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Understanding the thermal conductivity of granite is critical for many geological and deep engineering applications. The heated granite was subjected to air-, water-, and liquid nitrogen (LN2-) coolings in this context. The transient hot-wire technique was used to determine the equivalent thermal conductivity (ETC) of the granite before and after treatment. The deterioration mechanism of ETC is analyzed from the meso-perspective. Finally, the numerical model is used to quantitatively study the impact of cooling rate on the microcrack propagation and heat conduction characteristics of granite. The results show that the ETC of granite is not only related to the heating temperature, but also affected by the cooling rate. The ETC of granite decreases nonlinearly with increasing heating temperature. A faster cooling rate causes a greater decrease in ETC at the same heating temperature. The higher the heating temperature, the stronger the influence of cooling rate on ETC. The main explanation for the decrease in ETC of granite is the increase in porosity and microcrack density produced by the formation and propagation of pore structure and microcracks during heating and cooling. Further analysis displays that the damage of granite at the heating stage is induced by the difference in thermal expansion and elastic properties of mineral particles. At the cooling stage, the faster cooling rate causes a higher temperature gradient, which in turn produces greater thermal stress. As a result, it not only causes new cracks in the granite, but also aggravates the damage at the heating stage, which induces a further decrease in the heat conduction performance of granite, and this scenario is more obvious at higher temperatures.

Laser scanning technology has been widely used in landslide aspects. However, the existing deformation analysis based on terrain laser scanners can only provide limited information, which is insufficient for understanding landslide kinematics and failure mechanisms. To overcome this limitation, this paper proposes an automated method for processing point clouds collected in landslide physical modeling. This method allows the acquisition of quantitative three-dimensional (3D) deformation field information. The results show the organized and spatially related point cloud segmentation in terms of spherical targets. The segmented point clouds can be fitted to determine the locations of all preset targets and their corresponding location changes. The proposed method has been validated based on theoretical analysis and numerical and physical tests, which indicates that it can batch-process massive data sets with high computational efficiency and good noise resistance. Compared to existing methods, this method shows a significant potential for understanding landslide kinematics and failure mechanisms and advancing the application of 3D laser scanning in geotechnical modeling.

The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering. Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding. To assess the slope stability problems with a more desirable computational effort, many machine learning (ML) algorithms have been proposed. However, most ML-based techniques require that the training data must be in the same feature space and have the same distribution, and the model may need to be rebuilt when the spatial distribution changes. This paper presents a new ML-based algorithm, which combines the principal component analysis (PCA)-based neural network (NN) and transfer learning (TL) techniques (i.e. PCA–NN–TL) to conduct the stability analysis of slopes with different spatial distributions. The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry. The PCA method is incorporated into the neural network algorithm (i.e. PCA-NN) to increase the computational efficiency by reducing the input variables. It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms (i.e. NN and decision trees, DT). Furthermore, the PCA–NN–TL algorithm shows great potential in assessing the stability of slope even with fewer training data.

The abrupt occurrence of the Zhongbao landslide is totally unexpected, resulting in the destruction of local infrastructure and river blockage. To review the deformation history of the Zhongbao landslide and prevent the threat of secondary disasters, the small baseline subsets (SBAS) technology is applied to process 59 synthetic aperture radar (SAR) images captured from Sentinel-1A satellite. Firstly, the time series deformation of the Zhongbao landslide along the radar line of sight (LOS) direction is calculated by SBAS technology. Then, the projection transformation is conducted to determine the slope displacement. Furthermore, the Hurst exponent of the surface deformation along the two directions is calculated to quantify the hidden deformation development trend and identify the unstable deformation areas. Given the suddenness of the Zhongbao landslide failure, the multi-temporal interferometric synthetic aperture radar (InSAR) technology is the ideal tool to obtain the surface deformation history without any monitoring equipment. The obtained deformation process indicates that the Zhongbao landslide is generally stable with slow creep deformation before failure. Moreover, the Hurst exponent distribution on the landslide surface in different time stages reveals more deformation evolution information of the Zhongbao landslide, with partially unstable areas detected before the failure. Two potential unstable areas after the Zhongbao landslide disaster are revealed by the Hurst exponent distribution and verified by the GNSS monitoring results and deformation mechanism discussion. The method combining SBAS-InSAR and Hurst exponent proposed in this study could help prevent and control secondary landslide disasters.

The influences of biological, chemical, and flow processes on soil structure through microbially induced carbonate precipitation (MICP) are not yet fully understood. In this study, we use a multi-level thresholding segmentation algorithm, genetic algorithm (GA) enhanced Kapur entropy (KE) (GAE-KE), to accomplish quantitative characterization of sandy soil structure altered by MICP cementation. A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation (SR) micro-CT with a resolution of 6.5 μm. After validation, tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores. The spatial distributions of porosity, pore structure parameters, and flow characteristics were calculated for quantitative characterization. The results offer pore-scale insights into the MICP treatment effect, and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: long-term deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslide-prone areas.

To achieve the loading of the stress path of hard rock, the spherical discrete element model (DEM) and the new flexible membrane technology were utilized to realize the transient loading of three principal stresses with arbitrary magnitudes and orientations. Furthermore, based on the deep tunnel of China Jinping Underground Laboratory II (CJPL-II), the deformation and fracture evolution characteristics of deep hard rock induced by excavation stress path were analyzed, and the mechanisms of transient loading-unloading and stress rotation-induced fractures were revealed from a mesoscopic perspective. The results indicated that the stress–strain curve exhibits different trends and degrees of sudden changes when subjected to transient changes in principal stress, accompanied by sudden changes in strain rate. Stress rotation induces spatially directional deformation, resulting in fractures of different degrees and orientations, and increasing the degree of deformation anisotropy. The correlation between the degree of induced fracture and the unloading magnitude of minimum principal stress, as well as its initial level is significant and positive. The process of mechanical response during transient unloading exhibits clear nonlinearity and directivity. After transient unloading, both the minimum principal stress and minimum principal strain rate decrease sharply and then tend to stabilize. This occurs from the edge to the interior and from the direction of the minimum principal stress to the direction of the maximum principal stress on the ε1-ε3 plane. Transient unloading will induce a tensile stress wave. The ability to induce fractures due to changes in principal stress magnitude, orientation and rotation paths gradually increases. The analysis indicates a positive correlation between the abrupt change amplitude of strain rate and the maximum unloading magnitude, which is determined by the magnitude and rotation of principal stress. A high tensile strain rate is more likely to induce fractures under low minimum principal stress.

