2025, 17(5): 2619-2632. doi:10.1016/j.jrmge.2024.09.009
The commonly used method for estimating crack opening displacement (COD) is based on analytical models derived from strain transferring. However, when large background noise exists in distributed fiber optic sensing (DFOS) data, estimating COD through an analytical model is very difficult even if the DFOS data have been denoised. To address this challenge, this study proposes a machine learning (ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models. The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models. To ensure that the best hyper-parameters will not be missing, the configuration space in Hyperopt is specified by probability distribution. The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness. The proposed models are compared each other in terms of goodness of fit and mean squared error. The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.
[...]Read more.2025, 17(5): 2633-2649. doi:10.1016/j.jrmge.2024.11.041
Predicting blasting quality during tunnel construction holds practical significance. In this study, a new semi-supervised learning method using convolutional variational autoencoder (CVAE) and deep neural network (DNN) is proposed for the prediction of blasting quality grades. Tunnel blasting quality can be measured by over/under excavation. The occurrence of over/under excavation is influenced by three factors: geological conditions, blasting parameters, and tunnel geometric dimensions. The proposed method reflects the geological conditions through measurements while drilling and utilizes blasting parameters, tunnel geometric dimensions, and tunnel depth as input variables to achieve tunnel blasting quality grades prediction. Furthermore, the model is optimized by considering the influence of surrounding rock mass features on the predicted positions. The results demonstrate that the proposed method outperforms other commonly used machine learning and deep learning algorithms in extracting over/under excavation feature information and achieving blasting quality prediction.
[...]Read more.2025, 17(5): 2650-2664. doi:10.1016/j.jrmge.2025.03.005
Lost circulation of drilling fluid is one of the most common and costly problems in drilling operations. This highlights the importance of wellbore strengthening treatment sthat can utilize lost circulation materials (LCMs) to seal fractures associated with the wellbore. In this work, a numerical model accounting for the deformation of surrounding rock, fluid flow in the fracture, fracture propagation, and the transport of LCMs is presented to investigate the wellbore strengthening, from the fracture initiation to the fracture arrest, due to plugs formed by LCMs. The equations governing the rock deformation and fluid flow are solved by the dual boundary element method and the finite volume method, respectively. The transport of LCMs is solved based on an empirical constitutive model in suspension flow, and several characteristic quantities are derived by dimensional analysis. It is found that two dimensionless parameters, dimensionless toughness and normalized initial particle concentration, control the migration of LCM particles. The numerical results show that the dimensionless toughness influences the entrance and bridging of LCMs while the initial concentration controls the location of the particle bridging. When the initial concentration is larger than 0.8, the particle bridging tends to occur near the fracture entry. Conversely, when the initial concentration is less than 0.8, the particle bridging occurs near the fracture tip. This work provides an effective tool to predict the LCM transport and plugging in the wellbore strengthening process.
[...]Read more.2025, 17(5): 2665-2681. doi:10.1016/j.jrmge.2024.05.054
Accurate prediction of ground surface settlement (GSS) adjacent to an excavation is important to prevent potential damage to the surrounding environment. Previous studies have extensively delved into this topic but all under the limitations of either imprecise theories or insufficient data. In the present study, we proposed a physics-constrained neural network (PhyNN) for predicting excavation-induced GSS to fully integrate the theory of elasticity with observations and make full use of the strong fitting ability of neural networks (NNs). This model incorporates an analytical solution as an additional regularization term in the loss function to guide the training of NN. Moreover, we introduced three trainable parameters into the analytical solution so that it can be adaptively modified during the training process. The performance of the proposed PhyNN model is verified using data from a case study project. Results show that our PhyNN model achieves higher prediction accuracy, better generalization ability, and robustness than the purely data-driven NN model when confronted with data containing noise and outliers. Remarkably, by incorporating physical constraints, the admissible solution space of PhyNN is significantly narrowed, leading to a substantial reduction in the need for the amount of training data. The proposed PhyNN can be utilized as a general framework for integrating physical constraints into data-driven machine-learning models.
[...]Read more.2025, 17(5): 2682-2694. doi:10.1016/j.jrmge.2024.05.028
Numerical modelling is an effective technique to improve the understanding of outburst initiation mechanisms and to take appropriate measures to address their threats. Based on the existing two-way sequential coupling method, two typical types of outbursts, i.e. the gas pocket outburst and the dynamic fracturing outburst, have been successfully simulated using field data from a coalfield in central China. The geological structure commonly observed in the coalfield, known as the ‘bedding shear zone’, contributes to the gas pocket outbursts in the region. The model for this type of outburst simulates mining-induced stress and gas pressure distributions during the outburst initiation stage and the subsequent development stage. Both coal ejection and gas release are observed in the model, and the simulation results are consistent with mine site observations, i.e. the amount of ejected coal, outburst cavity profile, and gas release rate change prior to an outburst. The second type of outburst is attributed to gas accumulation and elevated gas pressure due to the gassy floor seam and the heterogeneity in the floor strata, which is explained by the dynamic fracturing theory. While the dynamic coal ejection phenomenon is not captured in the simulation, the abrupt release of retained gas from a floor coal seam is successfully replicated. Both outburst models reveal that abnormal gas emission trends can be used as indicators of an upcoming outburst. The results of this study are expected to provide new insights into the outburst initiation mechanisms and outburst prevention measures.
[...]Read more.2025, 17(5): 2695-2712. doi:10.1016/j.jrmge.2024.07.015
A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading. The results show that the loading parameters (initial normal stress, normal stiffness, and shear velocity) determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle, creating different morphologies of macroscopic step-path rupture surfaces and asperities on them. The differences in stress state and rupture surface induce different cyclic shear responses. It shows that high initial normal stress accelerates asperity degradation, raises shear resistance, and promotes compression of intermittent joints. In addition, high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints. High shear velocity results in a higher shear resistance, greater dilation, and greater compression. Finally, shear strength is most sensitive to initial normal stress, followed by shear velocity and normal stiffness. Moreover, average dilation angle is most sensitive to initial normal stress, followed by normal stiffness and shear velocity. During the shear cycles, frictional coefficient is affected by asperity degradation, backfilling of rock debris, and frictional area, exhibiting a non-monotonic behavior.
