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A large number of laboratory investigations related to the permeability have been conducted on the sliding zones. Yet little attention has been paid to the particular sliding zones of the slide-prone Badong Formation. Here, we experimentally investigate the permeability nature and the mechanism of seepage in the viscous sliding zone of the Huangtupo Landslide. Saturated seepage tests have been performed first with consideration of six dry densities and thirteen hydraulic gradients, in conjunction with the mercury intrusion porosimetry test and scanning electron microscopy test for the microstructure analysis after seepage. The results show that seepage in the sliding zone soil does not follow Darcy's Law, since there is a threshold hydraulic gradient (i0) below which no flow is observed and a critical hydraulic gradient (icr) over which the hydraulic conductivity (K) tends to be stable. The percentage of bound water could be responsible for the occurrence of i0 and icr. Furthermore, pore size distributions (PSD) less than 0.6 µm and between 10 and 90 µm exhibit positive and negative correlations with the i0, respectively, indicating that the i0 is related to the PSD. The mechanism accounting for this result is that pore water pressure forces fine clay particles into the surrounding large pores and converts arranged particles to discretely distributed ones, thereby weakening the connectivity of pores. The seepages in the sliding zones behave differently from that in the sliding mass and sliding bed in response to the permeability. [ABSTRACT FROM AUTHOR]

Numerous engineering case studies suggest that the complexity and oscillatory of landslide displacement types, influenced by diverse deformation patterns, monitoring errors, and varied observation time frames, present challenges for the application of traditional warning criteria in assessing landslide movement in practice. As such, this study attempts to propose a data-dependent early warning criterion through the dimensionless analysis of displacement for two typical landslide displacement patterns: exponential pattern and step-like pattern. The displacement trend in exponential pattern landslides is extracted using the simple moving average (SMA) method, with the onset of acceleration (OOA) determined through the application of the Pettitt test. The Deformation Standardized Anomaly Index (DSAI) is defined to quantitatively evaluate the alert levels corresponding to each monitored displacement data of both patterns. After validating the proposed criterion in ten landslide cases, results indicate that the landslides have a high probability of losing stability when DSAI exceeds four, and the landslide tends to stabilize as DSAI decreases. Compared to displacement speed ratio (DSR), the proposed DSAI is more applicable, aligning with the actual conditions of both landslides with exponential pattern and step-like pattern and exhibiting a relatively low false alarm rate, making it a valuable criterion for landslide early warning. [ABSTRACT FROM AUTHOR]

Timely evaluation of landslide risk based on displacement data is crucial for the effective warning of landslide events. However, the complexity and diversity of displacement curves obtained in the field make the selection of evaluation criteria and thresholds challenging and subjective. Thus, this study proposes a new early warning criterion called the deformation probability index (DPI) to overcome this limitation. By analyzing features of landslide displacement curves obtained from the field, four linetypes were identified, and differences in velocity data between pre-failure and post-failure landslides were summarized from a probabilistic perspective. Subsequently, the DPI is defined to assess the early warning level of a landslide by converting observed velocity values into standardized velocity values using cumulative distribution function standardization. The resulting DPI values are divided into five early warning intervals with three alerting levels to indicate the relative stability of the landslide. Applying the proposed method to 16 landslides from around the world demonstrates the effectiveness of the DPI criterion in evaluating landslide risk, with a high correlation between alerting levels and observed landslide occurrences. The DPI criterion demonstrates notable sensitivity exclusively to displacement velocity and monitoring frequency, exhibiting remarkable generality and accuracy, thus offering a new method for landslide early warning. [ABSTRACT FROM AUTHOR]

Block-flexure toppling failure is frequently encountered in interbedded anti-inclined rock (IAR) slopes, and seriously threatens the construction of hydropower infrastructure. In this study, we first investigated the Lean Reservoir area's geological setting and the Linda landslide's characteristics. Then, uniform design and random design were used to design 110 training datasets and 31 testing datasets, respectively. Afterwards, the toppling response was obtained by using the discrete element code. Finally, support vector regression was used to obtain the influence weights of 21 impact factors. The results show that the influence weight of the slope angle and rock formation dip angle on the toppling deformation among tertiary impact factors is 25.96% and 17.28%, respectively, which are much greater than the other 19 impact factors within the research range. For the primary impact factors, the influence weight is sorted from large to small as slope geometry parameters, joints parameters, and rock mechanics parameters. Joints parameters, especially the geometric parameters, cannot be ignored when evaluating the stability of IAR slopes. Through numerical simulation, it was qualitatively determined that failure surfaces of slopes were controlled by cross joints and that the rocks in the slope toe play a role in preventing slope deformation. [ABSTRACT FROM AUTHOR]