The geometric properties of fracture surfaces significantly influence shear-seepage in rock fractures, introducing complexities to fracture modelling. The present study focuses on the hydro-mechanical behaviours of rough rock fractures during shear-seepage processes to reveal how dilatancy and fracture asperities affect these phenomena. To achieve this, an improved shear-flow model (SFM) is proposed with the incorporation of dilatancy effect and asperities. In particular, shear dilatancy is accounted for in both the elastic and plastic stages, in contrast to some existing models that only consider it in the elastic stage. Depending on the computation approaches for the peak dilatancy angle, three different versions of the SFM are derived based on Mohr-Coulomb, joint roughness coefficient-joint compressive strength (JRC-JCS), and Grasselli's theories. Notably, this is a new attempt that utilizes Grasselli's model in shear-seepage analysis. An advanced parameter optimization method is introduced to accurately determine model parameters, addressing the issue of local optima inherent in some conventional methods. Then, model performance is evaluated against existing experimental results. The findings demonstrate that the SFM effectively reproduces the shear-seepage characteristics of rock fracture across a wide range of stress levels. Further sensitivity analysis reveals how dilatancy and asperity affect hydraulic properties. The relation between hydro-mechanical properties (dilatancy displacement and hydraulic conductivity) and asperity parameters is analysed. Several profound understandings of the shear-seepage process are obtained by exploring the phenomenon under various conditions.

Non-contact remote sensing techniques, such as terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, have been globally applied for landslide monitoring in high and steep mountainous areas. These techniques acquire terrain data and enable ground deformation monitoring. However, practical application of these technologies still faces many difficulties due to complex terrain, limited access and dense vegetation. For instance, monitoring high and steep slopes can obstruct the TLS sightline, and the accuracy of the UAV model may be compromised by absence of ground control points (GCPs). This paper proposes a TLS- and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics (RTK)-based control points (RCPs), low-precision TLS-based control points (TCPs) and assumed control points (ACPs) to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions. The effects of GCP accuracy, GCP quantity and automatic tie point (ATP) quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed. The results show that, the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs. The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County, China, and was validated against data from multiple sources.

In current practice of bolt reaming and anchoring of roadways in soft coal and rock mass, resin cartridges bend easily under the strong pushing and stirring of bolts, and the resin accumulates in the bolt-reamed area and does not participate in the stirring. As a result, bolts encounter high drilling resistance and cannot reach the bottom of drillholes. The effective anchorage length is far less than the actual anchorage length. Bolts are not centered, and the shear is misaligned at the joint surface in the reaming area, which leads to cracking of the whole anchoring solid and large shear deformation of bolts. This study systematically analyzes the characteristics of roadway bolt reaming and anchoring. The influences of resin stirring force, bolt pull-out force, and reaming–anchoring solid strength on reaming–anchoring performance were analyzed theoretically. The main purpose is to develop a device that enhances reaming and anchoring. The mechanism through which the device strengthens the reaming–anchoring solid was analyzed theoretically. Numerical simulation and experiments were carried out to verify the improved performance of the small-pore reaming and anchoring using the proposed technology. The results showed that the stirring migration rate of the resin cartridge is greatly improved by adding the device to bolts. The reaction rate of the anchoring mixture, stirring pressure, pull-out force of the reaming and anchoring system, bolt concentricity, and shear and compressive strengths of the anchoring solid are also enhanced in the reaming area. This ensures that the resin cartridge in the reaming area is completely stirred, which greatly improves the shear resistance of the reaming–anchoring solid. Meanwhile, the drilling performance, torsional force, and stirring efficiency of bolts are maximized and prevail over those of conventional bolts.

The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision of the landslide spatial position. However, the spatial position error of landslide survey is inevitable, resulting in considerable uncertainties in LSP modeling. To overcome this drawback, this study explores the influence of positional errors of landslide spatial position on LSP uncertainties, and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error. This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example. The 30–110 m error-based multilayer perceptron (MLP) and random forest (RF) models for LSP are established by randomly offsetting the original landslide by 30, 50, 70, 90 and 110 m. The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics. Finally, a semi-supervised model is proposed to relieve the LSP uncertainties. Results show that: (1) The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors, and are lower than those of original data-based models; (2) 70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices, thus original landslides with certain position errors are acceptable for LSP; (3) Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Polymer-blend geocell sheets (PBGS) have been developed as substitute materials for manufacturing geocells. Various attempts have been made to test and predict the behaviors of commonly used geogrids, geotextiles, geomembranes, and geocells. However, the elastic-viscoplastic behaviors of novel-developed geocell sheets are still poorly understood. Therefore, this paper investigates the elastic-viscoplastic behaviors of PBGS to gain a comprehensive understanding of their mechanical properties. Furthermore, the tensile load-strain history under various loading conditions is simulated by numerical calculation for widespread utilization. To achieve this goal, monotonic loading tests, short-term creep and stress relaxation tests, and multi-load-path tests (also known as arbitrary loading history tests) are performed using a universal testing machine. The results are simulated using the nonlinear three-component (NLTC) model, which consists of three nonlinear components, i.e. a hypo-elastic component, a nonlinear inviscid component, and a nonlinear viscid component. The experimental and numerical results demonstrate that PBGS exhibit significant elastic-viscoplastic behavior that can be accurately predicted by the NLTC model. Moreover, the tensile strain rates significantly influence the tensile load, with higher strain rates resulting in increased tensile loads and more linear load-strain curves. Also, parametric analysis of the rheological characteristics reveals that the initial tensile strain rates have negligible impact on the results. The rate-sensitivity coefficient of PBGS is approximately 0.163, which falls within the typical range observed in most geosynthetics.

Deformation analysis is fundamental in geotechnical modeling. Nevertheless, there is still a lack of an effective method to obtain the deformation field under various experimental conditions. In this study, we introduce a process–based physical modeling of a pile–reinforced reservoir landslide and present an improved deformation analysis involving large strains and water effects. We collect multi–period point clouds using a terrain laser scanner and reconstruct its deformation field through a point cloud processing workflow. The results show that this method can accurately describe the landslide surface deformation at any time and area by both scalar and vector fields. The deformation fields in different profiles of the physical model and different stages of the evolutionary process provide adequate and detailed landslide information. We analyze the large strain upstream of the pile caused by the pile installation and the consequent violent deformation during the evolutionary process. Furthermore, our method effectively overcomes the challenges of identifying targets commonly encountered in geotechnical modeling where water effects are considered and targets are polluted, which facilitates the deformation analysis at the wading area in a reservoir landslide. Eventually, combining subsurface deformation as well as numerical modeling, we comprehensively analyze the kinematics and failure mechanisms of this complicated object involving landslides and pile foundations as well as water effects. This method is of great significance for any geotechnical modeling concerning large-strain analysis and water effects.

In light of the escalating global energy imperatives, mining of challenging-to-access resources, such as steeply inclined extra-thick coal seams (SIEC), has emerged as one of the future trends within the domain of energy advancement. However, there is a risk of gas and coal spontaneous combustion coupling disasters (GCC) within the goaf of SIEC due to the complex goaf structure engendered by the unique mining methodologies of SIEC. To ensure that SIEC is mined safely and efficiently, this study conducts research on the GCC within the goaf of SIEC using field observation, theoretical analysis, and numerical modeling. The results demonstrate that the dip angle, the structural dimensions in terms of width-to-length ratio, and compressive strength of the overlying rock are the key factors contributing to the goaf instability of SIEC. The gangue was asymmetrically filled, primarily accumulating within the central and lower portions of the goaf, and the filling height increased proportionally with the advancing caving height, the expansion coefficient, and the thickness of the surrounding rock formation. The GCC occurs in the goaf of SIEC, with an air-return side range of 41 m and an air-intake side range of 14 m, at the intersection area of the “”-shaped gas concentration distribution (gas explosion). The optimal nitrogen flow rate is 1000 m3/h with an injection port situated 25 m away from the working face for the highest nitrogen diffusion efficacy and lowest risk of gas explosion, coal spontaneous combustion, and GCC. It has significant engineering applications for ensuring the safe mining of SIEC threatened by the GCC.

