Journal of Rock Mechanics and Geotechnical Engineering最新文献

Grouting is a widely used approach to reinforce broken surrounding rock mass during the construction of underground tunnels in fault fracture zones, and its reinforcement effectiveness is highly affected by geostress. In this study, a numerical manifold method (NMM) based simulator has been developed to examine the impact of geostress conditions on grouting reinforcement during tunnel excavation. To develop this simulator, a detection technique for identifying slurry migration channels and an improved fluid-solid coupling (F–S) framework, which considers the influence of fracture properties and geostress states, is developed and incorporated into a zero-thickness cohesive element (ZE) based NMM (Co-NMM) for simulating tunnel excavation. Additionally, to simulate coagulation of injected slurry, a bonding repair algorithm is further proposed based on the ZE model. To verify the accuracy of the proposed simulator, a series of simulations about slurry migration in single fractures and fracture networks are numerically reproduced, and the results align well with analytical and laboratory test results. Furthermore, these numerical results show that neglecting the influence of geostress condition can lead to a serious overestimation of slurry migration range and reinforcement effectiveness. After validations, a series of simulations about tunnel grouting reinforcement and tunnel excavation in fault fracture zones with varying fracture densities under different geostress conditions are conducted. Based on these simulations, the influence of geostress conditions and the optimization of grouting schemes are discussed.

The through-diffusion and membrane behavior testing procedure using a closed-system apparatus has been widely used for concurrent measurement of diffusion and membrane efficiency coefficients of low-permeability clay-based barrier materials. However, the common assumption of perfectly flushing conditions at the specimen boundaries could induce errors in analyses of the diffusion coefficients and membrane efficiencies. In this study, an innovative pseudo three-dimensional (3D) analytical method was proposed to evaluate solute distribution along the boundary surfaces of the soil-porous disks system, considering the non-perfectly flushing conditions. The results were consistent with numerical models under two scenarios considering different inflow/outflow positions. The proposed model has been demonstrated to be an accurate and reliable method to estimate solute distributions along the boundaries. The calculated membrane efficiency coefficient and diffusion coefficient based on the proposed analytical method are more accurate, resulting in up to 50% less relative error than the traditional approach that adopts the arithmetic mean value of the influent and effluent concentrations. The retardation factor of the clay specimen also can be calculated with a revised cumulative mass approach. Finally, the simulated transient solute transport matched with experimental data from a multi-stage through-diffusion and membrane behavior test, validating the accuracy of the proposed method.

Anti-slide piles are one of the most important reinforcement structures against landslides, and evaluating the working conditions is of great significance for landslide mitigation. The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method. However, due to many assumptions involved in calculation, the analytical models cannot be fully applicable to complex site situations, e.g. landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein. In view of this, the combination of distributed fiber optic sensing (DFOS) and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area, China. Brillouin optical time domain reflectometry (BOTDR) was utilized to monitor the strain distribution along the pile. Next, by analyzing the relative deformation between the pile and its adjacent inclinometer, the pile-soil interface separation was profiled. Finally, the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method. According to the ratio of calculated internal forces to the design values, the working conditions of the anti-slide pile could be evaluated. The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system, and can quantitatively evaluate its working conditions.

Primary toppling usually occurs in layered rock slopes with large anti-dip angles. In this paper, the block toppling evolution was explored using a large-scale centrifuge system. Each block column in the layered model slope was made of cement mortar. Some artificial cracks perpendicular to the block column were prefabricated. Strain gages, displacement gages, and high-speed camera measurements were employed to monitor the deformation and failure processes of the model slope. The centrifuge test results show that the block toppling evolution can be divided into seven stages, i.e. layer compression, formation of major tensile crack, reverse bending of the block column, closure of major tensile crack, strong bending of the block column, formation of failure zone, and complete failure. Block toppling is characterized by sudden large deformation and occurs in stages. The wedge-shaped cracks in the model incline towards the slope. Experimental observations show that block toppling is mainly caused by bending failure rather than by shear failure. The tensile strength also plays a key factor in the evolution of block toppling. The simulation results from discrete element method (DEM) is in line with the testing results. Tensile stress exists at the backside of rock column during toppling deformation. Stress concentration results in the fragmented rock column and its degree is the most significant at the slope toe.