Rainfall with heavy intensity and long duration is a main inducing factor that triggers landslides on anti-inclined rock slopes. Numerous interbedded anti-inclined rock (IAR) slopes in Southwest China frequently experience mountain collapses and landslides under heavy rainfall. Therefore, it is necessary to study the failure mode of IAR slopes during rainfall. In this study, the Linda landslide and Mari landslide, typical IAR landslides in the study area, were used as examples to build a physical model of IAR slopes under continuous rainfall conditions, and a self-made rainfall simulation system was used to mimic natural rainfall conditions. The multi-physics data of displacement, velocity, stress, strain, pore water pressure, and acoustic signals in model deformation and failure under rainfall conditions were obtained using multi-field monitoring technology. In addition, the materials' water content and mechanical parameters at different locations of the model were obtained after the experiment. The analysis results indicate that: in the first and middle periods of rainfall, the slope model experienced a shallow local deformation and a holistic toppling deformation; until the late rain, the failure of the IAR slope caused by rainfall finally presented a typical failure mode of multiple failures and collapses of regressive landslides. According to its multi-field evolution characteristics, the velocity and acoustic signals were highly consistent with the failure characteristics of the model, which could be used as landslide early warning indicators. In addition, it was observed that the shape of the multi-slip surfaces generated by the model was a concave double straight line extending from the slope toe to the slope surface and sharing the failure section at the slope toe. Finally, the deformation and failure process of the model could be speculated to be divided into five stages based on the deformation behavior and multi-field physical data feature: the initial interlayer dislocation stage, the deformation stage, the crack propagation stage, the retrogressive failure stage, and the holistic failure stage. The failure mode and deformation mechanism of IAR slopes under rainfall were revealed in the model test, which could provide appropriate guidance for the analysis and evaluation of similar landslides. Highlights: The failure of the interbedded anti-inclined rock slope model caused by rainfall presented a typical failure mode of multiple failures and collapses of regressive landslides. The velocity and acoustic signals are highly consistent with the failure characteristics of the model, and the shape of the multi-slip surfaces was a concave double straight line extending from the slope toe to the slope surface. According to its multi-field evolution characteristics, the deformation and failure process of the model under rainfall conditions can be divided into five stages. [ABSTRACT FROM AUTHOR]

Analyzing and quantifying the deformation process of landslides is of paramount importance in facilitating landslide early warning. As such, this study is committed to proposing a universal phenomenological model for deformation stages division and early warning of landslides based on the kinematic features. First, five landslide deformation patterns were classified based on the creep theory, and suggestions for stage division of each deformation pattern are presented. Then, the statistical characteristics of landslide velocity were analyzed, and a probability-based deformation stage division method was proposed. Finally, the Comprehensive Standardized Deformation Index (CSDI) model, which includes the calculation of the CSDI M - M (Min-Max normalization) and CSDI M (Mean normalization) was proposed and verified in 24 landslides worldwide. The results show that, except for the oscillating pattern, the CSDI M - M is feasible in the stages division of all deformation patterns with a strong correspondence with the actual state of the landslides. The CSDI M is a reliable landslide warning criterion and threshold determination method, as it is effective in the early warning of imminent landslides with a low false alarm rate. The CSDI model provides new insight into the division of landslide deformation stages and landslide risk assessment. [ABSTRACT FROM AUTHOR]

Block-flexure toppling failures in interbedded anti-inclined rock (IAR) slopes present significant risks to infrastructure and safety. Based on the Linda landslide field investigations, this study conducted a series of physical modelling tests encompassing 15 slope angle and bedding angle combinations. The aim of 15 sets of tests was to explore the deformation and failure mechanisms in IAR slopes. The observations during the deformation and failure stages elucidated how changes in slope and bedding angles influence rock mass deformation and failure characteristics. The findings highlight that slope angle critically impacts IAR slope stability, while bedding angle notably affects deformation depth, failure area, and failure surface configuration. IAR slopes with bedding angles less than 60° tend to exhibit shallow, single-surface failures with the linear failure surface, whereas bedding angles exceeding 70° lead to deep-seated, large-area, multi-surface failures characterised by double-folded failure surfaces. The magnitude of the failure inclination angle increases with slope and bedding angles, ranging from 0 to 35°. Furthermore, deformation and failure in IAR slopes involve the progressive extension of cracks from the slope crest to the interior, eventually reaching the slope toe. Finally, the block-flexure toppling process in IAR slopes can be divided into an incubation deformation stage and an accelerated deformation stage, and based on curve characteristics, different deformation modes of the IAR slopes can be further classified as stepwise cumulative accelerated deformation mode and leap deformation mode. Highlights: Fifteen cases of physical modelling tests covering various slope angles and bedding angles were conducted to replicate the deformation and failure mechanism in interbedded anti-inclined rock (IAR) slopes; Slope angle plays a pivotal role in determining the stability of IAR slopes, whereas bedding angle controls slope deformation depth, failure area, and failure surface characteristics; The IAR landslide is a progressive failure process that extends from the slope crest to the slope toe; The deformation process of IAR slopes can be categorised into the incubation deformation stage and the accelerated deformation stage. Additionally, the deformation mode can be further classified as either stepwise cumulative accelerated or leap deformation modes. [ABSTRACT FROM AUTHOR]