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.

Historically, landslides have been the primary type of geological disaster worldwide. Generally, the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations. Moreover, the stability of reservoir banks changes with the long-term dynamics of external disaster-causing factors. Thus, assessing the time-varying reliability of reservoir landslides remains a challenge. In this paper, a machine learning (ML) based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction. This study systematically investigated the prediction performances of three ML algorithms, i.e. multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). Additionally, the effects of the data quantity and data ratio on the predictive power of deep learning models are considered. The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides. The CNN model outperforms both the MLP and LSTM models in predicting the failure probability. Furthermore, selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Granitic veins (GVs) have a significant influence on the mechanical responses of tunnels excavated in granitic strata. Distinguishing the mechanical properties of host granites (HGs), GVs and vein-granite interfaces (VGIs) is critical. For this, this paper analyzed the mechanical behaviors and rupture processes of typical HG, GV, and VGI samples under uniaxial compression condition. For the rocks studied, although the linear axial stress‒strain relation can be identified and the deformation modulus can be determined, the transverse deformation developed nonlinearly with axial stress. As a result, the instantaneous Poisson's ratio increases continuously and may even exceed 0.5, making it extremely difficult to accurately determine the Poisson's ratio. In addition, the studied GV samples were found to be significantly brittle, indicating that large-scale GVs cannot be ignored when assessing rockburst hazards in granitic strata with brittle GVs. In terms of the rupture process, the HG and GV samples were gradually damaged by the formation of small-scale cracks and then ruptured by large cracks formed from small-scale cracks, whereas the VGI samples ruptured along large cracks with significant energy release. By examining the characteristic stress thresholds of these three granites, it is noted that the crack closure stress σcc exceeds both the crack initiation stress σci and the crack damage stress σcd for the HG and VGI samples. The transverse damage to a tested sample appears to be significantly greater than the axial damage, which is essentially related to the rock grain size and grain size distribution.

This paper aims to investigate the role of bi-directional shear in the mechanical behaviour of granular materials and macro-micro relations by conducting experiments and discrete element method (DEM) modelling. The bi-directional shear consists of a static shear consolidation and subsequent shear under constant vertical stress and constant volume conditions. A side wall node loading method is used to exert bi-directional shear of various angles. The results show that bi-directional shear can significantly influence the mechanical behaviour of granular materials. However, the relationship between bi-directional shear and mechanical responses relies on loading conditions, i.e. constant vertical stress or constant volume conditions. The stress states induced by static shear consolidation are affected by loading angles, which are enlarged by subsequent shear, consistent with the relationship between bi-directional shear and principal stresses. It provides evidence for the dissipation of stresses accompanying static liquefaction of granular materials. The presence of bi-directional principal stress rotation (PSR) is demonstrated, which evidences why the bi-directional shear of loading angles with components in two directions results in faster dissipations of stresses with static liquefaction. Contant volume shearing leads to cross-anisotropic stress and fabric at micro-contacts, but constant vertical stress shearing leads to complete anisotropic stress and fabric at micro-contacts. It explains the differentiating relationship between stress-strain responses and fabric anisotropy under these two conditions. Micromechanical signatures such as the slip state of micro-contacts and coordination number are also examined, providing further insights into understanding granular behaviour under bi-directional shear.

When assessing the sliding stability of a concrete dam, the influence of large-scale asperities in the sliding plane is often ignored due to limitations of the analytical rigid body assessment methods provided by current dam assessment guidelines. However, these asperities can potentially improve the load capacity of a concrete dam in terms of sliding stability. Although their influence in a sliding plane has been thoroughly studied for direct shear, their influence under eccentric loading, as in the case of dams, is unknown. This paper presents the results of a parametric study that used finite element analysis (FEA) to investigate the influence of large-scale asperities on the load capacity of small buttress dams. By varying the inclination and location of an asperity located in the concrete-rock interface along with the strength of the rock foundation material, transitions between different failure modes and correlations between the load capacity and the varied parameters were observed. The results indicated that the inclination of the asperity had a significant impact on the failure mode. When the inclination was 30° and greater, interlocking occurred between the dam and foundation and the governing failure modes were either rupture of the dam body or asperity. When the asperity inclination was significant enough to provide interlocking, the load capacity of the dam was impacted by the strength of the rock in the foundation through influencing the load capacity of the asperity. The location of the asperity along the concrete-rock interface did not affect the failure mode, except for when the asperity was located at the toe of the dam, but had an influence on the load capacity when the failure occurred by rupture of the buttress or by sliding. By accounting for a single large-scale asperity in the concrete-rock interface of the analysed dam, a horizontal load capacity increase of 30%–160% was obtained, depending on the inclination and location of the asperity and the strength of the foundation material.

To investigate the long-term stability of deep rocks, a three-dimensional (3D) time-dependent model that accounts for excavation-induced damage and complex stress state is developed. This model comprises three main components: a 3D viscoplastic isotropic constitutive relation that considers excavation damage and complex stress state, a quantitative relationship between critical irreversible deformation and complex stress state, and evolution characteristics of strength parameters. The proposed model is implemented in a self-developed numerical code, i.e. CASRock. The reliability of the model is validated through experiments. It is indicated that the time-dependent fracturing potential index (ξTFPI) at a given time during the attenuation creep stage shows a negative correlation with the extent of excavation-induced damage. The time-dependent fracturing process of rock demonstrates a distinct interval effect of the intermediate principal stress, thereby highlighting the 3D stress-dependent characteristic of the model. Finally, the influence of excavation-induced damage and intermediate principal stress on the time-dependent fracturing characteristics of the surrounding rocks around the tunnel is discussed.

The travel time of rock compressional waves is an essential parameter used for estimating important rock properties, such as porosity, permeability, and lithology. Current methods, like wireline logging tests, provide broad measurements but lack finer resolution. Laboratory-based rock core measurements offer higher resolution but are resource-intensive. Conventionally, wireline logging and rock core measurements have been used independently. This study introduces a novel approach that integrates both data sources. The method leverages the detailed features from limited core data to enhance the resolution of wireline logging data. By combining machine learning with random field theory, the method allows for probabilistic predictions in regions with sparse data sampling. In this framework, 12 parameters from wireline tests are used to predict trends in rock core data. The residuals are modeled using random field theory. The outcomes are high-resolution predictions that combine both the predicted trend and the probabilistic realizations of the residual. By utilizing unconditional and conditional random field theories, this method enables unconditional and conditional simulations of the underlying high-resolution rock compressional wave travel time profile and provides uncertainty estimates. This integrated approach optimizes the use of existing core and logging data. Its applicability is confirmed in an oil project in West China.