[...]Read more.2025, 17(5): 2713-2726. doi:10.1016/j.jrmge.2024.09.037
The use of abrasive waterjets (AWJs) for rock drilling offers advantages in urbanized areas, locations that are vulnerable to damage, and piling operations. However, the overall operational cost of AWJ systems remains high compared to that of conventional drilling methods, which constrains the long-term industrial application of AWJs. For instance, the abrasive costs account for over 60% of the total process cost, but the recycling of abrasives remaining after drilling could significantly reduce machining costs. In this study, the post-impact characteristics of abrasives were explored, aiming to enhance their recyclability. The physical properties and particle distribution of used abrasives vary depending on the jet energy, ultimately affecting their recyclability and recycling rate. The particle properties of used abrasives (particle size distribution, particle shape, and mean particle size) were compared under different waterjet energy variables (standoff distance (SOD) and water pressure) and test conditions (dry and underwater). Furthermore, the collision stages of the abrasive particles within a waterjet system were classified and analyzed. The results revealed that abrasive fragmentation predominantly occurred due to internal collisions within the mixing chamber. In addition, an attempt was made to optimize the waterjet parameters for an economical and efficient operation. The findings of this study could contribute to enhancing the cost-effectiveness of AWJ systems for rock drilling applications.
[...]Read more.2025, 17(5): 2727-2740. doi:10.1016/j.jrmge.2024.09.049
Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties, ensures safe and efficient drilling operations, and optimizes reservoir characterization and production. The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs. This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset, particularly in complex formations. The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships. It integrates the best Gradient Boosting Regressor (GBR) algorithm to model pore pressure at wells and later utilizes Continuous Wavelet Transformation (CWT) of the seismic dataset for spatial analysis, and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume. In the second stage, for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area, a three-dimensional pore pressure prediction is conducted using CWT. The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure. Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction, the GBR has provided exceptional results that have been validated by evaluation metrics based on the R2 score i.e., 0.91 between the calibrated and predicted pore pressure. Via the deep neural network, the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.
[...]Read more.2025, 17(5): 2741-2757. doi:10.1016/j.jrmge.2024.09.047
With the development of urban infrastructure, it is inevitable that shield tunnels will undercross intercity railways. However, the safe operation of intercity railways requires strict subgrade deformation. On the basis of the engineering background of the Lianghu Tunnel in Wuhan, the three-dimensional centrifuge test and numerical back analysis were used to study the development of subgrade surface settlement during shield tunneling. A three-dimensional numerical model with the same size as the prototype was subsequently established to further study the settlement development and torsion behavior of the subgrade during tunnel excavation. The results show that the maximum settlement point of the transverse settlement trough gradually moves to the tunnel axis during tunnel excavation and that the entire subgrade experiences torsional deformation. Moreover, the effect of the intersection angle between the axes of the tunnel and the subgrade on the surface settlement of the subgrade was further studied. The results show that the intersection angle has no effect on the maximum settlement, but the width of the settlement trough increases gradually with increasing angle. Finally, on the basis of the soil arching effect caused by tunnel excavation, the subgrade settlement during tunnel excavation is reduced by reinforcing the soil in different zones of soil arching. The results show that the settlement of the subgrade caused by the shield tunnel can be effectively controlled by adding reinforcement directly to the top of the tunnel, and the maximum settlement of the subgrade surface is reduced from 24.41 mm to 9.47 mm, a reduction of approximately 61.2%.
[...]Read more.2025, 17(5): 2758-2777. doi:10.1016/j.jrmge.2024.05.042
A holistic and precise assessment of retaining wall deformations is critical for on-site risk management of large combined deep excavation projects, where the risk-related points are highly dispersed, evolving, and interacting. Despite extensive exploration of this topic in previous studies, the omission of intricate spatiotemporal characteristics of wall deformations has resulted in diminished prediction accuracy and stability. To mitigate this deficiency, a spatiotemporal characteristics matrix for all data points and time series was first generated for a deep excavation scenario and used as input for a new hybrid model that combines convolutional neural network (CNN) and long short-term memory (LSTM) with incorporated attention mechanism (CNN-LSTM-Att), which enables the cross-learning mechanism and improves interpretability. In addition, by leveraging the attention weight, a new risk assessment index for retaining wall deformations across various scenarios was formulated. Then the proposed method was applied in a large combined deep excavation in Shanghai, China. The results show that: (1) The incorporation of fed-in characteristic data and the attention mechanism enables the proposed method to produce satisfactory prediction results for the holistic spatiotemporal distribution of a large combined excavation; (2) Compared with other published models, the proposed model shows much better prediction accuracy, interpretability, and stability, especially in medium- and long-term predictions; and (3) The new risk assessment index serves as a reliable decision-making tool for assessing the risk evolution of retaining wall deformations and provides valuable guidance for effective risk management in multi-scenario excavation projects.
[...]Read more.2025, 17(5): 2778-2792. doi:10.1016/j.jrmge.2024.05.018
Ensuring the stability of the surrounding rock mass is of great importance during the construction of a large underground powerhouse. The presence of unfavorable structural planes within the rock mass, such as faults, can lead to substantial deformation and subsequent collapse. A series of in situ experiments and discrete element numerical simulations have been conducted to gain insight into the progressive failure behavior and deformation response of rocks in relation to controlled collapse scenarios involving gently inclined faults. First, the unloading damage evolution process of the surrounding rock mass is characterized by microscopic analysis using microseismic (MS) data. Second, the moment tensor inversion method is used to elucidate the temporal distribution of MS event fracture types in the surrounding rock mass. During the development stage of the collapse, numerous tensile fracture events occur, while a few shear fractures corresponding to structural plane dislocation precede their occurrence. The use of the digital panoramic borehole camera, acoustic wave test, and numerical simulation revealed that gently inclined faults and deep cracks at a certain depth from the cavern periphery are the primary factors contributing to rock collapse. These results provide a valuable case study that can help anticipate and mitigate fault-slip collapse incidents while providing practical insights for underground cave excavation.
[...]Read more.2025, 17(5): 2793-2809. doi:10.1016/j.jrmge.2024.05.021
Particle morphology is critical in affecting the crushing behavior of rockfill materials. In contrast, most current single particle simulations lack satisfactory morphology accuracy, and the resulting crushing modes deviate from observations to some extent. Therefore, we reconstruct the real particle morphology with the spherical harmonic (SH) method and employ the finite-discrete element method (FDEM) to simulate the one-dimensional (1D) compressive crushing process of basalt particles commonly used in rockfill. The influences of four main morphological parameters, i.e. sphericity, aspect ratio, roundness, and convexity, on the single particle strength and the crushing modes are discussed. The results show that with the SH degree set to 15 and a mesh number of 20,480, the FDEM models of reconstructed particles achieve sufficient morphology accuracy and high computational efficiency. Based on the model, the simulation results demonstrate that the aspect ratio has the most significant impact on single particle strength, followed by sphericity. In contrast, roundness and convexity have a weaker effect than the above two parameters. Also, it is revealed that single particle strength decreases with increasing aspect ratio and sphericity, while it increases with higher roundness and convexity. Furthermore, aspect ratio significantly changes the initial crushing position, sphericity dominates post-crushing fragment size and quantity, and roundness mainly affects post-crushing morphology. The model results have been employed in establishing a support vector regression (SVR)-based predicted model, exhibiting good predictive performance and advantages for the optimization of rockfill particles in engineering.