This study investigated a dynamic stability analytical model for bedding rock slope with soil interlayer, considering various rain patterns. The critical rain intensities for anhydrous and water-filled fissure were derived, respectively. Rain coefficients (i.e., concentration and peak position coefficients) were subsequently introduced to investigate rain patterns. The evolving water head under different infiltration scenarios was derived based on Darcy's law and mass conversation law. Additionally, the time-varying water pressure along soil interlayer was obtained based on triangular distribution assumption. An analytical model incorporating time-varying water head was established to predict slope stability based on limit equilibrium method. Numerical simulations further validated the analytical solutions for time-varying water head and pressure. The evolving characteristic of slope stability was analyzed, and a failure threshold was proposed. Results demonstrated that rain patterns and rain coefficients significantly impact on the rainfall threshold corresponding to slope failure. For uniform rain-induced slope failure, rainfall threshold tends to increase as rain intensity decreases. For unimodal rain-induced slope failure, the rainfall threshold is the largest for post-peak rain corresponding to slope failure, followed by the medium-peak rain, and the smallest for forward-peak rain. These results and findings enhance our understanding of the dynamic stability of bedding rock slope with inclined soil interlayer and provide valuable insights into assessing the evolving slope stability under different rain patterns. Highlights: An analytical model of dynamic slope stability coupling time-varying water head was established. Impacts of rain pattern and rainfall intensity on slope stability were systematically investigated. Rainfall threshold for slope failure considering different rain patterns was proposed based on analytical model. 〹 [ABSTRACT FROM AUTHOR]

A simplified geomechanical model was proposed by considering three typical necking-type slopes; this model lays a foundation for the further investigation of the deformation behaviors of such slopes. Three physical models of necking-type slopes were built according to the geomechanical model with slope evolution stages. Finally, preliminary calculations related to the arching effect in the physical model were conducted. Three evolution stages of necking-type slopes, namely, the initial stage, compression stage, and failure stage, were presented based on the formation and disappearance of the arching effect within the slope. The specific parameters of the geomechanical model were given. In the setup of the tilting test, the failure angle of the necking-type slope model was calculated to be approximately 50° with a large lateral resistance coefficient. The proposed geomechanical model and physical models of necking-type slopes provide guidance for the establishment of geomechanical and physical models of landslides at specific sites. [ABSTRACT FROM AUTHOR]

Fluctuating reservoir levels and rainfall behind dams generally cause more severe weathering of the rock forming reservoir bank slopes. Physical weathering tends to weaken the rock and can cause slope instability. Slate sampled from a newly built reservoir area was exposed to cyclic wetting and drying to accelerate physical weathering. A series of tests, including uniaxial and triaxial compressive tests, investigated the weakening of slate. Based on the tests, the degradation of the mechanical properties, failure model, and energy evolution process were studied. Cyclic wetting and drying significantly weaken the specimens. Specifically, the mechanical properties show a monotonic reduction with the number of wetting and drying cycles and the confining stress. The average rebound hardness value of the samples decreases from 30.7 to 20.3 as the number of wetting and drying cycles increases from 0 to 10. The weathering degree and mechanical properties of the specimens can be estimated by the rebound hardness through one linear function and six exponential functions. Five types of fractures and three failure modes were found to describe the failure features of the specimens, and cyclic wetting and drying tended to complicate the failure modes. The evolution process of strain energy represented by five specified types can be divided into three stages according to the variations in the energy parameters, and two newly proposed energy parameters can be used to reflect the energy dissipated by micro-fissure closure and fracture generation. [ABSTRACT FROM AUTHOR]

The evaluation of landslide runout behavior plays a vital role in the risk assessment of landslides. In this study, the runout behavior of a landslide that occurred on December 10, 2019, in the Three Gorges Reservoir region, China, known as the Kamenziwan landslide, is studied with the discrete element method. A field survey is first conducted to study the geological characteristics and failure mechanism of this landslide. The survey results indicate that this landslide is possibly caused by the long-term fluctuation of the reservoir water level. Then, a three-dimensional (3-D) numerical model of this landslide is constructed on the basis of the terrain data obtained before and after the landslide occurrence. The critical input parameters to the numerical model, in terms of the contact micro-parameters among particles, are calibrated with the machine learning method and uniaxial compression tests. To depict the effectiveness of the built numerical model, the simulation results are compared with the video snapshots taken by a local resident. Finally, the runout behavior and the energy transformation of the landslide are analyzed based on the validated numerical model. [ABSTRACT FROM AUTHOR]

Landslides pose a significant risk to human life and property, making landslide susceptibility mapping (LSM) a crucial component of landslide risk assessment. However, spatial correlations among mapping units are often neglected in statistical or machine learning models proposed for LSM. This study proposes KNN-GCN, a deep learning model for slope-unit-based LSM based on a graph convolutional network (GCN) and the K-nearest neighbor (KNN) algorithm. The model was experimentally applied to the Lueyang region and validated through the following steps. Firstly, we collected data for 15 landslide causal factors and from landslide inventories and established a slope unit map (SUM) through slope unit division. Next, we performed a multicollinearity analysis of landslide causal factors and divided the training and test sets at a 7:3 ratio. We then constructed a GCN model based on a slope unit graph (SUG) generated from the SUM using the KNN algorithm. The proposed KNN-GCN model was tuned using a grid search with fivefold cross-validation on the training set, and then trained and validated on training and test sets separately. Finally, the performance of the KNN-GCN model was compared with that of six other models which were categorized into two groups: CG#1 was the traditional KNN, support vector regression (SVC), and automated machine learning (AutoML), and CG#2 included KNN-G, SVC-G and AutoML-G with additional spatial information. Our results demonstrate that the proposed model achieves superior performance (area under the curve [AUC] = 0.8351) and generates the most comprehensible susceptibility map with distinct boundaries between different susceptibility levels. Notably, while the proposed KNN-GCN model displays exceptional performance in slope-unit-based LSM, its implementation requires high-level computing resources, and it is not recommended for small datasets. [ABSTRACT FROM AUTHOR]

