Rock Mechanics Bulletin最新文献

In this study, laboratory testing and numerical simulation methods are used to investigate the mechanical behavior and perform fracture prediction of a novel high-strength and high-toughness steel with a negative Poisson's ratio (NPR) effect under combined tensile-shear loading conditions. A test device capable of meeting different tensile-shear combination test angles is designed and manufactured, wherein the mechanical experiments on the NPR (Negative Poisson's Ratio) steel specimens are carried out at various testing angles. Q235 steel and MG400 steel are used as experimental control groups. The results show that the mechanical deformation of NPR steel is significantly better than that of Q235 steel and MG400 steel. Its tensile-shear test curve has no yield plateau and it has quasi-ideal elastic-plastic mechanical properties. The loading direction gradually changes from tension-dominated to shear-dominated as the tension-shear angle increases, and the strength and deformation of the specimens show a decreasing trend. Based on the laboratory test results, a finite element numerical model of NPR steel is established. A series of numerical simulations are carried out under the conditions of different tension and shear angles and the average stress triaxiality and fracture strain data are obtained. The fracture data of NPR steel are fitted using the Johnson-Cook fracture criterion, and the Johnson-Cook fracture parameters under the tensile-shear test conditions of NPR steel are thus obtained. The numerical simulation verifies that the fracture model can accurately predict the tensile-shear fracture behavior of NPR steel.

With the growing prominence of recycling projects of groundwater, the attention towards subsidence concerns in geological formations is intensifying. However, due to the long evolutionary time and complex underground discontinuities, the deformation field and subsidence mechanism are difficult to obtain. To this concern, this study implemented the Hydro-Mechanical Coupled Discontinuous Deformation Analysis (HM-DDA) in a groundwater recycling project located at a goaf mining site. The method for establishing numerical stratigraphic models and determining the required numerical parameters is introduced. This contributes to the comprehensive reconstruction of changes in in-situ stress within the goaf area, encompassing the initial stress equilibrium state, as well as the processes of water pumping and injection. The results indicated that the water injection process mitigated stress concentrations at both ends of the goaf area. Specifically, a 30-m rise in water head resulted in a corresponding elevation of the ground surface by 3.94 ​cm.

Landslides are one of the geological disasters with wide distribution, high impact and serious damage around the world. Landslide risk assessment can help us know the risk of landslides occurring, which is an effective way to prevent landslide disasters in advance. In recent decades, artificial intelligence (AI) has developed rapidly and has been used in a wide range of applications, especially for natural hazards. Based on the published literatures, this paper presents a detailed review of AI applications in landslide risk assessment. Three key areas where the application of AI is prominent are identified, including landslide detection, landslide susceptibility assessment, and prediction of landslide displacement. Machine learning (ML) containing deep learning (DL) has emerged as the primary technology which has been considered successfully due to its ability to quantify complex nonlinear relationships of soil structures and landslide predisposing factors. Among the algorithms, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two models that are most widely used with satisfactory results in landslide risk assessment. The generalization ability, sampling training strategies, and hyper-parameters optimization of these models are crucial and should be carefully considered. The challenges and opportunities of AI applications are also fully discussed to provide suggestions for future research in landslide risk assessment.

During the construction and operation of a pumped storage power station in an abandoned mine, the soft rock-coal body structure, comprising the roof and the residual coal pillars, encounters a complex stress environment characterized by cyclic loads. The study of its failure mechanism under cyclic dynamic loading holds significant theoretical and practical importance to stay the safety and stability of the abandoned mine pumped storage power station. In this paper, we take “roof-residual coal pillar” soft rock-coal combinations with different percentages of rock as the research object, and study their mechanical properties, failure mechanism, energy evolution characteristics and acoustic emission distribution characteristics through cyclic dynamic loading experiments. The results of the experiment indicate that: (1) Both weak cyclic dynamic loading and high rock percentage enhance the deformation resistance of soft rock-coal combinations. Under low-disturbance horizontal cyclic loading, its peak strength and modulus of elasticity increase with increasing rock percentage. (2) Under low-disturbance horizontal cyclic loading, an increasing trend is observed in the average total strain energy density, dissipation energy density, and elastic energy density of the combinations as the percentage of rock increases. (3) Under low-disturbance horizontal cyclic loading, as the percentage of rock increases in the soft rock-coal combinations, the degree of failure in the rock body part progressively intensifies, while the destruction of the coal portion progressively decreases. (4) The large number of acoustic emission signals are generated at the instant of destabilization and destruction of the coal-rock combinations, mainly dominated by the signals generated by the destruction of the coal body. Acoustic emission counts and absolute energy at key point N₂ decrease as the percentage of rock increases. The b value is primarily distributed in the cyclic dynamic loading stage and the failure stage, both displaying zones of sudden increase and sudden decrease in b value.

Bench blasting is commonly used in open-pit mining. Some design parameters such as positions of hole packing and caving holes have great influences on the blasting effects. In this work, with a hybrid discrete-finite element method, numerical simulations of bench blasting are conducted, capturing the whole continuous-discontinuous processes. Considering two engineering cases, the influences of hole packing and caving holes are evaluated. The numerical results not only lead to some improved designs by relocating the packing positions and caving holes but also indicate the reliability of the adopted numerical tools.