[...]Read more.2025, 17(5): 2810-2828. doi:10.1016/j.jrmge.2024.05.049
Understanding the shear mechanical behaviors and instability mechanisms of rock joints under dynamic loading remains a complex challenge. This research conducts a series of direct shear tests on real rock joints subjected to cyclic normal loads to assess the influence of dynamic normal loading amplitude (Fd), dynamic normal loading frequency (fv), initial normal loading (Fs), and the joint roughness coefficient (JRC) on the mechanical properties and instability responses of these joints. The results show that unstable sliding is often accompanied by friction weakening due to dynamic normal loads. A significant negative correlation exists between cyclic normal loads and the normal displacement during the shearing process. Dynamic normal load paths vary the contact states of asperities on the rough joint surfaces, impacting the stick-slip instability mechanism of the joints, which in turn affects both the magnitude and location of the stress drop during the stick-slip events, particularly during the unloading phases. An increasing Fd results in a more stable shearing behavior and a reduction in the amplitude of stick-slip stress drops. The variation in fv influences the amplitude of stress drop for the joints during shear, characterized by an initial decrease (fv = 0.25−2 Hz) before exhibiting an increment (fv = 2−4 Hz). As Fs increases, sudden failures of the interlocked rough surfaces are more prone to occur, thus producing enhanced instability and a more substantial stress drop. Additionally, a larger JRC intensifies the instability of the joints, which would induce a more pronounced decline in the stick-slip stress. The Rate and state friction (RSF) law can provide an effective explanation for the unstable sliding phenomena of joints during the oscillations of normal loads. The findings may provide certain useful references for a deeper comprehension of the sliding behaviors exhibited by rock joints when subjected to cyclic dynamic disturbances.
[...]Read more.2025, 17(5): 2829-2842. doi:10.1016/j.jrmge.2024.05.008
In subsurface projects where the host rock is of low permeability, fractures play an important role in fluid circulation. Both the geometrical and mechanical properties of the fracture are relevant to the permeability of the fracture. To evaluate this relationship, we numerically generated self-affine fractures reproducing the scaling relationship of the power spectral density (PSD) of the measured fracture surfaces. The fractures were then subjected to a uniform and stepwise increase in normal stress. A fast Fourier transform (FFT)-based elastic contact model was used to simulate the fracture closure. The evolution of fracture contact area, fracture closure, and fracture normal stiffness were determined throughout the whole process. In addition, the fracture permeability at each step was calculated by the local cubic law (LCL). The influences of roughness exponent and correlation length on the fracture hydraulic and mechanical behaviors were investigated. Based on the power law of normal stiffness versus normal stress, the corrected cubic law and the linear relationship between fracture closure and mechanical aperture were obtained from numerical modeling of a set of fractures. Then, we derived a fracture normal stiffness-permeability equation which incorporates fracture geometric parameters such as the root-mean-square (RMS), roughness exponent, and correlation length, which can describe the fracture flow under an effective medium regime and a percolation regime. Finally, we interpreted the flow transition behavior from the effective medium regime to the percolation regime during fracture closure with the established stiffness-permeability function.
[...]Read more.2025, 17(5): 2843-2856. doi:10.1016/j.jrmge.2024.05.045
The scale effect on shear strength of rock joints is well-documented. However, whether scale effects are negative, positive, or even exist or not is still controversial. Joint roughness significantly influences the shear strength of rock joints. Compared to the shear tests, using the joint roughness coefficient (JRC) and its roughness parameters offers a more convenient method for describing the scale effect on shear strength. However, it is crucial to understand that the scale effect mechanisms of JRC are distinct from those of shear strength. Therefore, this paper aims to clarify these distinct mechanisms. By digitally extracting roughness parameters from granite samples, it is found that the scale effect of roughness parameters mainly comes from the sampling methods and the geometric characteristics of parameters. Furthermore, a full data sampling method considering heterogeneity is proposed to obtain more representative roughness parameters. To reveal the scale effect mechanisms of shear strength, Gaussian filtering is firstly used to separate the waviness and unevenness components of roughness, facilitating a deeper understanding of the geometric characteristics of roughness. It is suggested that the wavelength of the waviness component can reflect the scale effect on shear strength. Secondly, numerical simulations of ideal artificial joint models are conducted to validate that the wavelength of the waviness component serves as the dividing point between positive and negative scale effects. The mechanical mechanisms of positive and negative scale effects are also interpreted. Finally, these mechanisms successfully elucidate the occurrence patterns of the scale effect on natural joint profiles.
[...]Read more.2025, 17(5): 2857-2878. doi:10.1016/j.jrmge.2024.05.033
Understanding the temperature-dependent mechanical behavior and fracture characteristics of granite is crucial for many engineering projects. In this study, the real-time temperature curves of granite specimens were obtained during the heating and cooling process, and the thermal treatment tests were conducted. The physical properties of the specimen before and after thermal treatment, including mass, volume, and P-wave velocity, were measured. The acoustic emission (AE) signal in the uniaxial compression is monitored. The results indicate that the physical properties of granite deteriorate with temperature, while the mechanical properties show two effects of thermal strengthening and thermal weakening. This phenomenon is comprehensively analyzed by literature statistical data and optical microscopic observation. Furthermore, the AE characteristic is strongly dependent on temperature. High temperature induces more AE ring count to appear in the early stage of loading. As the temperature increases, the crack initiation stress decreases and the table crack propagation stage becomes longer. The attenuation of high-frequency signals and the enhancement of low-frequency signals are related to the development and interaction mechanism of thermally-induced crack and stress-induced crack. At 600 °C, the global b-value increases significantly. Meanwhile, the evolution of dynamic b-value helps explain the failure process of granite under axial load after thermal treatment. In addition, a new thermo-mechanical damage statistical constitutive model of granite considering temperature effects is proposed by introducing AE parameters. The main advantages of this model can well fit the nonlinear behavior of granite in the early loading stage after thermal treatment, and reflect the failure process of granite before the peak value.