The presence of discontinuities (e.g. faults, fractures, veins, layering) in crystalline rocks can be challenging for seismic interpretations because the wide range of their size, orientation, and intensity, which controls the mechanical properties of the rock and elastic wave propagation, resulting in equally varying seismic responses at different scales. The geometrical characterisation of adjacent outcrop discontinuity networks allows a better understanding of the nature of the subsurface rocks and aids seismic interpretation. In this study, we characterise the discontinuity network of the Balmuccia peridotite (BP) in the Ivrea–Verbano Zone (IVZ), northwestern Italy. This geological body is the focus of the Drilling the Ivrea–Verbano zonE (DIVE), an international continental scientific drilling project, and two active seismic surveys, SEismic imaging of the Ivrea ZonE (SEIZE) and high-resolution SEIZE (Hi-SEIZE), which aim to resolve the subsurface structure of the DIVE drilling target through high-resolution seismic imaging. For fracture characterisation, we developed two drone-based digital outcrop models (DOMs) at two different resolutions (10−3–10 m and 10−1–103 m), which allowed us to quantitatively characterise the orientation, size, and intensity of the main rock discontinuities. These properties affect the seismic velocity and consequently the interpretation of the seismic data. We found that (i) the outcropping BP discontinuity network is represented by three more sets of fractures with respect to those reported in the literature; (ii) the discontinuity sizes follow a power-law distribution, indicating similarity across scales, and (iii) discontinuity intensity is not uniformly distributed along the outcrop. Our results help to explain the seismic behaviour of the BP detected by the SEIZE survey, suggesting that the low P-wave velocities observed can be related to the discontinuity network, and provide the basic topological parameters (orientation, density, distribution, and aperture) of the fracture network unique to the BP. These, in turn, can be used for interpretation of the Hi-SEIZE seismic survey and forward modelling of the seismic response.

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications. This study addresses the importance of estimating the friction angle and presents the development of four soft computing models: YJ-FPA-MLPnet, YJ-CRO-MLPnet, YJ-ACOC-MLPnet, and YJ-CSA-MLPnet. First of all, the Yeo-Johnson (YJ) transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances. This technique is expected to improve the accuracy of friction angle prediction models. The friction angle prediction models then utilized multi-layer perceptron neural networks (MLPnet) and metaheuristic optimization algorithms to further enhance performance, including flower pollination algorithm (FPA), coral reefs optimization (CRO), ant colony optimization continuous (ACOC), and cuckoo search algorithm (CSA). The prediction models without the YJ technique, i.e. FPA-MLPnet, CRO-MLPnet, ACOC-MLPnet, and CSA-MLPnet, were then compared to those with the YJ technique, i.e. YJ-FPA-MLPnet, YJ-CRO-MLPnet, YJ-ACOC-MLPnet, and YJ-CSA-MLPnet. Among these, the YJ-CRO-MLPnet model demonstrated superior reliability, achieving an accuracy of up to 83% in predicting the friction angle of clay in practical engineering scenarios. This improvement is significant, as it represents an increase from 1.3% to approximately 20% compared to the models that did not utilize the YJ transformation technique.

Soil tensile strength is a critical parameter governing the initiation and propagation of tensile cracking. This study proposes an eco-friendly approach to improve the tensile behavior and crack resistance of clayey soils. To validate the feasibility and efficacy of the proposed approach, direct tensile tests were employed to determine the tensile strength of the compacted soil with different W-OH treatment concentrations and water contents. Desiccation tests were also performed to evaluate the effectiveness of W-OH treatment in enhancing soil tensile cracking resistance. During this period, the effects of W-OH treatment concentration and water content on tensile properties, soil suction and microstructure were investigated. The tensile tests reveal that W-OH treatment has a significant impact on the tensile strength and failure mode of the soil, which not only effectively enhances the tensile strength and failure displacement, but also changes the brittle failure behavior into a more ductile quasi-brittle failure behavior. The suction measurements and mercury intrusion porosimetry (MIP) tests show that W-OH treatment can slightly reduce soil suction by affecting skeleton structure and increasing macropores. Combined with the microstructural analysis, it becomes evident that the significant improvement in soil tensile behavior through W-OH treatment is mainly attributed to the W-OH gel's ability to provide additional binding force for bridging and encapsulating the soil particles. Moreover, desiccation tests demonstrate that W-OH treatment can significantly reduce or even inhibit the formation of soil tensile cracking. With the increase of W-OH treatment concentration, the surface crack ratio and total crack length are significantly reduced. This study enhances a fundamental understanding of eco-polymer impacts on soil mechanical properties and provides valuable insight into their potential application for improving soil crack resistance.

Landslides occurring in sensitive clay often result in widespread destruction, posing a significant risk to human lives and property due to the substantial decrease in undrained shear strength during deformation. Assessing the consequences of these landslides is challenging and necessitates robust numerical methods to comprehensively investigate their failure mechanisms. While studies have extensively explored upward progressive landslides in sensitive clays, understanding downward progressive cases remains limited. In this study, we utilised the nodal integration-based particle finite element method (N-PFEM) with a nonlinear strain-softening model to analyse downward progressive landslides in sensitive clay on elongated slopes, induced by surcharge loads near the crest. We focused on elucidating the underlying failure mechanisms and evaluating the effects of different soil parameters and strain-softening characteristics. The simulation results revealed the typical pattern for downward landslides, which typically start with a localised failure in proximity to the surcharge loads, followed by a combination of different types of failure mechanisms, including single flow slides, translational progressive landslides, progressive flow slides, and spread failures. Additionally, inclined shear bands occur within spread failures, often adopting distinctive ploughing patterns characterised by triangular shapes. The sensitive clay thickness at the base, the clay strength gradient, the sensitivity, and the softening rate significantly influence the failure mechanisms and the extent of diffused displacement. Remarkably, some of these effects mirror those observed in upward progressive landslides, underscoring the interconnectedness of these phenomena. This study contributes valuable insights into the complex dynamics of sensitive clay landslides, shedding light on the intricate interplay of factors governing their behaviour and progression.

This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method (QFOSM). The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids. Based on this geometric interpretation, the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system. The proposed method has the advantage of computational simplicity, akin to the conventional first-order second-moment method (FOSM), while providing estimation accuracy close to that of the first-order reliability method (FORM). Its performance is demonstrated with a numerical example and three slope examples. The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters. The proposed method does not involve complex optimization or iteration required by the FORM. It can provide a valuable complement to the existing approximate reliability analysis methods, offering rapid sensitivity evaluation and slope reliability analysis.