Blasting test research was conducted on iron ore specimens with variable line density charging structures. Computer tomography (CT), digital image processing, and three-dimensional model reconstruction techniques were used to analyze the damage characteristics of iron ore specimens after blasting based on the calculated number of box dimensions. The results show that increasing the variable line density section charge uncoupling coefficient reduces the overall damage to the specimen by up to 1.73%, indicating that the overall damage size negatively correlates with the size of the variable line density section charge uncoupling coefficient. The damage characteristics of iron ore specimens from different layers (uncoupled charging section, transition section, coupled charging section) have some variability; when the uncoupling coefficient of the uncoupled charging section was reduced, the uncoupled section of the center of the damaged layer increased and then reduced. In contrast, the transition section shows a trend of increase, and the coupled section shows a minor difference, fully demonstrating the change in the variable line density section of the uncoupling coefficient of the specimen blasting damage effects. This study concludes that in the actual blasting project, choosing a reasonable variable line charge density structure can make the release of explosive blast energy more uniform to efficiently and thoroughly use explosive power to improve the iron ore crushing effect.

For solving the computationally intensive problem encountered by the discrete element method (DEM) in simulating large-scale engineering problems, it is essential to establish a numerical model that can effectively simulate large-scale rocks. In this study, the coarse-graining effect of a linear-Mindlin with bonding model was studied in the unconfined compression strength (UCS) and Brazilian tensile strength (BTS) tests. We found that the main reason for the coarse-graining effect of the BTS tests is that the type I fracture toughness is positively correlated with the size of the particles. Based on the results analysis and fracture mechanics, the coarse-grained (CG) modeling theory was combined with a bonded particle model (BPM) for the first time and a coarse-grained bonded particle model (CG-BPM) was developed, which can be effectively used to model the tensile strength of large-scale rocks with different particle sizes. The excavation damage zone (EDZ) in an underground research laboratory (URL) was selected as an application case, which shows that the coarse-grained bonding model presented in this paper is more accurate and reliable than the traditional one in large-scale rock simulation, at least in the scenario where tensile failure is dominant.

Fracture initiation has been a challenging issue for fracturing deep and tight gas reservoirs, which generally requires a high breakdown pressure for hydraulic fracturing treatment. In this situation, fluid injections frequently have to terminate at the very beginning. Toward predicting and solving this issue, a novel fracturing system for deep and tight sandstone gas reservoirs was developed. The key components of the fracturing system and some criterion will be introduced, which can be used to optimize the ideal perforation locations along the landing part and select the right perforation strategy accordingly. The main components of the fracturing system include: (1) evaluating the log-based diagenetic rock typing and flow index; (2) 1D mechanical earth model; (3) calculating the breakdown pressure envelope along the well trajectory of the landing part; (4) calculating the optimal perforation directions along the landing part; (5) select the first set of perforation locations based on the log-based diagenetic rock typing and flow index; (6) narrowing down the first set of perforation locations to a second set of perforation locations based on the breakdown pressure envelope; (7) determining a perforation strategy based on breakdown pressure envelope and wellhead pressure safety limit in the second set of perforation locations. The computational framework to calculate the breakdown pressure envelope and optimal perforation directions is applicable to arbitrary well trajectory. The fracturing system has been used to provide pre-fracturing suggestions for wells landed in deep and tight sandstone reservoirs, which is very efficient for identifying locations with good rock typing and relatively low breakdown pressure. Also, it can indicate whether oriented perforation should be used further for alleviating breakdown issue. By taking these efforts and procedures, the fracturing success rate for deep and tight sandstone gas reservoirs can be improved, which has been verified in practice. Example studies from the field will be provided to demonstrate the fracturing system's performance and applicability.

Rockburst is a phenomenon where sudden, catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation process. Rockburst disasters endanger the safety of people's lives and property, national energy security, and social interests, so it is very important to accurately predict rockburst. Traditional rockburst prediction has not been able to find an effective prediction method, and the study of the rockburst mechanism is facing a dilemma. With the development of artificial intelligence (AI) techniques in recent years, more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst mechanism. In previous research, several scholars have attempted to summarize the application of AI techniques in rockburst prediction. However, these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction, or they do not provide a comprehensive overview. Drawing on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques, this paper conducts a comprehensive review of rockburst prediction methods leveraging AI techniques. Firstly, pertinent definitions of rockburst and its associated hazards are introduced. Subsequently, the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized, with emphasis placed on the respective advantages and disadvantages of each approach. Finally, the strengths and weaknesses of prediction methods leveraging AI are summarized, alongside forecasting future research trends to address existing challenges, while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

Rock abrasivity influences wear of cutting tools and consequently, performance of mechanized tunneling machines. Several methods have been proposed to evaluate rock abrasivity in recent decades, each one has its own advantages. In this paper, a new method is introduced to estimate wear of disc cutters based on rock cutting tests using scaled down discs (i.e. 54 and 72 ​mm diameter). The discs are made of H13 steel, which is a common steel type in producing real-scale discs, with hardness of 32 and 54 HRC. The small-scale linear rock cutting machine and a new abrasion test apparatus, namely University of Tehran abrasivity test machine, are utilized to perform the tests. Tip width of the worn discs is monitored and presented as the function of the accumulated test run to classify the rock abrasion. Abrasivity tests show that by increasing the UCS of the rock samples, wear rate is doubled gradually that reveals the sensitivity of the test procedure to the main parameters affecting the abrasivity of hard rocks. For the rocks with the highest UCS, the normal wear stops after performing 5 to 10 rounds of the tests, and then, deformation of the disc tip is detectable. Two abrasivity indices are defined based on the abrasivity tests results and their correlations with CAI and UCS are established. Comparison of the established correlations in this study with previous investigations demonstrates the sensitivity of the indices to the parameters affecting wear of the disc cutters and repeatability of the outputs obtained from abrasivity tests using scaled down discs. Findings of this study can be used to enhance the accuracy of rock abrasivity classifications.