[...]Read more.2025, 17(5): 2879-2892. doi:10.1016/j.jrmge.2024.05.062
Prepulse combined hydraulic fracturing facilitates the development of fracture networks by integrating prepulse hydraulic loading with conventional hydraulic fracturing. The formation mechanisms of fracture networks between hydraulic and pre-existing fractures under different prepulse loading parameters remain unclear. This research investigates the impact of prepulse loading parameters, including the prepulse loading number ratio (C), prepulse loading stress ratio (S), and prepulse loading frequency (f), on the formation of fracture networks between hydraulic and pre-existing fractures, using both experimental and numerical methods. The results suggest that low prepulse loading stress ratios and high prepulse loading number ratios are advantageous loading modes. Multiple hydraulic fractures are generated in the specimen under the advantageous loading modes, facilitating the development of a complex fracture network. Fatigue damage occurs in the specimen at the prepulse loading stage. The high water pressure at the secondary conventional hydraulic fracturing promotes the growth of hydraulic fractures along the damage zones. This allows the hydraulic fractures to propagate deeply and interact with pre-existing fractures. Under advantageous loading conditions, multiple hydraulic fractures can extend to pre-existing fractures, and these hydraulic fractures penetrate or propagate along pre-existing fractures. Especially when the approach angle is large, the damage range in the specimen during the pre-pulse loading stage increases, resulting in the formation of more hydraulic fractures.
[...]Read more.2025, 17(5): 2893-2903. doi:10.1016/j.jrmge.2024.05.041
Hydraulic fracturing then fluid circulation in enhanced geothermal system (EGS) reservoirs have been shown to induce seismicity remote from the stimulation – potentially generated by the distal projection of thermoporoelastic stresses. We explore this phenomenon by evaluating stress perturbations resulting from stimulation of a single stage of hydraulic fracturing that is followed by thermal depletion of a prismatic zone adjacent to the hydraulic fracture. We use Coulomb failure stress to assess the effect of resulting stress perturbations on instability on adjacent critically-stressed faults. Results show that hydraulic fracturing in a single stage is capable of creating stress perturbations at distances to 1000 m that reach 10−5-10−4 MPa. At a closer distance, the magnitude of stress perturbations increases even further. The stress perturbation induced by temperature depletion could also reach 10−3-10−2 MPa within 1000 m - much higher than that by hydraulic fracturing. Considering that a critical change in Coulomb failure stress for fault instability is 10−2 MPa, a single stage of hydraulic fracturing and thermal drawdown are capable of reactivating critically-stressed faults at distances within 200 m and 1000 m, respectively. These results have important implications for understanding the distribution and magnitudes of stress perturbations driven by thermoporoelastic effects and the associated seismicity during the simulation and early production of EGS reservoirs.
[...]Read more.2025, 17(5): 2904-2927. doi:10.1016/j.jrmge.2024.06.007
The cyclic injection and production of fluids into and from underground gas storage (UGS) may lead to caprock failure, such as capillary sealing failure, hydraulic fracturing, shear failure, and fault slipping or dilation. The dynamic sealing capacity of a caprock-fault system is a critical constraint for safe operation, and is a key factor in determining the maximum operating pressure (MOP). This study proposed an efficient semi-analytical method for calculating changes in the in situ stress within the caprock. Next, the parameters of dynamic pore pressure, in situ stresses, and deformations obtained from reservoir simulations and geomechanical modeling were used for inputs for the analytical solution. Based on the calculated results, an experimental scheme for the coupled cyclic stress-permeability testing of caprock was designed. The stability analysis indicated that the caprock was not prone to fatigue shear failure under the current injection and production strategy, supported by the experimental results. The experimental results further reveal that the sealing capacity of caprock plugs may remain stable. This phenomenon is attributed to cyclic stress causing pore connectivity and microcrack initiation in certain plugs, while leading to pore compaction in others. A comparison between the dynamic pore pressure and the minimum principal stress suggests that the risk of tensile failure is extremely low. Furthermore, although the faults remain stable under the current injection and production strategies, the continuous increase in injection pressure may lead to an increased tendency for fault slip and dilation, which can cause fault slip ultimately. The MOPs corresponding to each failure mode were calculated. The minimum value of approximately 36.5 MPa at capillary sealing failure indicated that the gas breakthrough in the caprock occurred earlier than rock failure. Therefore, this minimum value can be used as the MOP for the target UGS.
[...]Read more.2025, 17(5): 2928-2942. doi:10.1016/j.jrmge.2024.09.013
Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production. Conventional methods relying on empirical correlations and assumptions often result in high costs, time consumption, inaccuracies, and uncertainties. This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield, Northwestern Uganda. The group method of data handling with differential evolution (GMDH-DE) algorithm was used to predict permeability due to its capability to manage complex, nonlinear relationships between variables, reduced computation time, and parameter optimization through evolutionary algorithms. Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing, the GMDH-DE outperformed the group method of data handling (GMDH) and random forest (RF) in predicting permeability with higher accuracy and lower computation time. The GMDH-DE achieved an R2 of 0.9985, RMSE of 3.157, MAE of 2.366, and ME of 0.001 during training, and for testing, the ME, MAE, RMSE, and R2 were 1.3508, 12.503, 21.3898, and 0.9534, respectively. Additionally, the GMDH-DE demonstrated a 41% reduction in processing time compared to GMDH and RF. The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin, Tanzania, which lacks core data. Shapley additive explanations (SHAP) analysis identified thermal neutron porosity (TNPH), effective porosity (PHIE), and spectral gamma-ray (SGR) as the most critical parameters in permeability prediction. Therefore, the GMDH-DE model offers a novel, efficient, and accurate approach for fast permeability prediction, enhancing hydrocarbon exploration and production.
[...]Read more.2025, 17(5): 2943-2963. doi:10.1016/j.jrmge.2024.09.001
In tunnel construction, tunnel boring machine (TBM) tunnelling typically relies on manual experience with sub-optimal control parameters, which can easily lead to inefficiency and high costs. This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multi-objective optimization (MOO). First, the effective TBM operation dataset is obtained through data preprocessing of the Songhua River (YS) tunnel project in China. Next, the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters (i.e. total thrust and cutterhead torque), rock mass classification, and hazard risks (i.e. tunnel collapse and shield jamming). Then, considering three optimal objectives, (i.e., penetration rate, rock-breaking energy consumption, and cutterhead hob wear), the MOO framework and corresponding mathematical expression are established. The Pareto optimal front is solved using DE-NSGA-II algorithm. Finally, the optimal control parameters (i.e., advance rate and cutterhead rotation speed) are obtained by the satisfactory solution determination criterion, which can balance construction safety and efficiency with satisfaction. Furthermore, the proposed method is validated through 50 cases of TBM tunnelling, showing promising potential of application.