Rock avalanches often cause many casualties and considerable property damage due to hypermobility. To better understand the propagation process of rock avalanches, the mechanical energy evolution was investigated based on numerical simulation method and field investigation. Taking the well-known Jiweishan landslide as an example, the kinetic, potential, and dissipation energy elements were extracted from the kinematics parameters, and effective collisions were determined by calculating the local peaks of velocity. The lower velocity regions were observed in the middle portion of the sliding mass. Blocks in these regions were the medium through which the energy was transferred from the back portion of sliding mass to the front portion, acting like the stationary spheres in Newton's Cradle. The entire sliding mass followed the law of conservation of energy, while the energy evolution of the individual blocks varied by their locations. Considering that more collisions occurred in the front portion of the sliding mass, blocks in this area received more energy during propagation, thus they traveled a longer distance. Furthermore, the energy transfer became more obvious with the increase in the volume of sliding mass, causing a larger travel distance for the rock avalanche. [ABSTRACT FROM AUTHOR]

Mountainous regions present severe risks, including damage to transportation infrastructure, due to the occurrence of geohazards such as rock avalanches and other types of landslides. Lessons learned from past damaging events are helpful for risk reduction in mountainous regions. In this paper, to study a rock avalanche impact on a bridge, a numerical modeling framework with the aid of the discrete element method (DEM) is developed; the progressive failure process of an expressway bridge that was damaged by a rock avalanche is reproduced, and the failure mechanisms and dynamic behaviors of the bridge under impact are assessed. The Yaxi Expressway bridge that was destroyed by the September 20, 2020, Yaoheba rock avalanche in southwest China is used as an illustrative example. A field investigation is carried out to identify geological conditions, characteristics, and the kinetic failure pattern of the rock avalanche and the bridge. The stereographic projection analyses indicate that this rock avalanche was initiated as a planar slide. A three-dimensional numerical model of the rock avalanche and bridge is developed based on the terrain data acquired. The critical parameters of the numerical model are calibrated based on results obtained from laboratory tests. Finally, the progressive failure process and failure mechanism of the impacted bridge are analyzed based on the constructed numerical model. The comprehensive analysis framework can be applied to similar events involving geohazard-structure interaction. [ABSTRACT FROM AUTHOR]

To evaluate the performance of deep learning methods on the landslide susceptibility mapping, five different convolutional neural networks (CNN)—AlexNet, Inception-v3, Xception, ResNet-101, and DenseNet-201—were employed to predict the landslide susceptibility along a transmission line. Ten landslide influencing factors were extracted from three databases and considered in the input layers. The landslide (10,481 grids) and non-landslide (10,481 grids) data were randomly subdivided into 70% (14,673 grids) and 30% (6289 grids) to construct the training and validation samples, respectively. The appropriate architecture and training parameters were carefully selected after many attempts until the training and validation accuracy was above 90%. The receiver operating characteristic (ROC) curve, landslide density (LD), and landslide ratio (LR) were determined to estimate the five CNN networks’ prediction accuracy. All CNN networks had high area-under-the-curve (AUC) values when assessing landslide susceptibility, and most landslides occurred in the outputs with predicted high and very high landslide susceptibility (LD > 65% and LR > 2.90). Generally, CNN networks had a higher accuracy than the two traditional methods due to the powerful capability of deep feature extraction. Additionally, the computational time cost in three steps was recorded to investigate the efficiency of five CNN networks, and all CNN networks accomplished the goals within an acceptable time using a commercially available computer (~ 24 h). Comparatively, AlexNet and Xception had better performance than other networks on the landslide susceptibility assessment. [ABSTRACT FROM AUTHOR]

The geological strength index (GSI) plays an important role in the quality evaluation and stability analysis of rock mass. Traditional methods for quantitatively estimating GSI are often subjective, time-consuming, and dangerous. This paper proposed a method for rapid and quantitative GSI estimate using 3D point clouds, which can be generated through non-contact measurement methods such as photogrammetry and Light Detection and Ranging (LiDAR). The overall methodology is as follows: (1) point clouds were acquired using a terrestrial laser scanner; (2) discontinuities were identified through artificial neural networks (ANN) and density-based spatial clustering of applications with noise (DBSCAN); (3) geometric information was extracted for the detected discontinuities; (4) GSI was estimated according to the detection and characterization of discontinuities. The proposed method was used for the Yujiashan road cut to calculate the GSI and the GSI partitioning was performed simultaneously. Three sets of discontinuity were detected in the Yujiashan road cut, and Structure Rating (SR) and Surface Conditions Point Clouds Rating (SCPC) were calculated to be 13.2 and 25, respectively. Correspondingly, the GSI was estimated to be 32, which was consistent with the results of the in-situ evaluation (rating 25–40). Furthermore, the Yujiashan road cut was divided into 17 segments, and the effect of sampling size on the GSI calculation was discussed. The application results show that the GSI of the rock mass can be obtained objectively and efficiently through 3D point clouds, which can be used as a potential alternative to the traditional method for GSI estimation. Article Highlights: A semi-automatic method was developed to determine the rockmass GSI using laser scanning. Discontinuities were detected from the 3D point cloud using a machine-learning algorithm. Four geometric parameters of detected discontinuities were extracted for GSI calculation. The scale effect of GSI calculation was investigated using the proposed partitioning algorithm. GSI estimation obtained from the proposed method matched the traditional manual surveys. [ABSTRACT FROM AUTHOR]

