Journal of Rock Mechanics and Geotechnical Engineering最新文献

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.

Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal. Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry. Discrete fracture networks (DFNs) are increasingly used in engineering analyses to spatially model fractures at various scales. The reliability of coal DFNs largely depends on the confidence in the input cleat statistics. Estimates of these parameters can be made from image-based three-dimensional (3D) characterization of coal cleats using X-ray micro-computed tomography (μCT). One key step in this process, after cleat extraction, is the separation of individual cleats, without which the cleats are a connected network and statistics for different cleat sets cannot be measured. In this paper, a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray μCT images. Kernels (filters) representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images. The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin, Queensland, Australia. It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation. Bedding-parallel fractures are also identified and separated, which has historically been challenging to delineate and rarely reported. A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling. Finally, variability and heterogeneity with respect to the core axis are investigated. Significant heterogeneity is observed and suggests that the representative elementary volume (REV) of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.

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.

The composite pile consisting of core-pile and surrounding cement-enhanced soil is a promising pile foundation in recent years. However, how and to what extent the cement-enhanced soil influences the ultimate lateral resistance has not been fully investigated. In this paper, the ultimate lateral resistance of the composite pile was studied by finite element limit analysis (FELA) and theoretical upper-bound analysis. The results of FELA and theoretical analysis revealed three failure modes of laterally loaded composite piles. The effects of the enhanced soil thickness, strength, and pile-enhanced soil interface characteristics on the ultimate lateral resistance were studied. The results show that increasing the enhanced soil thickness leads to a significant improvement on ultimate lateral resistance factor (N_P), and there is a critical thickness beyond which the thickness no longer affects the N_P. Increasing the enhanced soil strength induced 6.2%–232.6% increase of N_P. However, no noticeable impact was detected when the enhanced soil strength was eight times higher than that of the natural soil. The maximum increment of N_P is only 30.5% caused by the increase of interface adhesion factor (α). An empirical model was developed to calculate the N_P of the composite pile, and the results show excellent agreement with the analytical results.

Vertical drains are used to accelerate consolidation of clays in ground improvement projects. Smear zones exist around these drains, where permeability is reduced due to soil disturbance caused by the installation process. Hansbo solution is widely used in practice to consider the effects of drain discharge capacity and smear on the consolidation process. In this study, a computationally efficient diameter reduction method (DRM) obtained from the Hansbo solution is proposed to consider the smear effect without the need to model the smear zone physically. Validated by analytical and numerical results, a diameter reduction factor is analytically derived to reduce the diameter of the drain, while achieving similar solutions of pore pressure dissipation profile as the classical full model of the smear zone and drain. With the DRM, the excess pore pressure u obtained from the reduced drain in the original undisturbed soil zone is accurate enough for practical applications in numerical models. Such performance of DRM is independent of soil material property. Results also show equally accurate performance of DRM under conditions of multi-layered soils and coupled radial-vertical groundwater flow.

Injecting carbon dioxide (CO₂) into coal seams may unlock substantial carbon sequestration potential. Since the coal acts like a carbon filter, it can preferentially absorb significant amounts of CO₂. To explore this further, desorption of the adsorbed gas due to pressure drop is investigated in this paper, to achieve an improved understanding of the long-term fate of injected CO₂ during post-injection period. This paper presents a dual porosity model coupling gas flow, adsorption and geomechanics for studying coupled processes and effectiveness of CO₂ sequestration in coals. A new adsorption−desorption model derived based on thermodynamics is incorporated, particularly, the desorption hysteresis is considered. The reliability of the proposed adsorption-desorption isotherm is examined via validation tests. It is indicated that occurrence of desorption hysteresis is attributed to the adsorption-induced pore deformation. After injection ceases, the injected gas continues to propagate further from the injection well, while the pressure in the vicinity of the injection well experiences a significant drop. Although the adsorbed gas near the well also decreases, this decrease is less compared to that in pressure because of desorption hysteresis. The unceasing spread of CO₂ and drops of pressure and adsorbed gas depend on the degree of desorption hysteresis and heterogeneity of coals, which should be considered when designing CO₂ sequestration into coal seams.

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes, which is vital for India's socio-economic growth. Due to natural and artificial factors, frequency of slope instabilities along the networks has been increasing over last few decades. Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year. Several rock mass classification methods are generally used to assess the strength and deformability of rock mass. This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas. Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes. For evaluating the stability of these slopes, kinematic analysis was performed along with geological strength index (GSI), rock mass rating (RMR), continuous slope mass rating (CoSMR), slope mass rating (SMR), and Q-slope in the present study. The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable. The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating (SSR) for the factor of safety (FoS) of 1.2 and 1 respectively. Q-slope with probability of failure (PoF) 1% gives two slopes as stable slopes. Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS. The value ranges given by different empirical classifications were RMR (37–74), GSI (27.3–58.5), SMR (11–59), and CoSMR (3.39–74.56). Good relationship was found among RMR & SSR and RMR & GSI with correlation coefficient (R²) value of 0.815 and 0.6866, respectively. Lastly, a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

Monitoring shear deformation of sliding zones is of great significance for understanding the landslide evolution mechanism, in which fiber optic strain sensing has shown great potential. However, the correlation between strain measurements of quasi-distributed fiber Bragg grating (FBG) sensing arrays and shear displacements of surrounding soil remains elusive. In this study, a direct shear model test was conducted to simulate the shear deformation of sliding zones, in which the soil internal deformation was captured using FBG strain sensors and the soil surface deformation was measured by particle image velocimetry (PIV). The test results show that there were two main slip surfaces and two secondary ones, developing a spindle-shaped shear band in the soil. The formation of the shear band was successfully captured by FBG sensors. A sinusoidal model was proposed to describe the fiber optic cable deformation behavior. On this basis, the shear displacements and shear band widths were calculated by using strain measurements. This work provides important insight into the deduction of soil shear deformation using soil-embedded FBG strain sensors.

Enzyme-induced carbonate precipitation (EICP) is an emanating, eco-friendly and potentially sound technique that has presented promise in various geotechnical applications. However, the durability and microscopic characteristics of EICP-treated specimens against the impact of drying-wetting (D-W) cycles is under-explored yet. This study investigates the evolution of mechanical behavior and pore characteristics of EICP-treated sea sand subjected to D-W cycles. The uniaxial compressive strength (UCS) tests, synchrotron radiation micro-computed tomography (micro-CT), and three-dimensional (3D) reconstruction of CT images were performed to study the multiscale evolution characteristics of EICP-reinforced sea sand under the effect of D-W cycles. The potential correlations between microstructure characteristics and macro-mechanical property deterioration were investigated using gray relational analysis (GRA). Results showed that the UCS of EICP-treated specimens decreases by 63.7% after 15 D-W cycles. The proportion of mesopores gradually decreases whereas the proportion of macropores increases due to the exfoliated calcium carbonate with increasing number of D-W cycles. The microstructure in EICP-reinforced sea sand was gradually disintegrated, resulting in increasing pore size and development of pore shape from ellipsoidal to columnar and branched. The gray relational degree suggested that the weight loss rate and UCS deterioration were attributed to the development of branched pores with a size of 100–1000 μm under the action of D-W cycles. Overall, the results in this study provide a useful guidancee for the long-term stability and evolution characteristics of EICP-reinforced sea sand under D-W weathering conditions.