[...]Read more.2025, 17(5): 2964-2986. doi:10.1016/j.jrmge.2024.05.040
The loaded rock experiences multiple stages of deformation. It starts with the formation of microcracks at low stresses (crack initiation, CI) and then transitions into unstable crack propagation (crack damage, CD) near the ultimate strength. In this study, both the acoustic emission method (AEM) and the ultrasonic testing method (UTM) were used to examine the characteristics of AE parameters (b-value, peak frequency, frequency-band energy ratio, and fractal dimension) and ultrasonic (ULT) properties (velocity, amplitude, energy attenuation, and scattering attenuation) of bedded shale at CI, CD, and ultimate strength. The comparison involved analyzing the strain-based method (SBM), AEM, and UTM to determine the thresholds for damage stress. A fuzzy comprehensive evaluation model (FCEM) was created to describe the damage thresholds and hazard assessment. The results indicate that the optimal AE and ULT parameters for identifying CI and CD stress are ringing count, ultrasonic amplitude, energy attenuation, and scattering attenuation of the S-wave. Besides, damage thresholds were detected earlier by AE monitoring, ranging from 3 MPa to 10 MPa. CI and CD identified by UTM occurred later than SBM and AEM, and were in the range of 12 MPa. The b-value, peak frequency, energy ratio in the low-frequency band (0–62.5 kHz), correlation dimension, and sandbox dimension showed low values at the peak stress, while the energy ratio in a moderate-frequency band (187.5–281.25 kHz) and amplitude showed high values. The successful application of FCEM to laboratory testing of shales has demonstrated its ability to quantitatively identify AE/ULT precursors of seismic hazards associated with rock failure.
[...]Read more.2025, 17(5): 2987-3000. doi:10.1016/j.jrmge.2024.07.001
In this study, we employed Bayesian inversion coupled with the summation-by-parts and simultaneous-approximation-term (SBP-SAT) forward simulation method to elucidate the mechanisms behind mining-induced seismic events caused by fault slip and their potential effects on rockbursts. Through Bayesian inversion, it is determined that the sources near fault FQ14 have a significant shear component. Additionally, we analyzed the stress and displacement fields of high-energy events, along with the hypocenter distribution of aftershocks, which aided in identifying the slip direction of the critically stressed fault FQ14. We also performed forward modeling to capture the complex dynamics of fault slip under varying friction laws and shear fracture modes. The selection of specific friction laws for fault slip models was based on their ability to accurately replicate observed slip behavior under various external loading conditions, thereby enhancing the applicability of our findings. Our results suggest that the slip behavior of fault FQ14 can be effectively understood by comparing different scenarios.
[...]Read more.2025, 17(5): 3001-3017. doi:10.1016/j.jrmge.2024.09.039
2025, 17(5): 3018-3034. doi:10.1016/j.jrmge.2024.10.032
Polyurethane foam, when used as a compressible layer in deep soft rock tunnels, offers a feasible solution to reduce the support pressure on the secondary lining. The foam spraying method using sprayed polyurethane material is convenient for engineering applications; however, the compressive behaviour and feasibility of sprayed polyurethane material as a compressible layer remain unclear. To address this gap, this study conducts uniaxial compression tests and scanning electron microscope (SEM) tests to investigate the compressive behaviour of the rigid foams fabricated from a self-developed polyurethane spray material. A peridynamics model for the composite lining with a polyurethane compressible layer is then established. After validating the proposed method by comparison with two tests, a parametric study is carried out to investigate the damage evolution of the composite lining with a polyurethane compressible layer under various combinations of large deformations and compressible layer parameters. The results indicate that the polyurethane compressible layer effectively reduces the radial deformation and damage index of the secondary lining while increasing the damage susceptibility of the primary lining. The thickness of the polyurethane compressible layer significantly influences the prevention effect of large deformation-induced damage to the secondary lining within the density range of 50–100 kg/m3. In accordance with the experimental and simulation results, a simple, yet reasonable and convenient approach for determining the key parameters of the polyurethane compressible layer is proposed, along with a classification scheme for the parameters of the polyurethane compressible layer.
[...]Read more.2025, 17(5): 3035-3053. doi:10.1016/j.jrmge.2024.08.021
The torsional low strain integrity test (TLSIT), known for its advantages such as a smaller detection blind zone, improved identification of shallowly buried defects, stable phase velocity for signal interpretation, and better adaptability for existing pile testing. However, it lacks a comprehensive understanding of the authentic three-dimensional (3D) strain wave propagation mechanism, particularly wave reflection and transmission at defects. To address this gap, a novel 3D theoretical framework is introduced in this context to model the authentic 3D wave propagation during the TLSIT. The proposed approach is validated by comparing its results with those obtained from 3D finite element method (FEM) simulations and simplified 1D (one-dimensional) and 3D analytical solutions. Additionally, a parametric study is conducted to enhance insights into the formation mechanism of high-frequency interference observed during the TLSIT. Finally, a defect identification study is performed to provide guidance for interpreting the wave spectrum in terms of defect characteristics.
[...]Read more.2025, 17(5): 3054-3072. doi:10.1016/j.jrmge.2024.10.022
The joint roughness coefficient (JRC) is a key parameter in the assessment of mechanical properties and the stability of rock masses. This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation (GA-BP) neural network. Conventional JRC evaluations have typically depended on two-dimensional (2D) and three-dimensional (3D) parameter calculation methods, which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness. Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles, heights, and back slope morphological features. Subsequently, five simple statistical parameters, i.e. average dip angle, median dip angle, average height, height coefficient of variation, and back slope feature value (K), were utilized to quantify these characteristics. For the prediction of JRC, we compiled and analyzed 105 datasets, each containing these five statistical parameters and their corresponding JRC values. A GA-BP neural network model was then constructed using this dataset, with the five morphological characteristic statistics serving as inputs and the JRC values as outputs. A comparative analysis was performed between the GA-BP neural network model, the statistical parameter method, and the fractal parameter method. This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.