Block-flexure toppling is the most common toppling mode occurring in interbedded anti-inclined slopes. With the development of hydropower projects in Southwest China, numerous deep-seated large-area toppling failures have become revealed along the Yalong River, seriously threatening the construction and operation of hydropower infrastructure. With the Mari landslide as the research focus, a geological field investigation, base friction physical model test, and numerical simulation test were carried out to study the evolutionary process of slope failure. The results demonstrate that the deep-seated toppling in the study area initiated and developed with the river downcutting process, and the landslide was the final result of the toppling deformation. The interbedded structure of layered rock masses, the steep slate foliations in the slope, and the cross joints in the metamorphic quartz sandstone perpendicular to the rock layer are the controlling factors of the toppling deformation. The landslide induced by toppling mainly developed in the highly deformed zone, where multiple failure surfaces developed, and the location and shape of the failure surfaces were controlled by the cross joints in the metamorphic quartz sandstone. The evolutionary process of the deep-seated large-area toppling in the study area mainly underwent four stages: the initial slope formation stage, the rock column bending and cracking stage, the failure surface penetration stage, and the failure stage. [ABSTRACT FROM AUTHOR]

Physical modelling is a useful method to examine how landslides deform and fail at the laboratory scale. Reliable and accurate measurement systems are required for landslide model tests. This study comprehensively evaluates the performance of a multismartphone measurement (MSM) system for landslide model tests. Two slope models with different particle sizes were selected to assess the trueness and precision of the MSM system based on photogrammetry techniques under varied conditions, including different smartphone qualities and phone configurations. Benchmark data from a 3D laser scanner and preprinted markers were employed in the entire-point tests and marker-point tests, respectively. In addition, the MSM system's application test for monitoring landslides is presented. The results show that the system can be applied to achieve both trueness and precision at the millimetre level and submillimetre level in the entire-point tests and marker-point tests, respectively. Best-practice settings of the MSM system based on assessment tests are suggested for landslide slope models. The deformation behaviour of the excavation-induced slope model in the application test was clearly illustrated through the marker-based MSM system. [ABSTRACT FROM AUTHOR]

Multi-field information monitoring is useful to better understand the deformation and failure behaviour of landslides. Therefore, in this study, a physical slope model under excavation was analysed through multi-field monitoring to ascertain the failure mechanism of the slope. During the process of physical model tests, ① area information including 2D surface displacement, 2D surface strain, velocity, 2D surface temperature, and 3D surface deformation, ② line information including deep displacement and deep strain, and ③ point information of the earth pressure of the model were acquired via multi-field monitoring. The pre-failure, failure, and post-failure stages of the slope model are analysed through multi-field monitoring. The results indicate that the relative displacement between the yielding and stationary parts and a triangular shear plane reflect the deformation behaviour of the slope related to the arching effect. The arch ring expands and becomes elongated during the excavation process. The slope failure time can be effectively predicted via the inverse velocity method. Multi-field monitoring can reveal the behaviour of the slope model from different perspectives and offer new insights into the failure mechanism of the slope. [ABSTRACT FROM AUTHOR]

Determination of the shear failure zones of rock joints leads to a better understanding of their shear mechanical behavior. In this paper, we present a low-cost approach to determining the shear damage zones of rock joints after direct shear tests. Experimental direct shear tests were performed on one pair of man-made and six pairs of natural samples to collect digital high-resolution images using a digital camera before and after the tests. For each sample, the images acquired before and after tests were brought into registration, and a disparity image was generated. Then, a binary image with the shear damage zones marked as true was generated, and the noise was filtered out with postprocessing methods. The traditional RISS and MN methods were employed to examine the performance of the proposed approach by comparing their accuracy, time consumption, adaptability and robustness. The results demonstrated that the proposed approach IR is comparable to the MN approach in terms of accuracy but saves 82.92% of the time, and has better accuracy, adaptability and robustness compared to the RISS approach. Highlights: A new approach based on the image registration technique was proposed to determine the shear damage zones of rock joints. The proposed approach showed better efficiency, accuracy, and robustness than the two traditional methods in the comparative study. The proposed approach has been integrated into the open-sourced SDZM toolbox, which facilitates engineering applications and comparative studies. [ABSTRACT FROM AUTHOR]

