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Affected by discontinuities, the properties of rock masses are characterized by strong scale dependency and anisotropy. Rock mass samples taken at any scale smaller than the representative elementary volume (REV) size could lead to an incorrect characterization and property upscaling. To better understand the sampling problem, numerical tests based on an outcrop-data-based discrete fracture network (DFN) model were conducted, trying to determine the REV size and its anisotropy. The model was validated and subsequently sampled to produce 455 rectangular samples with a width ranging from 0.05 to 21 m and a constant height-to-width ratio of 2. The samples were introduced into a 3D particle flow code model to create synthetic rock mass (SRM) samples. Numerical uniaxial compressive tests at different loading directions were performed to study the scale dependency and anisotropy of mechanical parameters. The results show that the mechanical REV sizes in different directions differ, changing between 7 and 19 m. The mechanical properties of rock mass samples in a REV size exhibit strong anisotropy, with the values of uniaxial compressive strength (UCS) and elastic modulus (E) varying from 5.6 to 10.3 MPa and 3.9 to 8.0 GPa, respectively. The simulated values of UCS were validated based on GSI and the Hoek–Brown failure criterion. The geometrical REV size based on the volumetric fracture intensity was calculated to be 7 m, equal to the minimum of the mechanical REV size; this suggests that the geometrical REV defines the lower bound of the REV size. [ABSTRACT FROM AUTHOR]

The geological conditions in the upper reaches of the Jinsha River on the southeastern margin of the Qinghai–Tibet Plateau are extremely complex. Many paleo-landslides and lacustrine sediments were formed in this area under the synergistic effect of tectonic activity and climate. The study of old deposits is helpful for understanding paleoenvironment, paleoclimate, and river geomorphology evolution. This study addressed the Wangdalong landslide damming event as the object and implemented a detailed on-site investigation. The geological characteristics of the landslide body, sliding surface, source area, and lacustrine deposits are determined. The 3D topographic restoration and the SFLOW software were used to simulate landslide dynamics, and the geomorphological evolution sequence of the Wangdalong landslide is proposed. Based on the landslide dating, the mineral analysis of lacustrine sediments, and the numerical simulation, the landslide was determined to be the product of the synergistic effect of climate and structure, and an earthquake or a heavy rainfall was the most probable triggering factor of the landslide. This study established a systematic analysis of the landslide damming event, which has reference significance for similar research. [ABSTRACT FROM AUTHOR]

Extensive models used in debris flow runout simulations are two-dimensional with many limitations. Considering these limitations, a new three-dimensional computational fluid dynamics (CFD) code based on the finite difference method (FDM) was introduced to simulate debris flow runouts. The unique fractional area-volume obstacle representation (FAVOR), true-volume of fluid (Tru-VOF), and the renormalized group (RNG) model in the 3D CFD code were used to tackle mesh processing, free surface tracking, and turbulence, respectively. The RNG model has great performance in describing low-intensity flows and flows with strong shear regions. In addition, the 3D CFD modeling considers the vertical mobility of debris flow, which offers higher simulation accuracy compared to 2D approaches. Through simulating a case and a mesh size study, the accuracy of the model was validated and the optimal mesh size of Xiaojia Gully debris flow model was obtained, respectively. The affected areas, runout distances, deposition depths, and velocities of potential debris flows in Xiaojia Gully were acquired by adopting the present model. The simulation results show that debris flows with return periods of 50 years, 100 years, and 200 years will threaten the lives and safety of residents and their property in Xiaojia Gully. A sensitivity analysis was used to evaluate the influences of rheological parameters on this model, further verifying the rationality of the selected parameters. In general, the present model can scientifically and accurately simulate debris flows on irregular terrain and can be employed for similar risk management and engineering designs. [ABSTRACT FROM AUTHOR]