[...]Read more.2025, 17(5): 3073-3092. doi:10.1016/j.jrmge.2024.11.010
Deep geological sequestration is widely recognized as a reliable method for nuclear waste management, with expanded applications in thermal energy storage and adiabatic compressed air energy storage systems. This study evaluated the suitability of granite, basalt, and marble as reservoir rocks capable of withstanding extreme high-temperature and high-pressure conditions. Using a custom-designed triaxial testing apparatus for thermal-hydro-mechanical (THM) coupling, we subjected rock samples to temperatures ranging from 20 °C to 800 °C, triaxial stresses up to 25 MPa, and seepage pressures of 0.6 MPa. After THM treatment, the specimens were analyzed using a Real-Time Load-Synchronized Micro-Computed Tomography (MCT) Scanner under a triaxial stress of 25 MPa, allowing for high-resolution insights into pore and fissure responses. Our findings revealed distinct thermal stability profiles and microscopic parameter changes across three phases—slow growth, slow decline, and rapid growth—with critical temperature thresholds observed at 500 °C for granite, 600 °C for basalt, and 300 °C for marble. Basalt showed minimal porosity changes, increasing gradually from 3.83% at 20 °C to 12.45% at 800 °C, indicating high structural integrity and resilience under extreme THM conditions. Granite shows significant increases in porosity due to thermally induced microcracking, while marble rapidly deteriorated beyond 300 °C due to carbonate decomposition. Consequently, basalt, with its minimal porosity variability, high thermal stability, and robust mechanical properties, emerges as an optimal candidate for nuclear waste repositories and other high-temperature geological engineering applications, offering enhanced reliability, structural stability, and long-term safety in such settings.
[...]Read more.2025, 17(5): 3093-3106. doi:10.1016/j.jrmge.2024.04.039
The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment. Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes. In contrast, unmanned aerial vehicle (UAV) photogrammetry is not limited by terrain conditions, and can efficiently collect high-precision three-dimensional (3D) point clouds of rock masses through all-round and multiangle photography for rock mass characterization. In this paper, a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling. The method is based on four steps: (1) Establish a point cloud spatial topology, and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms; (2) Extract discontinuities using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and fit the discontinuity plane by combining principal component analysis (PCA) with the natural breaks (NB) method; (3) Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud; and (4) Adopt a Poisson reconstruction method for refined rock block modeling. The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys. The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks. The calculation results are accurate and reliable, which can meet the practical requirements of engineering.
[...]Read more.2025, 17(5): 3107-3124. doi:10.1016/j.jrmge.2024.10.011
A simplified analytical approach is proposed for predicting the load-displacement behavior of single piles in unsaturated soils considering the contribution from the nonlinear shear strength and soil stiffness influenced by matric suction. This approach includes a Modified Load Transfer Model (MLTM) that can predict the nonlinear relationships between the shear stress and pile-soil relative displacement along the pile shaft, and between the pile base resistance and base settlement. The proposed model is also extended for pile groups to incorporate the interaction effects between individual piles. The analytical approach is validated through a comparative analysis with the measurements from two single pile tests and one pile group test. In addition, a finite element analysis using 3D modeling is carried out to investigate the behavior of pile groups in various unsaturated conditions. This is accomplished with a user-defined subroutine that is written and implemented in ABAQUS to simulate the nonlinear mechanical behavior of unsaturated soils. The predictions derived from the proposed analytical and numerical methods compare well with the measurements of a published experimental study. The proposed methodologies have the potential to be applied in geotechnical engineering practice for the rational design of single piles and pile groups in unsaturated soils.
[...]Read more.2025, 17(5): 3125-3145. doi:10.1016/j.jrmge.2025.03.016
This study investigated the hydraulic and mechanical behaviors of unsaturated coarse-grained railway embankment fill materials (CREFMs) using a novel unsaturated large-scale triaxial apparatus equipped with the axis translation technique (ATT). Comprehensive soil-water retention and constant-suction triaxial compression tests were conducted to evaluate the effects of initial void ratio, matric suction, and confining pressure on the properties of CREFMs. Key findings reveal a primary suction range of 0–100 kPa characterized by hysteresis, which intensifies with decreasing density. Notably, the air entry value and residual suction are influenced by void ratio, with higher void ratios leading to decreased air entry values and residual suctions, underscoring the critical role of void ratio in hydraulic behavior. Additionally, the critical state line (CSL) in the bi-logarithmic space of void ratio and mean effective stress shifts towards higher void ratios with increasing matric suction, significantly affecting dilatancy and critical states. Furthermore, the study demonstrated that the mobilized friction angle and modulus properties depend on confining pressure and matric suction. A novel modified dilatancy equation was proposed, which enhances the predictability of CREFMs' responses under variable loading, particularly at high stress ratios defined by the deviatoric stress over the mean effective stress. This research advances the understanding of CREFMs' performance, especially under fluctuating environmental conditions that alter suction levels.
[...]Read more.2025, 17(5): 3146-3160. doi:10.1016/j.jrmge.2024.09.053
Granite saprolite (GS) slope failure is a common yet catastrophic phenomenon in South China. Although the impact of subtropical climate, characterized by high temperatures and heavy rainfall, is widely recognized, the effect of the capillary imbibition and drying (CID) process, which frequently occurs during the dry season, on the hydro-mechanical properties of GS and slope stability is largely overlooked. This research examines natural GS specimens with various degrees of weathering subjected to CID cycles. The study investigates the capillary imbibition (CI) process and the evolution of the soil's hydro-mechanical properties across CID cycles. The results indicate that the CI process in GS is fundamentally different from that in clays and sands. The aggregated structure of GS comprising numerous fissures and large pores plays a critical role. In addition, the CID cycles cause the hydro-mechanical degradation of GS, including a finer particle composition, decreased shear strength, and increased permeability and disintegration potential, where damage to soil cementation and fissure development are identified as critical factors. This investigation reveals new insights into the mechanical properties of GS that are essential for the development of effective landslide management strategies in South China.