Wetting and drying cycles (WDCs) have a significant impact on the shear behavior of discontinuities with different joint wall materials (DDJMs). This influence is crucial for the reasonable evaluation of the long-term stability of soft and hard interbedded rock slopes under water level fluctuations. As the surface topographies of natural discontinuities collected from the field vary, conducting comparative experiments on natural discontinuity specimens with identical surface topographies is challenging. To solve this problem, a 3D surface topography reconstruction technique was employed to obtain DDJM specimens with three types of surface topographies collected from a typical sliding-prone stratum in the Three Gorges Reservoir area, China. A series of experiments, including computed tomography scanning, 3D laser scanning, and direct shear tests, were conducted to investigate the influence of WDCs on the micro- and macroproperties of joint walls, surface topographies, and shear behavior of DDJMs. The experimental results showed that repeated WDC treatments caused the degradation of the microstructures and macroscopic physical properties of the studied joint walls, and the more severely weakened joint wall played a predominant role in reducing the shear strength of DDJMs. The influence of WDCs on the surface topographies of DDJMs was negligible in this study; changes in the shear behavior of DDJMs were closely associated with the weakening of joint walls induced by WDCs; and the impact degree of joint wall weakening on the deterioration of the shear behavior of DDJMs was interactively influenced by and positively correlated with both the joint roughness coefficient and normal stress. These results will contribute to a better understanding of the evolution of the stability of soft and hard interbedded rock slopes induced by water level fluctuations in the Three Gorges Reservoir area and other reservoir regions. [ABSTRACT FROM AUTHOR]

Rock discontinuities fundamentally impact the mechanical and hydraulic behaviors of a rock mass, and thus it is a critically important task to characterize the geometrical parameters of these rock discontinuities. To measure the discontinuity orientation more accurately and efficiently, two well-known point clouds were taken as cases (a cube and a road cut), and an artificial neural network (ANN)-an machine learning algorithm-was established to identify discontinuities from point clouds through learning a small number of training samples, which had been manually selected from the raw point clouds. Four attributes associated with geometrical features of point clouds were specified as input parameters, namely, point XYZ-coordinates, point normal, point curvature, and point density. Two main groups-discontinuity and non-discontinuity-were produced in the output layer, and the number of the discontinuity groups greatly depended on the sets of discontinuities in the real situation. Using principal component analysis (PCA) and density-based spatial clustering of applications with noise (DBSCAN), single discontinuities were extracted from the group discontinuities which were obtained using ANN, and the corresponding orientations were calculated. The results obtained with the proposed method in this study matched the field surveys and results calculated by a modified region-growing algorithm. The computational efficiency was significantly enhanced using the proposed method, only taking several seconds to process a huge data. More importantly, the accuracy of discontinuity detection was greatly improved by specifying the noise data as the non-discontinuity groups during training samples selection in ANN. The ANN approach does not require the engineers have a strong professional background in computer programming, which simplified the detection and characterization process of rock discontinuity. Furthermore, an APP-named DisDetANN-was developed to implement the rock discontinuity detection based on the proposed ANN model, and the full code of the DisDetANN has been freely shared on GitHub. Highlights: An artificial neural network was created by machine learning to detect group discontinuities from point clouds. Point coordinate, normal, curvature, and density were considered in input layers of the artificial neural network. A clustering algorithm was employed to subdivide group discontinuities into single discontinuities. Both efficiency and accuracy of the discontinuity detection was improved by the proposed approach. An APP and full codes of the proposed method were freely made available to the engineering community. [ABSTRACT FROM AUTHOR]

Recent research has paid little attention to weakening of discontinuities with different joint wall material (DDJM) induced by wetting and drying cycles. Experimental investigations were conducted to examine the effects of wetting–drying cycles on weakening of three typical types of natural planar DDJM collected from a sliding-prone formation in the Three Gorges reservoir region of China. Deterioration of the corresponding four types of discontinuities with identical joint wall material (DIJM) were also been studied for comparison. The weakening laws of DDJM were revealed from macro- and microperspectives by testing on mineral compositions, microstructures and water absorption rate of joint walls as well as shear strength of discontinuities. The results indicate that the repeated wetting–drying cycles lead to obvious changes in microstructure and increases of macro water absorption rate for the studied joint walls with the changing degree closely related to their clay mineral contents; the peak shear strengths of DDJM gradually decreases with the wetting and drying cycles for a certain normal stress with the weakening rate between that of the DIJM with two joint wall materials of the DDJM, while closer to that of DIJM with the weak half materials of the DDJM; significant correlations have been found between the variation coefficient of rock microstructure of joint walls and the macrodeterioration coefficient of DDJM; the changing rates of multi-scale parameters before and after six wetting–drying cycles vary obviously, indicating the weakening is slowed down after six wetting–drying cycles for the studied discontinuities. Highlights: Effects of wetting-drying cycles on weakening discontinuities with different joint wall material were studied. Deterioration of the discontinuities with identical joint wall material were also been investigated for comparison. The weakening laws were revealed from macro and micro perspectives. Significant correlations have been found between the variation coefficient of microstructure of joint walls and the macro deterioration coefficient. The results could provide a basis to better understand the stability evolution of rock slopes with soft and hard rock interbedded. [ABSTRACT FROM AUTHOR]

In a recent discussion by Rashida and Singh (Rock Mech Rock Eng, 2023) regarding our paper (Tan et al. Rock Mech Rock Eng 56:7599–7617, 2023) investigating the thermal effects on non-Darcian flow within heated rock fractures, several points were raised. They argued that the temperatures and durations in our experiments may not induce thermal expansion in fractured rock due to low thermal expansion coefficients. Additionally, they highlighted potential water phase changes during heating and recommended precautions, such as pre-heated water and confining pressure, which were not implemented in our experimental setup. In response, we provide further clarification. Firstly, concerning heating time, our focus is on temperature-induced alterations in fracture geometry during the heating process rather than after temperature stabilization. Secondly, our detailed analysis of the coefficient of thermal expansion for granite specimens confirms that thermal expansion does lead to fracture closure within the imposed temperature range, affecting flow characteristics. Lastly, considering the aims of the study, precautions against water phase changes were not implemented, but the potential water phase changes had a negligible effect on our results due to the low temperatures employed and the short water residence time in the fracture. In summary, we maintain the methodological soundness of our experimental setup and the appropriateness of the obtained results in alignment with the objectives of our study. The discussion and this response are helpful to further clarify our experimental results and eliminate potential ambiguities. Highlights: Temperature-induced alterations in fracture geometry during the heating process are noteworthy. Even minor fracture closure resulting from thermal expansion markedly influences flow behaviors. Low test temperatures render potential water phase changes inconsequential to our findings. [ABSTRACT FROM AUTHOR]