The deterioration of unstable rock mass raised interest in evaluating rock mass quality. However, the traditional evaluation method for the geological strength index (GSI) primarily emphasizes the rock structure and characteristics of discontinuities. It ignores the influence of mineral composition and shows a deficiency in assessing the integrity coefficient. In this context, hyperspectral imaging and digital panoramic borehole camera technologies are applied to analyze the mineral content and integrity of rock mass. Based on the carbonate mineral content and fissure area ratio, the strength reduction factor and integrity coefficient are calculated to improve the GSI evaluation method. According to the results of mineral classification and fissure identification, the strength reduction factor and integrity coefficient increase with the depth of rock mass. The rock mass GSI calculated by the improved method is mainly concentrated between 40 and 60, which is close to the calculation results of the traditional method. The GSI error rates obtained by the two methods are mostly less than 10%, indicating the rationality of the hyperspectral-digital borehole image coupled evaluation method. Moreover, the sensitivity of the fissure area ratio (Sr) to GSI is greater than that of the strength reduction factor (α), which means the proposed GSI is suitable for rocks with significant fissure development. The improved method reduces the influence of subjective factors and provides a reliable index for the deterioration evaluation of rock mass.

Comprehensive data from in situ and laboratory tests in residual soil have been presented by Zhang et al. (2023). A number of issues addressed in the paper have been the interest of the discussers, namely the characterisation and behaviour of residual soils, limitation of piezocone testing due to the capacity of the entire system, measurement of shear wave velocity, rate effect of piezocone (CPTU) testing and piezocone testing with dual pore pressure penetrometers. Clarification and complementation of these issues are required with regard to both the execution and interpretation of the tests.

Degradation of rock mass produced by rock blasting, stress relief, and other causes is an important factor in the assessment of rock strength. Quantified as a disturbance factor, such degradation varies depending on blasting control, stress state and stress relief, and rock mass quality. This study focuses on the impact of disturbance on the safety of slopes. The disturbance in the rock mass is characterized by the geometry of the disturbed zone, its size, the magnitude, and the decaying rate with the distance away from the slope surface. A method accounting for decay of rock disturbance is presented. A study of the impact of rock disturbance characteristics on the quantitative stability measures of slopes was carried out. These characteristics included disturbed zone geometry, its thickness, the maximum magnitude of the disturbance factor, and the rate of disturbance decaying. The thickness of the disturbed zone and the maximum factor of disturbance were found to have the greatest impact. For example, the factor of safety for a 45° slope in low-quality rock mass can decrease from 1.96 to 1.09 as the thickness of the disturbed zone increases from 1/4 of slope height H to the double of H and the maximum disturbance factor increases from 0.5 to 1. Uniform thickness of a disturbed zone was found to yield more conservative outcomes than the triangular zones did. The critical failure surfaces were found to be shallow for high rates of disturbance decay, and they were the deepest for spatially uniform disturbance factors.

A series of direct shear tests under constant normal loading conditions were carried out on specimens of bolted sandstone single-joint treated with different numbers of dry–wet cycles. The experimental results show that the peak shear strength and shear stiffness of bolted sandstone joints were significantly reduced after 12 dry–wet cycles. The decrease in the shear strength of rough joints is more significant than that of flat joints. Due to the decrease in the strength of the surrounding rock, the deformation characteristics of the bolts are significantly affected by the number of dry–wet cycles performed. With an increase in the number of dry–wet cycles, the plastic hinge length of the bolt gradually increases, resulting in an increase in the corresponding shear displacement when the bolt breaks. Compared with the tensile–shear failure mode of the bolts in flat joints, the tensile–bending failure mode arises for bolts in rough joints. A shear curve model describing the whole process of bolted rock joints is established based on the deterioration of rock mechanical parameters caused by dry‒wet cycles. The model proposed considers the change in the friction angle of the joint surface with the shear displacement, which is applied to the derivation of the model by introducing the dynamic evolutionary friction angle parameter. The reasonably good agreement between a predicted curve and the corresponding experimental curve indicates that this method can effectively predict the shear strength of a bolted rock joint involving rough joint under dry–wet cycling conditions.

Intense precipitation infiltration and intricate excavation processes are crucial factors that impact the stability and security of towering and steep rock slopes within mining sites. The primary aim of this research was to investigate the progression of cumulative failure within a cracked rock formation, considering the combined effects of precipitation and excavation activities. The study was conducted in the Huangniuqian eastern mining area of the Dexing Copper Mine in Jiangxi Province, China. An engineering geological investigation was conducted, a physical model experiment was performed, numerical calculations and theoretical analysis were conducted using the matrix discrete element method (MatDEM), and the deformation characteristics and the effect of the slope angle of a fractured rock mass under different scenarios were examined. The failure and instability mechanisms of the fractured rock mass under three slope angle models were analyzed. The experimental results indicate that as the slope angle increases, the combined effect of rainfall infiltration and excavation unloading is reduced. A novel approach to simulating unsaturated seepage in a rock mass, based on the van Genuchten model (VGM), has been developed. Compared to the vertical displacement observed in a similar physical experiment, the average relative errors associated with the slope angles of 45°, 50°, and 55° were 2.094%, 1.916%, and 2.328%, respectively. Accordingly, the combined effect of rainfall and excavation was determined using the proposed method. Moreover, the accuracy of the numerical simulation was validated. The findings contribute to the seepage field in a meaningful way, offering insight that can inform and enhance existing methods and theories for research on the underlying mechanism of ultra-high and steep rock slope instability, which can inform the development of more effective risk management strategies.

This paper focuses on the performance of a braced deep excavation in soft soil based on field monitoring and numerical modeling. Laboratory tests were conducted to determine the soil parameters used in the modified Cam–Clay (MCC) model. Intelligent field monitoring means were adopted and a three-dimensional model was established. Spatial and temporal effects induced by the excavation are investigated for the deep-large foundation pit in soft soil. Deformation characteristics of the enclosure structure and the surrounding environment throughout the excavation process are presented. The behaviors of diaphragm walls, columns, the maximum wall deflection rate, ground surface settlement, and utility pipelines were focused on and investigated during the whole excavation process. Besides, the axial forces of the internal supports are analyzed. Based on the measured and simulated data, the following main conclusions were obtained: the numerical simulation results are in good agreement with the measured values, which proves the accuracy of the model parameters; the wall and the ground surface showed the maximum displacement increment at stage 9, which was a coupled product of the “creep effect” of the soft soil in Nanjing, China and the “depth effect” of the excavation; as the excavation progressed, the ground settlement changed from a “rising” to a “spoon-shaped” trend, δvm was measured between δvm = 0.0686%H and δvm = 0.1488%H; the rebound deformation curve of the pit bottom was corrugated, and the depth of disturbance of the pit bottom after the completion of soil unloading was 2–3 times the excavation depth; the closer the pipeline is to the corner of the pit, the less the excavation process will affect the settlement of the pipeline and the less the obvious pit corner effect will occur; the support strength of the buttress and the longest corner brace should be strengthened during the actual construction process to ensure the stability of the foundation deformation.