[...]Read more.2025, 17(5): 3161-3179. doi:10.1016/j.jrmge.2024.05.048
The soil packing, influenced by variations in grain size and the gradation pattern within the soil matrix, plays a crucial role in constituting the mechanical properties of sandy soils. However, previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio (CBRs) of sandy soils by precisely articulating soil packing in the modeling framework. This study presents an innovative artificial intelligence (AI)-based approach for modeling the CBRs of sandy soils, considering grain size variability meticulously. By synthesizing extensive data from multiple sources, i.e. extensive tailored testing program undertaking multiple tests and extant literature, various modeling techniques including genetic expression programming (GEP), multi-expression programming (MEP), support vector machine (SVM), and multi-linear regression (MLR) are utilized to develop models. The research explores two modeling strategies, namely simplified and composite, with the former incorporating only sieve analysis test parameters, while the latter includes compaction test parameters alongside sieve analysis data. The models' performance is assessed using statistical key performance indicators (KPIs). Results indicate that genetic AI-based algorithms, particularly GEP, outperform SVM and conventional regression techniques, effectively capturing complex relationships between input parameters and CBRs. Additionally, the study reveals insights into model performance concerning the number of input parameters, with GEP consistently outperforming other models. External validation and Taylor diagram analysis demonstrate the GEP models' superiority over existing literature models on an independent dataset from the literature. Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBRs, further emphasizing GEP's efficacy in modeling such complexities. This study contributes to enhancing CBRs modeling accuracy for sandy soils, crucial for pertinent infrastructure design and construction rapidly and cost-effectively.
[...]Read more.2025, 17(5): 3180-3197. doi:10.1016/j.jrmge.2024.05.057
Conventional empirical equations for estimating undrained shear strength (su) from piezocone penetration test (CPTu) data, without incorporating soil physical properties, often lack the accuracy and robustness required for geotechnical site investigations. This study introduces a hybrid virus colony search (VCS) algorithm that integrates the standard VCS algorithm with a mutation-based search mechanism to develop high-performance XGBoost learning models to address this limitation. A dataset of 372 seismic CPTu and corresponding soil physical properties data from 26 geotechnical projects in Jiangsu Province, China, was collected for model development. Comparative evaluations demonstrate that the proposed hybrid VCS-XGBoost model exhibits superior performance compared to standard meta-heuristic algorithm-based XGBoost models. The results highlight that the consideration of soil physical properties significantly improves the predictive accuracy of su, emphasizing the importance of considering additional soil information beyond CPTu data for accurate su estimation.
[...]Read more.2025, 17(5): 3198-3212. doi:10.1016/j.jrmge.2024.09.054
Understanding the mechanisms of gas transport and the resulting preferential pathways formation through bentonite-based barriers is essential for their performance evaluation. In this experimental study, gas migration within a heterogenous mixture of MX80 bentonite pellets and powder with a ratio of 80/20 in dry mass was investigated. A novel X-ray transparent constant volume cell has been developed to assess the effect of gas pressure, material heterogeneities, and water vapor gas saturation on breakthrough pressure and gas pathways. The new cell allows to perform high-resolution X-ray computed micro-tomography (X-ray μCT) scans to track microstructural changes during different phases of saturation and gas injection. Experimental results showed that the gas breakthrough occurred when the pressure was raised to 3 MPa. This is slightly higher than the expected swelling pressure (2.9 MPa) of the bentonite sample. Each gas injection was followed by a long resaturation phase restoring material homogeneity at μCT resolution scale (16 μm). However, the elapsed time needed for gas to breakthrough at 3 MPa diminished at each subsequent injection test. X-ray μCT results also revealed the opening of the specimen/cell wall interface during gas passage. This opening expanded as the injection pressure increased. The gas flow along the interface was associated with the development of dilatant pathways inside the sample, although they did not reach the outlet surface. It was observed that the water vapor gas saturation had no effect on the breakthrough pressure. These findings enhance the understanding of the complex mechanisms underlying microstructural evolution and gas pathway development within the highly heterogeneous mixture. The experimental outcomes highlight the effectiveness of X-ray μCT to improve quality protocols for engineering design and safety assessments of engineered barriers.
[...]Read more.2025, 17(5): 3213-3224. doi:10.1016/j.jrmge.2024.09.029.
This study investigates the innovative reuse of sewage sludge with eco-friendly alkaline solutes to improve clayey soil without conventional cementitious binders. The unconfined compressive strength (UCS) was the main criterion to assess the quality and effectiveness of the proposed solutions, as this test was performed to measure the strength of the stabilized clay by varying binders’ dosages and curing times. Moreover, the direct shear test (DST) was used to investigate the Mohr-Coulomb parameters of the treated soil. Microstructure observations of the natural and treated soil were conducted using scanning electron microscope (SEM), energy-dispersive spectroscopy (EDS), and FTIR. Furthermore, toxicity characteristic leaching procedure (TCLP) tests were performed on the treated soil to investigate the leachability of metals. According to the results, using 2.5% of sewage sludge activated by NaOH and Na2SiO3 increases the UCS values from 176 kPa to 1.46 MPa after 7 d and 56 d of curing, respectively. The results of the DST indicate that sewage sludge as a precursor increases cohesion and enhances frictional resistance, thereby improving the Mohr-Coulomb parameters of the stabilized soil. The SEM micrographs show that alkali-activated sewage sludge increases the integrity and reduces the cavity volumes in the stabilized soil. Moreover, TCLP tests revealed that the solubility of metals in the treated soil alkali-activated by sewage sludge significantly decreased. This study suggests that using sewage sludge can replace cement and lime in ground improvement, improve the circular economy, and reduce the carbon footprint of construction projects.
[...]Read more.2025, 17(5): 3225-3242. doi:10.1016/j.jrmge.2024.08.010
Microbially induced calcite precipitation (MICP) and Enzyme induced calcite precipitation (EICP) techniques were implemented to reinforce the large-scale calcareous sand in this study. Then a coupled numerical model to predict the biochemical reactions and hydraulic characteristics of MICP and EICP reactions was proposed and verified by physical experiments. Results showed that: This model could describe the variations of bacteria, calcium, calcite, permeability over time reasonably. It is necessary to consider the influence of the calculation domain scale when simulating the convection-diffusion-reaction in the multi-process of MICP and EICP reactions. The numerical and experimental values of calcite content are 0.841 g/cm3 and 0.861 g/cm3 for MICP-reinforced sand, 0.263 g/cm3 and 0.227 g/cm3 for EICP-reinforced sand after 192 h of reaction. The reaction rate krea is an important parameter to control the calcite content. Accordingly, the permeability coefficient of MICP and EICP reinforced calcareous sand decreases by 32% and 18%. Due to the influence of substance transportation and calcite precipitation, the calcite shows a trend of decreasing firstly and then increasing with the enhancing of the initial permeability coefficient in biochemical reactions. The optimal injecting ratio q11: q12 in this study is 100:300 mL/min. The process for the application of MICP and EICP coupled numerical model is also recommended, which provides reference for engineering projects in ground improvement.