To recreate high-quality three-dimensional (3D) models of rock joints for quantitative roughness characterization more economically, portably, and quickly, it is necessary to assess the feasibility and accuracy of 3D reconstruction of rock joints using SfM & MVS photogrammetry with a camera in animation mode. A low-cost approach for 3D reconstruction by shooting videos was conducted to obtain point clouds of rock joints. Besides, to evaluate the accuracy of photogrammetry, high-resolution models of the same cases were collected using 3D laser scanning technology to compare the difference between models reconstructed by these two technologies, and the difference was calculated by one algorithm—Iterative Closest Point (ICP). Comparative analyses demonstrated that the models reconstructed by photogrammetry and the output of the laser scanning were in good agreement. Furthermore, these two different sources of point cloud data were used to realize quantitative characterization of the structural surface roughness via calculating JRC and θ*max/(C+1) values, and the related assessment results also showed good agreement. To ensure the precision of 3D models of rock joints, it is strongly advised that the 3D reconstruction be carried out using a camera that has been properly calibrated. Moreover, there is a significant difference in calibrated camera parameters between animation mode and image mode, which cannot be ignored in 3D reconstruction. Comparative analyses demonstrated that SfM & MVS photogrammetry with a DSLR camera in animation mode can generate a point cloud of multi-scale rock joints with acceptable accuracies for roughness estimation, proving the effectiveness of a low-cost approach for 3D reconstruction by shooting videos. [ABSTRACT FROM AUTHOR]

Affected by periodic reservoir water level fluctuations and seasonal rainfall, most reservoir landslides show a step-like pattern of dynamic switching between accelerated and decelerated deformation states. This paper proposes a novel adaptive interval prediction method for accurate prediction of such step-like landslide displacement. In this method, future deformation state (acceleration or deceleration) of landslide is identified by random forest model. Based on the dual-output least squares support vector machine (DO-LSSVM) model, two independent interval predictors are established to adaptively predict landslide displacement in the accelerated deformation state and the decelerated deformation state, respectively. Landslide triggering factors, including rainfall and reservoir water level fluctuations, are adopted as inputs to train the random forest model and DO-LSSVM predictors. To depict the effectiveness of the proposed method, a typical step-like landslide, the Baishuihe landslide in the Three Gorges Reservoir region of China, is taken as a case study. The prediction performance of landslide displacement is evaluated by the prediction interval coverage probability (PICP), the normalized mean prediction interval width (NMPIW), and the modified coverage width-based criterion (CWC). Compared to existing interval prediction methods, the predicted displacement intervals by the proposed method have smaller NMPIW and relatively high PICP. Moreover, the mean and standard deviation values of CWC are much smaller than those obtained from existing methods, showing improved prediction accuracy and reliability. Results of this study confirm good performance of the proposed method in interval predictions of step-like landslide displacement. The prediction results could facilitate early warning of landslide disasters. [ABSTRACT FROM AUTHOR]

Water flow through fractured rocks in high-temperature settings is relevant to many subsurface geo-energy engineering projects including geothermal energy extraction, hydrocarbon production, and nuclear waste disposal. However, existing studies have either focused only on flow process within fractures at room temperature or on the thermal effect on fracture permeability. As a result, how and to what extent rock temperature affects the flow behavior in rock fractures remains poorly understood. Herein, we report an experimental study of the heated flow behavior in rock fractures at steady-state elevated temperatures under sequentially increasing flow rates. We find that rock temperature greatly affects the flow behavior within the fracture via two mechanisms: water properties variations and fracture closure. The former reduces the flow resistance, while the latter both reduces the effective hydraulic aperture and enhances the inertial effect. The more heterogeneous the initial aperture distribution is, the more significant the effect of the temperature-induced fracture closure on the flow nonlinearity will be. By combining the Forchheimer equation with water temperature measured at the fracture inlet and outlet, we quantitatively evaluated the effect of these two mechanisms on rock fracture permeability and flow nonlinearity. Finally, we develop an empirical expression for the critical hydraulic gradient of the fracture at different temperatures based on the nonlinear flow model for room-temperature fractures to quantify the emergence of non-Darcian flow. The findings from the present study are expected to shed insight into the prediction and evaluation of fluid flow within deep subsurface fractured reservoirs. Highlights: Physical mechanism of thermal effect on water flow in heated rock fractures is revealed experimentally. Thermal effect promotes non-Darcian flow by changing water properties and inducing fracture closure. A parametric model to predict onset of non-Darcian flow in heated fractures is proposed. [ABSTRACT FROM AUTHOR]

