the Mohr-Coulomb failure criterion, localization of stress concentration regions, and visualization of maximum shear stress reduction through photoelasticity. Experimental results indicate that the friction coefficient increases with the undulation angle, and the form of stress change has an unignorable impact on frictional instability. The friction coefficient in the vertical unloading process is slightly lower than that in the loading process and larger than that in the shear unloading process. Loading is the stress change caused by shear displacement under constant normal stiffness conditions and unloading is the process of reducing the stress by controlling the position of the boundary constraints in the corresponding direction. Meanwhile, unloading in the shear direction has both seismic and aseismic features. Although the rapid drop of shear stress at the onset of shear unloading may induce fault instability, the reduction of normal stress and the restoration of displacement prove that unloading in the shear direction may also reduce the risk of fault failure in the subsequent process. In addition, the stress concentration region is mainly distributed perpendicular to the contact surface rather than the entire fault. This research is conducive to promoting the application of photoelasticity in studying induced seismicity and provides a practical method for calculating the energy released during such events. Based on the morphological characteristics and stress states of fault surfaces, the findings can be utilized in engineering practice to assess the risk of induced seismicity under different stress change conditions.

Various novel assisted drilling technologies enhance rock fragmentation performance by introducing microcracks on the rock surface to weaken rock strength. However, the quantitative relationship between artificially induced microcracks and rock fragmentation characteristics is not clear. In this study, we induced artificial microcracks of varying degrees on the rock surface through one-dimensional heat conduction. With the aid of the fluorescent resin, we visualized the microcrack patterns and quantitatively assessed the artificially induced microcracks. Subsequently, we performed quasi-static indentation tests on granite samples containing microcracks to establish the quantitative relationship between microcracks and rock fragmentation performance. The results indicate that the release of crystal water within the temperature range of 200 °C–300 °C is the primary factor leading to a significant increase in microcracks. Load drop signals correlate with the propagation of microcracks, including the competitive interactions between mechanically induced microcracks and artificially induced microcracks. Artificially induced microcracks require a certain initial length to continue propagating under mechanical stress, and excessively short microcracks are detrimental to subsequent propagation. A higher density of microcracks implies a more complex microcrack network, facilitating the merging of cracks to form rock chips under smaller mechanical loads. The consistency between the length density and number density of microcracks in influencing the crater parameters reflects their equal importance in affecting rock fragmentation performance. These findings could help determine the extent of rock weakening by artificially induced microcracks and reveal the mechanisms of rock fracture behavior influenced by microcrack, holding significant implications for the optimization of the process parameters of various assisted rock drilling techniques.

Peridotites (olivine-rich rocks) naturally react with CO₂-rich fluids to eventually form carbonates. Complete conversion involves incorporation of substantial amounts of CO₂, which requires prolonged fluid flow. Yet, these reactions also cause a large increase in solid volume (63–84 %), raising questions on how they proceed in nature without this excess solid volume clogging fluid pathways. It has been suggested that reaction-driven fracture, caused by development of crystallization pressure, facilitates continual creation of new pathways, allowing reaction to advance. If indeed so, this could enable injection of industrially captured CO₂ into peridotites for permanent sequestration. However, such a fracturing mechanism has not been reproduced experimentally. Here, we report nine reactive flow-through experiments, performed on pre-compacted Åheim dunite powder (∼88 % olivine) inside a 1D oedometer vessel, to simultaneously measure axial deformation and permeability development. Tests were performed at 150 °C and effective axial stresses of 1–15 MPa. After initial flow measurements using deionized water at 10 MPa, during which permeability and deformation remained unchanged, the samples were exposed to inflow of reactive fluid. Samples subjected to CO₂-saturated brine/water or NaHCO₃-saturated solution showed minor compaction (0–0.38 %), while permeability decreased from 10⁻¹⁶-10⁻¹⁷ to 10⁻²⁰-10⁻²¹ m². Microstructural and chemical analyses demonstrate a drastic reduction in porosity of the reaction zone where carbonation occurred. A reference sample exposed to NaHSO₄ solution (acidification, but no carbonation) instead showed slightly increased permeability, from 3 × 10⁻¹⁷ to 8.2 × 10⁻¹⁷ m², associated with 0.05 % compaction strain. Combined, the observations suggest dissolution of olivine at the grain contacts, leading to minor mechanical compaction, followed by precipitation of carbonates inside the remaining pores, clogging transport paths and thus reducing permeability. This indicates volume-increasing precipitation upon olivine carbonation under subsurface conditions clogs transport paths at laboratory timescales, severely limiting reaction rates and thus potential for crystallization pressure development and reaction-driven fracture.