Great potential of underground gas/energy storage in salt caverns seems to be a promising solution to support renewable energy. In the underground storage method, the operating cycle unfortunately may reach up to daily or even hourly, which generates complicated pressures on the salt cavern. Furthermore, the mechanical behavior of rock salt may change and present distinct failure characteristics under different stress states, which affects the performance of salt cavern during the time period of full service. To reproduce a similar loading condition on the cavern surrounding rock mass, the cyclic triaxial loading/unloading tests are performed on the rock salt to explore the mechanical transition behavior and failure characteristics under different confinement. Experimental results show that the rock salt samples present a diffused shear failure band with significant bulges at certain locations in low confining pressure conditions (e.g. 5 MPa, 10 MPa and 15 MPa), which is closely related to crystal misorientation and grain boundary sliding. Under the elevated confinement (e.g. 20 MPa, 30 MPa and 40 MPa), the dilation band dominates the failure mechanism, where the large-size halite crystals are crushed to be smaller size and new pores are developing. The failure transition mechanism revealed in the paper provides additional insight into the mechanical performance of salt caverns influenced by complicated stress states.

Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety. This study developed a novel hybrid model (NHM) that combines a joint denoising technique with an enhanced gray wolf optimizer (EGWO)-ν-support vector regression (ν-SVR) method. High-embankment field measurements were preprocessed using the joint denoising technique, which includes complete ensemble empirical mode decomposition, singular value decomposition, and wavelet packet transform. Furthermore, high-embankment settlements were predicted using the EGWO-ν-SVR method. In this method, the standard gray wolf optimizer (GWO) was improved to obtain the EGWO to better tune the ν-SVR model hyperparameters. The proposed NHM was then tested in two case studies. Finally, the influences of the data division ratio and kernel function on the EGWO-ν-SVR forecasting performance and prediction efficiency were investigated. The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements. Simultaneously, the NHM outperforms other alternative prediction methods in prediction accuracy and robustness. This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field measurements. Moreover, the appropriate data division ratio and kernel function for EGWO-ν-SVR are 7:3 and radial basis function, respectively.

Coalburst is one of the most serious disasters that threaten the safe production of coal mines, and this disaster is particularly serious in China. This paper presents an overview of coalbursts in China since 1980s. From the "stress and energy" and "regional and local" perspectives, the achievements in the theory, practice and management of coalbursts in China are systematically summarized. A theoretical system of coalbursts has been formed to reveal the deformational behavior of coalbursts and explain the mechanism of coalbursts. The occurrence conditions of coalbursts are put forward and the critical stress is obtained. The stress index method for risk evaluation of coalbursts before mining is proposed, and the deformation localization prediction method of coalbursts is put forward. The relationship between energy release and absorption in the process of coalbursts is found, and the prevention and control methods of coalbursts, including the regional method, the local method and support, are presented. The safety evaluation index of coalburst prevention and control is put forward. The integrated prevention and control method for coal and gas outbursts is proposed. The prevention and control technology and equipment of coalbursts have also been developed. Amongst them, the distribution law of the critical stress in China coalburst mines is discovered. The technology and equipment for monitoring, prevention and control of coalbursts, as well as for integrated prevention and control of combined coalbursts and other disasters, have been developed. The energy-absorbing and coalburst-preventing support technology for roadways is invented, and key engineering parameters of coalburst prevention and control are pointed out. In China, coalburst prevention and control laws and standards have been developed. Technical standards for coalbursts are formulated, statute and regulations for coal mines are established, and regulatory documents are promoted.