Since the classical element model cannot describe the nonlinear characteristics of rock during the entire compressive creep process, nonlinear elements and creep damage are generally introduced in the model to resolve this issue. However, several previous studies have reckoned that creep damage in rock only occurs in the accelerated creep stage and is only described by the Weibull distribution. Nevertheless, the creep damage mechanism of rocks is still not clearly understood. In this study, a reasonable representation of the damage variables of solid materials is presented. Specifically, based on the Gurson damage model, the damage state functions reflecting the constant creep stage and accelerated creep stage of rock are established. Further, the one-dimensional and three-dimensional creep damage constitutive equations of rock are derived by modifying the Nishihara model. Finally, the creep-acoustic emission tests of phyllite under different confining pressures are conducted to examine the creep damage characteristics of phyllite. And the proposed constitutive model is verified by analyzing the results of creep tests performed on saturated phyllite. Overall, this study reveals the relationship between the creep characteristics of rocks and the corresponding damage evolution pattern, which bridges the gap between the traditional theory and the quantitative analysis of rock creep and its damage pattern.

This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines. To reduce numerical simulation time and optimize ventilation design, several back propagation neural network (BPNN) models optimized by honey badger algorithm (HBA) with four chaos mapping (CM) functions (i.e., Chebyshev (Che) map, Circle (Cir) map, Logistic (Log) map, and Piecewise (Pie) map) are developed to predict the migration time. 125 simulations by the computational fluid dynamics (CFD) method are used to train and test the developed models. The determination coefficient (R2), the variance accounted for (VAF), the Willmott’s index (WI), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the sum of squares error (SSE) are utilized to evaluate the model performance. The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R2 (0.9945), WI (0.9986), VAF (99.4811%), and the lowest values of RMSE (15.7600), MAPE (0.0343) and SSE (6209.4), respectively. The wind velocity in roadway (Wv) is the most important feature for predicting the migration time of toxic fumes. Furthermore, the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.

Clogging frequently occurs in the cutter head, excavation chamber or screw conveyor when an earth pressure balance (EPB) shield machine is tunneling in soft or silty clay ground with high clay mineral content. In this paper, montmorillonite, kaolinite, and illite were selected as research objects, and molecular dynamics simulation and laboratory experiment were adopted. At the microscopic scale, dynamic contact behavior and interfacial mechanical behavior of the interface between clay minerals and water/surfactant solution was simulated and the interfacial adhesion and conditioning mechanism between clay minerals and water/surfactant solution was revealed. Thus, sodium dodecyl benzene sulfonate surfactant was selected as the main composition of the soil conditioner. Then, the adhesion stress before and after soil conditioning and the contact angles between clay minerals and water/surfactant solution were tested and analyzed at the macroscopic scale. The result shows that the contact angle between droplet and clay mineral surface is an important parameter to characterize soil adhesion. The simulation rules of the microscopic contact angle are consistent with the experiment results. Furthermore, the adsorption energy between microscopic substances is dominated by electrostatic force, which can reflect the adhesion stress between macroscopic substances. Soil adhesion stress can be effectively decreased by adding the surfactant to the soil conditioner.

When tunnelling in difficult ground conditions, shield machine would inevitably produce significant ground loss and vibration, which may disturb the ground ahead of the tunnel face. In this paper, discrete element models calibrated by model tests were established to investigate the response of tunnel face under the coupling effects of unloading and cutterhead vibrations. The results show that the friction angle reduction under cyclic loading and vibration attenuation in the sandy ground are significant and can be estimated by the fitted exponential functions. Under cutterhead vibration, the tunnel face stability is undermined and the limit support pressure (LSP) increases to 1.4 times as that in the static case with the growth of frequency and amplitude. Meanwhile, the loosening zone becomes wider and the arching effect is weakened with the reduction of peak horizontal stress and the increase of vertical stress above the tunnel. Based on the numerical results, a pseudo-static method was introduced into the limit equilibrium analysis of the wedge-prism model for calculating the LSP under vibration. With an error rate less than 5.2%, the proposed analytical method is well validated. Further analytical calculation reveals that the LSP would increase with the growth of vibration amplitude, vibration frequency and covered depth but decrease with the increase of friction angle. This study can not only lay a solid foundation for the further investigation of ground loss, ground water and soft-hard heterogeneous ground under cutterhead vibration, but also provide meaningful references for the control of environmental disturbance in practice.

This paper develops a new approach for reliability-based design (RBD) of tunnel face support pressure from a quantile value perspective. A surrogate model is constructed to calculate the collapse pressures of the random samples generated by a single run of Monte Carlo simulation (MCS). The cumulative distribution function (CDF) of the collapse pressure is then obtained and the support pressure aiming at a target failure probability is chosen as the upper quantile value of the collapse pressures. The proposed approach does not require repetitive reliability analyses compared to the existing methods. Moreover, a direct relationship between the target failure probability and the required support pressure is established. An illustrative example is used to demonstrate the implementation procedure. The accuracy of the reliability-based support pressures is verified by direct MCS incorporating with three-dimensional numerical simulations. Finally, the influencing factors, including the sample size of MCS, the correlation coefficient between random variables, the choice of experimental points, and the surrogate model, are investigated. This method can play a complementary role to available approaches due to its advantages of simplicity and efficiency.

In large-diameter shield tunnels, applying the double-layer lining structure can improve the load-bearing properties and maintain the stability of segmental lining. The secondary lining thickness is a key parameter in the design of a double lining structure, which is worth being explored. Based on an actual large-diameter shield tunnel, loading model tests are carried out to investigate the effect of the secondary lining thickness on the mechanical behaviours of the double lining structure. The test results show that within the range of secondary lining thicknesses discussed, the load-bearing limit of the double-layer lining increases with growing secondary lining thickness. As a passive support, the secondary lining acts as an auxiliary load-bearing structure by contacting the segment. And changes in secondary lining thickness have a significant effect on the contact state between the segment and secondary lining, with both the contact pressure level and the contact area between the two varying. For double-layer lining structures in large-diameter shield tunnels, it is proposed that the stiffness of the secondary lining needs to be matched to the stiffness of the segment, as this allows them to have a coordinated deformation and a good joint load-bearing effect.

The application of steel strut force servo systems in deep excavation engineering is not widespread, and there is a notable scarcity of in-situ measured datasets. This presents a significant research gap in the field. Addressing this, our study introduces a valuable dataset and application scenarios, serving as a reference point for future research. The main objective of this study is to use machine learning (ML) methods for accurately predicting strut forces in steel supporting structures, a crucial aspect for the safety and stability of deep excavation projects. We employed five different ML methods: radial basis function neural network (RBFNN), back propagation neural network (BPNN), K-Nearest Neighbor (KNN), support vector machine (SVM), and random forest (RF), utilizing a dataset of 2208 measured points. These points included one output parameter (strut forces) and seven input parameters (vertical position of strut, plane position of strut, time, temperature, unit weight, cohesion, and internal frictional angle). The effectiveness of these methods was assessed using root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE). Our findings indicate that the BPNN method outperforms others, with RMSE, R, and MAE values of 72.1 kN, 0.9931, and 57.4 kN, respectively, on the testing dataset. This study underscores the potential of ML methods in precisely predicting strut forces in deep excavation engineering, contributing to enhanced safety measures and project planning.