Seismic monitoring routines provide a robust framework for assessing rock stability and dynamic hazards in underground mining operations. However, the labor-intensive task of manually identifying wave arrivals and the suboptimal selection of geophone arrays do not meet the stringent timeliness and accuracy necessary for seismic source location in such contexts. The precise identification of wave arrivals in mining-induced seismicity and the automated selection of an optimal geophone array have emerged as critical challenges in achieving high-performance seismic monitoring in underground mines. To address these challenges, this paper introduces a novel deep transfer learning approach for identifying seismic wave arrivals, and developing an automatic geophone array selection method for seismic source localization in underground mines. First, an initial deep-learning model was constructed using a substantial seismic dataset comprising global earthquakes, designed to detect the arrival of seismic waves automatically. Then, a deep transfer learning process was applied, leveraging a seismic dataset of over 8,000 carefully picked P-wave arrivals from mining environments. This additional training enabled the model to adapt to the unique characteristics of mining-induced seismicity. In parallel, we introduced an innovative method to select geophone arrays based on mine-planned blasting sources. This approach determines the geophone array that minimizes location errors while reducing the standard deviation of P-wave arrivals compared to historical blasting sources. The effectiveness of this method was validated using recorded blasting data from a longwall panel in an underground coal mine. The results demonstrated a median horizontal locating error of 48.95 m, which can be further minimized to a range of 0 m to 17.63 m when considering systematic biases in seismic monitoring. These findings confirm the practicality and feasibility of our method, offering a valuable solution for the automation and enhancement of high-precision seismic monitoring in underground mining operations.

Due to the thin overlying formation of ultra-shallow buried large-span urban tunnels, the surrounding rock can be easily loosened and damaged after excavation. High prestressed anchoring support can improve the self-bearing capacity of the surrounding rock. However, traditional bolts often undergo necking fracture after the yield stage and strengthening stage, and the designed pre-tension is low, generally not exceeding 50 % of the yield strength of the bolt. To this end, our research group developed a new NPR bolt. After comparing the mechanical properties of the NPR bolt and the traditional bolt, the high strength, high elongation, and high prestressed properties of the NPR bolt are revealed. Subsequently, the constitutive relationship of the CABLE element in FLAC3D is modified, and the constitutive model of the NPR bolt is established, which can describe the whole process of bolt. Furthermore, a numerical comparison of prestressed control for ultra-shallow large-span tunnels is carried out. Compared with non-prestress, when the prestress of bolt reaches 100 kN, the area of tensile stress zone and plastic zone of surrounding rock are reduced by 79.6 % and 73.7 %, respectively. At the same time, the settlement of surface and roof is reduced by 66.7 % and 64.1 %, respectively. The control mechanism of the prestressed NPR bolt support is explored, and the design method for ultra-shallow large-span tunnels is proposed. The field application and monitoring results show that the surface and roof settlement of the tunnel supported by NPR bolts are 3.2 mm and 6.3 mm, respectively. The safe and effective stability control of the tunnel surrounding rock is achieved.

Seismic events associated with longwall coal mining have not been comprehensively understood, in particular the spatial-temporal relationship between mining-induced seismic events. Current studies on longwall mining-induced seismicity have proposed various spatial-temporal relationships between seismic events, but they may not explicitly follow the Gutenberg–Richter (GR) law for seismic magnitude distribution. This study applies a modified GR law to describe longwall mining-induced seismicity by considering the spatial-temporal distance between events. The closest event pair for each seismic event, which is the most probable source to trigger this event, is determined based on the spatial-temporal distance using the nearest neighbour method. A threshold based on the spatial-temporal distance is set via the trial-and-error method, enabling seismic events to be classified into triggering events and non-triggering events. Two groups of seismic events from the classification are further tested and proved to be valid by the temporal Bi-test and spatial Ripley's K function. The temporal Bi-test and spatial Ripley's K function demonstrate a greater tendency for clustering among triggering events and more randomness among non-triggering events. Our analysis of seismic events associated with longwall mining reveals that triggering events account for 60 % of all seismic events, making up a significantly higher percentage than that in earthquake seismology. The event family tree analysis suggests that a single mining-induced seismic event could generate up to five generations in the event triggering catalogue, and the average moment magnitude between each generation decays exponentially. We also find that the triggering between high-energy events is non-local, manifested as the propagation of discontinuities from different ends of the same fault. In addition, high-energy events may not necessarily be triggered by their closest precedent event but by the combined effects of mining activities and discontinuities. This study provides significant implications for the relationship between seismic events in mining engineering.

Rock discontinuities significantly impact the mechanical and hydraulic behavior of rock masses. A crucial aspect of rock engineering involves classifying discontinuities with similar orientations into groups. For this purpose, clustering algorithms, such as K-Means and Fuzzy K-Means (FKM), have been employed. However, the outcomes of these algorithms are influenced by the selection of initial cluster centers. This paper proposes an improved FKM algorithm to automatically identify rock discontinuity sets based on the Fisher distribution (FFKM). The algorithm uses the Fisher distribution to generate and select appropriate initial cluster centers. The performance of FFKM was initially validated using a published data set, and its results were compared with other clustering methods commonly used for grouping discontinuities. Results demonstrated the superior performance of FFKM over the FKM algorithm, comparable to other methods. Subsequently, the proposed algorithm was employed to analyze a fracture data set sampled at an open-pit iron mine. The FFKM facilitated identifying the correct number of sets and produced results consistent with the fracture sets observed in the field. Finally, the algorithm was verified using an artificial discontinuity data set, and the results demonstrated that the method correctly identified the number of sets and provided discontinuity sets similar to the original data set. The FFKM algorithm offers significant advantages: it maintains the essential characteristics of the FKM algorithm, effectively addresses the challenge of selecting suitable initial cluster centers, requires only the expected number of discontinuity sets as an input parameter, processes data within an acceptable computation time, serves as a tool for defining the number of discontinuity sets, and mitigates the drawbacks of the stereographic projection method.