The tensile strength at the rock-concrete interface is one of the crucial factors controlling the failure mechanisms of structures, such as concrete gravity dams. Despite the critical importance of the failure mechanism and tensile strength of rock-concrete interfaces, understanding of these factors remains very limited. This study investigated the tensile strength and fracturing processes at rock-mortar interfaces subjected to direct and indirect tensile loadings. Digital image correlation (DIC) and acoustic emission (AE) techniques were used to monitor the failure mechanisms of specimens subjected to direct tension and indirect loading (Brazilian tests). The results indicated that the direct tensile strength of the rock-mortar specimens was lower than their indirect tensile strength, with a direct/indirect tensile strength ratio of 65%. DIC strain field data and moment tensor inversions (MTI) of AE events indicated that a significant number of shear microcracks occurred in the specimens subjected to the Brazilian test. The presence of these shear microcracks, which require more energy to break, resulted in a higher tensile strength during the Brazilian tests. In contrast, microcracks were predominantly tensile in specimens subjected to direct tension, leading to a lower tensile strength. Spatiotemporal monitoring of the cracking processes in the rock-mortar interfaces revealed that they show AE precursors before failure under the Brazilian test, whereas they show a minimal number of AE events before failure under direct tension. Due to different microcracking mechanisms, specimens tested under Brazilian tests showed lower roughness with flatter fracture surfaces than those tested under direct tension with jagged and rough fracture surfaces. The results of this study shed light on better understanding the micromechanics of damage in the rock-concrete interfaces for a safer design of engineering structures.

In the existing landslide susceptibility prediction (LSP) models, the influences of random errors in landslide conditioning factors on LSP are not considered, instead the original conditioning factors are directly taken as the model inputs, which brings uncertainties to LSP results. This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP uncertainties, and further explore a method which can effectively reduce the random errors in conditioning factors. The original conditioning factors are firstly used to construct original factors-based LSP models, and then different random errors of 5%, 10%, 15% and 20% are added to these original factors for constructing relevant errors-based LSP models. Secondly, low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method. Thirdly, the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case. Three typical machine learning models, i.e. multilayer perceptron (MLP), support vector machine (SVM) and random forest (RF), are selected as LSP models. Finally, the LSP uncertainties are discussed and results show that: (1) The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties. (2) With the proportions of random errors increasing from 5% to 20%, the LSP uncertainty increases continuously. (3) The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors. (4) The influence degrees of two uncertainty issues, machine learning models and different proportions of random errors, on the LSP modeling are large and basically the same. (5) The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide susceptibility. In conclusion, greater proportion of random errors in conditioning factors results in higher LSP uncertainty, and low-pass filter can effectively reduce these random errors.

Static Poisson's ratio (ν_s) is crucial for determining geomechanical properties in petroleum applications, namely sand production. Some models have been used to predict ν_s; however, the published models were limited to specific data ranges with an average absolute percentage relative error (AAPRE) of more than 10%. The published gated recurrent unit (GRU) models do not consider trend analysis to show physical behaviors. In this study, we aim to develop a GRU model using trend analysis and three inputs for predicting ν_s based on a broad range of data, ν_s (value of 0.1627–0.4492), bulk formation density (RHOB) (0.315–2.994 g/mL), compressional time (DTc) (44.43–186.9 μs/ft), and shear time (DTs) (72.9–341.2 μs/ft). The GRU model was evaluated using different approaches, including statistical error analyses. The GRU model showed the proper trends, and the model data ranges were wider than previous ones. The GRU model has the largest correlation coefficient (R) of 0.967 and the lowest AAPRE, average percent relative error (APRE), root mean square error (RMSE), and standard deviation (SD) of 3.228%, −1.054%, 4.389, and 0.013, respectively, compared to other models. The GRU model has a high accuracy for the different datasets: training, validation, testing, and the whole datasets with R and AAPRE values were 0.981 and 2.601%, 0.966 and 3.274%, 0.967 and 3.228%, and 0.977 and 2.861%, respectively. The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges, which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.