High-pressure waterjet-assisted tunnel boring machine (WTBM) is an efficient method for improving the tunneling performance of a tunnel boring machine (TBM) and reducing the wear of its disc cutters in hard rock with high geostresses. Confining pressure directly affects the efficiency of rock breaking and the configuration of the disc cutters. In this study, we evaluated the effect of confining pressure on WTBM rock breaking by developing a self-designed and manufactured experimental system, including confining pressure loading, TBM disc-cutter penetration, and high-pressure waterjet. The macro fracture, acoustic emission (AE), peak normal force drop, and specific energy (SE) were analyzed for four different confining pressures (10, 20, 30, and 35 MPa). The results showed that the cutting depth of the waterjet increased linearly as the waterjet pressure increased and decreased with the gradual increase in the nozzle moving speed. The expansion and development of cracks formed rock debris, and the size of the rock fragments decreased with an increase in confining pressure. When the waterjet pressure was 280 MPa, the nozzle moving velocity was 800 mm/min and the kerf space was 75 mm, which indicated that the confining pressure, which was 23.16 MPa, minimized the cutting SE under this condition. However, regardless of the confining pressure, the maximum normal force of WTBM was less than that of a TBM, whereas the SE of WTBM was less than that of complete TBM cutting mode (CTCM). The average force drop and average drop rate of SE were approximately 25%, and 80%, respectively. The results of this study can inspire the design and mechanism of a TBM assisted by a high-pressure waterjet.

High-speed railway tunnels in various countries have continuously reported accidents of vault falling concrete blocks. Once the concrete block falling occurs, serious consequences follow, and traffic safety may be endangered. The aerodynamic shockwave evolves from the initial compression wave may be an important inducement causing the tunnel lining cracks to grow and form falling concrete blocks. A joint calculation framework is established based on ANSYS Fluent, ABAQUS, and FRANC3D for calculating the crack tip field under the aerodynamic shockwave. The intensification effect of aerodynamic shockwaves in the crack is revealed, and the evolution characteristics of the crack tip field and the influence factors of stress intensity factor (SIF) are analyzed. Results show that (1) the aerodynamic shockwave intensifies after entering the crack, resulting in more significant pressure in the crack than the input pressure. The maximum pressure of the inclined and longitudinal cracks is higher than the corresponding values of the circumferential crack, respectively. (2) The maximum SIF of the circumferential, inclined, and longitudinal crack appears at 0.5, 0.68, and 0.78 times the crack front length. The maximum SIF of the circumferential crack is higher than that of the inclined and longitudinal crack. The possibility of crack growth of the circumferential crack is the highest under aerodynamic shockwaves. (3) The influence of train speed on the SIF of the circumferential crack is more than 40%. When the train speed, crack depth, and crack length change, the change of pressure in the crack is the direct cause of the change of SIF.

This paper concentrates on the sensitivity and dynamic simulation of randomly distributed karst cave groups on tunnel stability and connectivity extended ratio based on water–rock interaction using a novel contact dynamic method (CDM). The concept of karst cave group connectivity extended ratio during tunneling and water inrush is proposed. The effects of cave shape and spatial distribution on Qiyueshan tunnel are investigated. Tunnel deformation and damage index, and connectivity extended ratio with uniform random karst cave groups are evaluated. The results demonstrate that the connectivity extended ratio is verified as a crucial judgment in predicting the safe distance and assessing the stability of the tunnel with the karst cave group. CDM model captures the fracture propagation and contact behavior of rock mass, surface flow, as well as the bidirectional water–rock interaction during the water inrush of Qiyueshan tunnel with multiple caves. A larger cave radius and smaller minimum distance between the cave and tunnel increase the deformation and damage index of the surrounding rock. When the cave radius and cave area ratio increase, the failure pattern shifts from overall to local failure. These findings potentially have broad applications in various surface and subsurface scenarios involving water–rock interactions.

The pipe curtain structure method (PSM) is a novel construction method to control ground deformation strictly. Compared with the traditional pipe-roofing and pipe jacking method, the connection between pipes in large spacings using PSM is widely acknowledged as a unique construction procedure. Further study on this connection procedure is needed to resolve similar cases in that the pipes are inevitably constructed on both sides of existing piles. Cutting the steel plate during the connection procedure is the first step, which is crucial to control the safety and stability of the surrounding environment and existing structures. The deformation mechanism and limit support pressure of the cutting steel plate during the connection between pipes in large spacings are studied in this paper, relying on the undercrossing Yifeng gate tower project of Jianning West Road River Crossing Channel in Nanjing, China. A modified 3D wedge-prism failure model is proposed using the 3D discrete element method. Combined with Terzaghi loose earth pressure theory and the limit equilibrium theory, the analytical solutions for the limit support pressure of the excavation face of the cutting steel plate are derived. The modified 3D wedge-prism failure model and corresponding analytical solutions are categorised into two cases: (a) unilateral cutting scheme, and (b) bilateral cutting scheme. The analytical solutions for the two cases are verified from the numerical simulation and in-situ data and compared with the previous solutions. The comparative analysis between the unilateral and bilateral cutting schemes indicates that the bilateral cutting scheme can be adopted as a priority. The bilateral cutting scheme saves more time and induces less ground deformation than the unilateral one due to the resistance generated from the superimposed wedge. In addition, the parametric sensitivity analysis is carried out using an orthogonal experimental design. The main influencing factors arranged from high to low are the pipe spacing, the cutting size, and the pipe burial depth. The ground deformation increases with the increased cutting size and pipe spacing. The pipe burial depth slightly affects the ground deformation if the other two factors are minor. Cutting steel plates in small sizes, excavating soil under low disturbance, and supporting pipes for high frequency can effectively reduce the ground surface subsidence.

Shipwreck salvage is a risky, time-consuming, and expensive process. Although there are many sunken ships along coastlines and in the open seas, the salvage process of a sunken ship has rarely been reported. The integrated salvage of the “Yangtze River Estuary II” shipwreck used a novel method with 22 closely locked curved rectangular pipes to form a watertight base that wrapped the shipwreck inside. The basing was lifted out of the water using a powerful crane situated on an engineering ship. For the first time, the tunneling method was used in a shipwreck salvage project, significantly reducing the disturbance to the shipwreck and its stowage, thereby preserving the original state and integrity of the shipwreck to the greatest extent. In this study, the basic concepts of the salvage method and process are explained. Solutions to critical issues in the new salvage method are provided, including jacking force prediction and major considerations for the structural design of the salvage system. The design of the salvage system and salvage process of the “Yangtze River Estuary II” shipwreck are introduced. The monitored jacking force, pipe deformation, and observed water-tightness verified that the proposed method was effective and efficient. Other possible application scenarios for the proposed method are presented at the end.