Transumbilical single-port laparoscopy is widely used in gynecological surgery. However, it is rarely used in the treatment of deep infiltrating endometriosis due to its own shortcomings and the complex condition of deep infiltrating endometriosis. The study aims to introduce a transumbilical single-port laparoscopic surgery based on retroperitoneal pelvic spaces anatomy, which can complete the operation of deep infiltrating endometriosis more easily. A retrospective analysis of 63 patients with deep infiltrating endometriosis treated by transumbilical single-port laparoscopy using this method was conducted. The operation duration was 120.00 (85.00 ± 170.00) (35–405) min, the estimated blood loss was 68.41 ± 39.35 ml, the postoperative hospital stay was 5.00 (4.00–6.00) days, and the incidence of postoperative complications was 4.76% (3/63). 1 patient was found to have intestinal injury during operation, 1 patient had ureteral injury after operation, and 1 patient had postoperative pelvic infection, with a recurrence rate of 9.52%. The postoperative scar score was 3.00 (3.00–4.00) and the postoperative satisfaction score was 9.00 (8.00–10.00). In summary, this study demonstrates the feasibility of transumbilical single-port laparoscopic surgery for deep infiltrating endometriosis based on retroperitoneal pelvic spaces anatomy. Hysterectomy, adenomyosis resection, etc. are also feasible with this method, boasting more obvious advantages. This method can make transumbilical single-port laparoscopy more widely used in deep infiltrating endometriosis. [ABSTRACT FROM AUTHOR]

Landslides frequently disrupt roadway networks in mountainous regions worldwide. Because of the relatively long roadway extension and low roadway density in mountainous regions, the occurrence of a landslide hazard along a local road segment will cause traffic paralysis on the individual roadway and will further impact regional roadway network accessibility. This paper establishes an integrative risk assessment framework based on risk theory and complex network theory to combine the results of landslide susceptibility mapping along roadways and impact assessment on the roadway network. Through an analysis of the relationship between various geo-environmental conditioning factors and historical landslides along roadways, the support vector machine (SVM) model is used to assess landslide susceptibility across the regional roadway network. Both topological connectivity of the entire roadway network and transport accessibility between local residents are considered in the impact assessment on roadway networks. To illustrate the effectiveness of the proposed risk assessment framework, a case study of the roadway network in Fengjie County, China, which is prone to landslide occurrence, is conducted. The resulting landslide risk heatmap of Fengjie County's roadway network is generated using ArcGIS software. The most critical road segments are identified as being highly susceptible to landslides, and if they are disrupted, the entire roadway network will suffer significant performance loss. The results can support adaptive strategies for landslide mitigation, preparedness, and emergency response services, as well as improve roadway plans to reduce exposure and associated consequences by adding new links to the existing roadway network. [ABSTRACT FROM AUTHOR]

For landslide displacement, interval predictions are generally more realistic and reliable compared with traditional point predictions. This paper presents a new interval prediction method for landslide displacement integrating dual-output least squares support vector machine (DO-LSSVM) and particle swarm optimization (PSO) algorithms. In this new method, the PSO algorithm is employed to optimize coefficients of the least squares support vector machine (LSSVM) model for obtaining point prediction results, and the interval prediction of the landslide displacement is made based on the dual-outputs obtained from the DO-LSSVM model. To assess the rationality of the predictions, three performance evaluation indicators, including the prediction interval coverage probability (PICP), normalized mean prediction interval width (NMPIW), and coverage width-based criterion (CWC), are established. Case studies of the Tanjiahe landslide and the Baishuihe landslide in the Three Gorges Reservoir region are then used to demonstrate the effectiveness of the proposed method in predicting the landslide displacement interval. The case study results demonstrate that this new method has the best overall performance compared with other existing methods, and this new method can provide accurate and reliable results for the medium- to long-term interval prediction of landslide displacement. [ABSTRACT FROM AUTHOR]

The stability of gentle slopes is rarely accessed in existing studies, which are at risk of below-toe failure in soils with low shear strength. The inherent spatial variability of soil shear strength poses a huge complication to the probabilistic stability evaluation of large-scale three-dimensional gentle slopes, which usually forces a trade-off between precision and efficiency. In view of this, a semi-analytical method is developed in the framework of discretized limit analysis, which gives a unified mathematical representation of toe failure and below-toe failure of slopes. The proposed method inherits the high efficiency of analytical methods and has the ability to integrate spatially variable shear strengths into the slope mechanical model. The model validation is conducted by comparisons with a widely recognized analytical method developed for uniform soils. The random fields are introduced to achieve a relatively accurate characterization of soil shear strength, and the Monte Carlo simulation is employed to obtain a sufficient number of factors of safety of slopes for the subsequent statistical analyses. In the parametric study, spatial variability-related parameters, including the coefficient of variation of soil cohesion covc or internal friction angle covφ, the autocorrelation lengths along vertical and horizontal directions ξ and k, the cross-correlation coefficient ρcφ, are varied systematically to reveal their influences on the slope stability from a statistical perspective. It is found that the ranking of the impact on the probabilistic stability of a gentle slope is given as: covc or covφ > ξ > k > ρcφ. Finally, the failure probabilities of the gentle slope are computed considering the variations of key parameters, which may have implications for practical slope designs. [ABSTRACT FROM AUTHOR]