[...]Read more.2025, 17(5): 3243-3252. doi:10.1016/j.jrmge.2024.05.007
The construction of coastal areas generates a substantial volume of waste marine clay (WMC), which poses environmental and safety challenges during the stockpiling process. The improved preparation of WMC as roadbed materials emerges as a crucial pathway for resource utilization. However, the engineering performance and durability of roadbed materials prepared from WMC have always been a concern for scholars and engineers. This study employs alkali-activated ground granulated blast-furnace slag (GGBFS) and municipal solid waste incineration bottom ash (MSWIBA) to solidify WMC for preparation of the roadbed materials. The results showed that the combined utilization of alkali-activated GGBFS and MSWIBA to improve WMC can meet the environmental and mechanical requirements of roadbed materials. The incorporation of 5–20% MSWIBA could improve the water stability coefficient and California bearing ratio to more than 85% and 80%, respectively. The durability of roadbed material was significantly improved by addition of MSWIBA. After 12 dry–wet cycles, the strength of the material without MSWIBA and with 5% MSWIBA was 0 and 2.87 MPa, respectively. Following analysis of engineering properties and durability, the optimal dosage of MSWIBA was determined to be 5%. The enhanced durability can be attributed to the optimization of material gradation and pore structure achieved through the incorporation of a small quantity of MSWIBA. The carbon emission and normalized global warming potentials of roadbed material treated by MSWIBA and GGBFS were much lower than that of cementitious binders such as ordinary Portland cement. These findings indicate that MSWIBA has the potential to substitute natural aggregates like sand and gravel, effectively improving the durability of roadbed materials and promoting the safe and efficient recycling of solid waste resources.
[...]Read more.2025, 17(5): 3253-3264. doi:10.1016/j.jrmge.2024.07.014
In addressing problematic soils, geotechnical engineers employ two key strategies: compatibility and improvement. This study focuses on soft and CL deltaic sediments, and seeks to enhance cementation by investigating microbially-induced calcium carbonate precipitation (MICP). Sporosarcina pasteurii bacteria, together with a cementation solution (urea and calcium-containing salt), were electrokinetically injected into deltaic clay soil from the Telar River in Iran. The initial samples, with a dry unit weight (γd) of 12.75 kN/m³, underwent injections in two modes: simultaneous injection of the bacterial and cementation solutions and individual injection in a sequential order. Unconfined compression strength tests and laboratory vane shear tests were conducted to assess changes in soil strength parameters, while a consolidation test was performed to investigate alterations in soil settlement parameters. A comparative analysis with an electroosmosis control sample revealed a remarkable increase in compressive strength and undrained shear strength for MICP bio-electrokinetic improvement. Moreover, the consolidation test demonstrated that the compression index (Cc) and recompression index (Cr) exhibited a more pronounced decline in the simultaneous injection than individual injection. This highlights the dual impact of the bio-electrokinetic method, namely the enhancement of shear strength and the mitigation of settlement in deltaic clay soil. The calcium carbonate content was measured for the samples, and the results indicated a higher degree of participation for the samples subjected to simultaneous injection. Microstructure analyses were conducted on samples, and calcite and vaterite were observed in bio-cemented samples.
[...]Read more.2025, 17(5): 3265-3279. doi:10.1016/j.jrmge.2024.11.029
Real-time assessment of subgrade compaction quality poses a significant challenge in the implementation of intelligent compaction (IC). Current compaction evaluation models are confined to specific scenarios and lack robustness. This study proposes a subgrade compaction strategy that utilizes a heterogeneous dataset to estimate compaction quality across diverse scenarios while maintaining model accuracy. Field compaction tests are conducted in four distinct scenarios, considering various construction parameters. Compaction models are developed using several machine learning algorithms. The datasets are thoroughly assessed in terms of quality, diversity and similarity. The proposed model exhibits good performance in new scenarios by incorporating an additional 5%–8% of new data for retraining. The model's generalization capability is enhanced by conducting a limited number of field tests, which are labor-saving and time-efficient. The model's accuracy consistently improves across diverse scenarios and optimal algorithms. The proposed compaction strategy adopts a physics-and-data dual-driven approach, aimed at practical engineering applications and guiding the compaction procedure.
[...]Read more.2025, 17(5): 3280-3288. doi:10.1016/j.jrmge.2024.07.012
Although the increase in the frequency of mass wasting events in the Sedongpu gully in recent years indicates that the Sedongpu gully has entered an intense mass wasting period, the current literature focuses only on ice‒rock avalanche events and lacks comprehensive knowledge of Sedongpu gully activity. To clarify the spatiotemporal distribution and scales of mass wasting events, we analysed multiple images from 1969 to present (including optical images and synthetic aperture radar (SAR) images) and topography data from 2013 to present. Since 1969, there have been at least 19 obvious mass wasting events that can be divided into 3 subpatterns: ice‒rock avalanches (IRAs, 8 events), ice‒moraine avalanches (IMAs, 2 events), and glacier debris flows (GDFs, 9 events). Since 2017, the Sedongpu gully has entered the most active period, i.e. more than 68% of events occurred after 2017, and approximately 530 Mm3 and 185 Mm3 of materials were removed from mixtures of glacial and moraine (MGM) and glacial source areas (GSAs). Recent continuous warning states that the temperature of the Sedongpu gully area exceeded 0 °C from April to July 2012, and the 2017 Mw 6.4 Nyingchi earthquake was critical in the current intense erosion state.
[...]Read more.2025, 17(5): 3289-3297. doi:10.1016/j.jrmge.2024.11.028
In rock engineering, the cyclic shear characteristics of rough joints under dynamic disturbances are still insufficiently studied. This study conducted cyclic shear experiments on rough joints under dynamic normal loads to assess the impact of shear frequency (fh) and shear displacement amplitude (ud) on the frictional properties of the joint. The results reveal that within a single shearing cycle, the normal displacement negatively correlates with the dynamic normal force. As the shear cycle number increases, the joint surface undergoes progressive wear, resulting in an exponential decrease in the peak normal displacement. In the cyclic shearing procedure, the forward peak values of shear force and friction coefficient display larger fluctuations at either lower or higher shear frequencies. However, under moderate shear frequency conditions, the changes in the shear strength of the joint surface are smaller, and the degree of degradation post-shearing is relatively limited. As the shear displacement amplitude increases, the range of normal deformation within the joint widens. Furthermore, after shearing, the corresponding joint roughness coefficient trend shows a gradual decrease with an increasing shear displacement amplitude, while varying with the shearing frequency in a pattern that initially rises and then falls, with a turning point at 0.05 Hz. The findings of this research contribute to a profound comprehension of the cyclic frictional properties of rock joints under dynamic disturbances.
[...]Read more.