This paper presents a regional 3D geological modeling method based on the stacking ensemble technique to overcome the challenges of sparse borehole data in large-scale linear underground projects. The proposed method transforms the 3D geological modeling problem into a stratigraphic property classification problem within a subsurface space grid cell framework. Borehole data is pre-processed and trained using stacking method with five different machine learning algorithms. The resulting modelled regional cells are then classified, forming a regional 3D grid geological model. A case study for an area of 324 km2 along Xuzhou metro lines is presented to demonstrate the effectiveness of the proposed model. The study shows an overall prediction accuracy of 85.4%. However, the accuracy for key stratigraphy layers influencing the construction risk, such as karst carve strata, is only 4.3% due to the limited borehole data. To address this issue, an oversampling technique based on the synthetic minority oversampling technique (SMOTE) algorithm is proposed. This technique effectively increases the number of sparse stratigraphic samples and significantly improves the prediction accuracy for karst caves to 65.4%. Additionally, this study analyzes the impact of sampling distance on model accuracy. It is found that a lower sampling interval results in higher prediction accuracy, but also increases computational resources and time costs. Therefore, in this study, an optimal sampling distance of 1 m is chosen to balance prediction accuracy and computation cost. Furthermore, the number of geological strata is found to have a negative effect on prediction accuracy. To mitigate this, it is recommended to merge less significant stratigraphy layers, reducing computation time. For key strata layers, such as karst caves, which have a significant impact on construction risk, further on-site sampling or oversampling using the SMOTE technique is recommended.

Large deformation of sand due to soil liquefaction is a major cause for seismic damage. In this study, the mechanisms and modeling of large post-liquefaction deformation of sand considering the significant influence of water absorption in shearing and seismic wave conditions. Assessment of case histories from past earthquakes and review of existing studies highlight the importance of the two factors. Based on the micro and macro scale mechanisms for post-liquefaction shear deformation, the mechanism for water absorption in shearing after initial liquefaction is revealed. This is aided by novel designed constant water-absorption-rate shear tests. Water absorption in shearing can be classified into three types, including partial water absorption, complete water absorption, and compulsory water absorption. Under the influence of water absorption in shearing, even a strongly dilative sand under naturally drained conditions could experience instability and large shear deformation. The mechanism for amplification of post-liquefaction deformation under surface wave load is also explained via element tests and theoretical analysis. This shows that surface wave–shear wave coupling can induce asymmetrical force and resistance in sand, resulting in asymmetrical accumulation of deformation, which is amplified by liquefaction. A constitutive model, referred to as CycLiq, is formulated to capture the large deformation of sand considering water absorption in shearing and seismic wave conditions, along with its numerical implementation algorithm. The model is comprehensively calibrated based on various types of element tests and validated against centrifuge shaking table tests in the liquefaction experiments and analysis projects (LEAP). The model, along with various numerical analysis methods, is adopted in the successful simulation of water absorption in shearing and Rayleigh wave-shear wave coupling induced large liquefaction deformation. Furthermore, the model is applied to high-performance simulation for large-scale soil-structure interaction in liquefiable ground, including underground structures, dams, quay walls, and offshore wind turbines.

The paper considers topical issues of geophysics related to the possible influence of disturbances in the magnetosphere on seismicity. The study was conducted based on a detailed catalog of seismic events in southern Sakhalin for the period from 2003 to 2023. The paper aims to test the assumption that such an influence can manifest itself in individual seismogenic zones during their proximity to discharge. The testing was carried out in randomly selected segments of the West Sakhalin (WSF) and Central Sakhalin (CSF) faults. The coincidence of the moments of some seismic events (with M > 2.7) and magnetic storms with a high index (G1 and higher) was revealed in these segments. The LURR (load-unload response ratio) method was used to identify periods when fault segments were in a subcritical stress-strain state. It was shown that the main part of the coincidences occurred during the periods of increased abnormal activity of the LURR parameter.

Based on the seismic moment tensor (SMT) data of 270 earthquakes in the Central Tien Shan, which occurred from 1978 to 2021 and include 63 events from the Global Centroid Moment Tensor catalog and 207 events from the works of Aleksander D. Kostyuk and Naylya A. Sycheva, the focal parameters (seismotectonic deformations (STD), kinematic and dynamic parameters, source radius, and stress drop) were calculated. The STD calculation was based on the approaches proposed in the studies of Yuriy V. Riznichenko and Sergei L. Yunga. The area under consideration is characterized by such deformation modes as thrust, transpression, underpression, and oblique. The distribution of the Lode–Nadai coefficient was calculated and plotted. A significant part of the study area is characterized by simple compression deformation, dominated by simple compression and simple shear. To calculate the stress drop deltasigma, the values of the scalar seismic moment M0, which are determined in the SMT calculation, and the source radii r, which are calculated on the basis of theoretical and experimental models of the dependence of the source radius on the moment magnitude, were used. The radii r and stress drops deltasigma were calculated for two models of earthquake sources: the Brune model and the Madariaga– Kaneko–Shearer model. A catalog of dynamic parameters was compiled. The comparison of the kinematic and dynamic parameters of earthquakes was carried out, and the relationship of the dropped stresses with the type of movement in the source, as well as with the Lode–Nadai coefficient, was established. The results obtained in the study can be useful for specialists in other fields of knowledge – geodesy, geology, and geophysics.

A study of wave processes near Cape Svobodny on the south-eastern coast of the island Sakhalin using autonomous wave recorders and a weather station has been performed. Analysis of five-month data of sea level and temperature, atmospheric pressure and wind speed revealed that there are no significant peaks for wind and infragravity (IG) waves in the wave period range 2–600 s, and the wave energy is lower at the point protected by Cape Svobodny. During storms, there is an increase in the energy of IG waves. Waves detected at periods of 14.2 seconds, 3.62 minutes, and 8.85 minutes are related to swell and edge waves propagating seaward. For explanation of short waves, the Longuet-Higgins and Stewart theory was used which describes the dispersion of swell in the surf zone and the formation of free waves. Edge waves were analyzed using the Lamb model and the Bessel function of the first kind of zero order. Modelling of wave processes propagating shoreward revealed the presence of IG waves with periods of 20–110 seconds and edge waves with periods of 4.27–7.63 minutes, confirmed by the dispersion relation for Stokes waves on a sloping bottom. Sea water temperature fluctuations of more than 7 °C with periods of 3–100 minutes affect the propagation of waves with periods longer than 3 minutes, destroying the edge and leaky waves. Analysis of wind wave characteristics showed no significant wave processes, including wind waves, in the 2–20 second period range. The maximum wave height was observed during prolonged southern winds associated with a cyclone. This study is important for understanding wave processes in this area, aiding in predicting their behaviour and impact on the coastline.

The organization of the lichen herbarium in the Institute of Marine Geology and Geophysics of the FEB RAS was initiated in 2014 after a number of integrated expeditions in the Sakhalin Region. The herbarium is based on the collections of Aleksander K. Ezhkin and Vladimir V. Kaganov. A significant amount of lichen samples was collected in protected areas of the Sakhalin Region, in wild remote sites, and in areas of volcanic activity. Currently, a part of the collection (1178 samples, 307 species) is mounted and stored in the Laboratory of plant ecology and geoecology, which is about 1/8 of all herbarium material of lichens stored in the Institute of Marine Geology and Geophysics of the FEB RAS.