The Yarlung Tsangpo river basin in the southeastern part of the Tibetan Plateau contains many maritime glaciers, where the massive debris flow formation caused by the glacial degradation leads to the issue of regional warming. It often forms a continuous geological disaster chain. In this study, the topographical features and debris flow deposits in the Peilong valley were investigated in November 2018. It was found that the geological conditions in the valley are complex and numerous unstable sources are existed. Field investigations, laboratory tests, and a geomorphological analysis were conducted to obtain relevant data for the valley. And comparison of Landsat images indicates that the rate of change of the glacial area is 51.62% from winter to summer. Discrete element software (PFC3D) is used to simulate glacial landslides on the left bank. The simulation results show that a dam with a height of 141 m was formed, creating a lake. The debris flow after the dam break is simulated for different peak discharge values and durations based on a free surface shallow flow (SFLOW) model, with a final accumulation depth of 21.6 m. The comprehensive results of numerical simulation show that the chain of geological disasters induced by glacial landslide poses a huge threat to residents and roads near Peilong valley. Therefore, it is recommended to monitor the glacier activities in real time to enable forecasting of debris flows and reduce the property losses and prevent loss of life. [ABSTRACT FROM AUTHOR]

This paper reports that the geometrical representative elementary volume (REV) of fractured rock masses is anisotropic, and the geometric shape of an REV is of a complex, irregular geometry. A 3D fracture network model of 98 m × 80 m × 80 m was created using the field information collected from tunnel PD254 at the Maji dam site in China. One hundred random cubes of various sizes with the same sampling direction were selected to assess the volumetric fracture intensity (P32). Changing the sampling direction along the dip direction and dip angle at a 10° increment, the REV size of each sampling direction was inferred using the Wald–Wolfowitz runs test and P32 values. The REV sizes of entire rock masses and each fracture set were calculated, and the results indicated that the REV sizes varied with the different sampling directions. The anisotropy ratio and standard deviation were employed to quantify the degree of the anisotropy of REVs. Based on the results, the anisotropy of REVs for fracture set 2 is the most significant, and that of fracture set 3 is the worst. The relationship between the anisotropy of REVs and fracture sets was discussed; one fracture set, two fracture sets, and three fracture sets were selected randomly from these simulated rock masses to evaluate the characteristics of the anisotropy of REVs. The mean value and the anisotropy of REVs increased with the number of fracture sets. The maximum REV size with a sampling direction was selected as an REV for each fracture set. Four cubes with different directions and different sizes were assembled into an irregular geometry. This geometry can represent the geometric shape of the REV of entire rock masses. [ABSTRACT FROM AUTHOR]

The mapping unit is the smallest indivisible unit of landslide susceptibility mapping. The mapping unit considerably affects the effect of landslide susceptibility mapping. Among many mapping units, the slope unit is one of the most suitable units for landslide susceptibility mapping. The methods available for classifying slope unit are not uniform, and the dividing effects differ. In this study, the hydrological method and curvature watershed method were used to classify the slope units in the study area on the upper Jinsha River, south-western China. Lithology, slope angle, slope aspect, normalized difference vegetation index (NDVI), land cover, rainfall, curvature, distance-to-river, distance-to-fault, were selected as the landslide conditioning factors. Support vector machine were applied to the landslide susceptibility modeling. Statistical indexes, 5-flod cross validation, Kappa coefficient, and AUC values were introduced to validate the prediction accuracy of the landslide susceptibility model. By comparing the shape characteristics of slope units classified by the two methods, we found that the slope unit classified based on curvature watershed method has a uniform size, shape between circle and equilateral triangle, and small internal terrain variation. As for the mean prediction accuracy, Kappa coefficient and AUC values of landslide susceptibility model, in the training stage, were 81.62%, 63.23%, and 89.72%, respectively, for the hydrological method, while 84.26%, 68.51%, and 90.80%, respectively, for the curvature watershed method; in the testing stage, were 80.70%, 61.40%, and 88.08%, respectively, for the hydrological method, while 83.24%, 66.49%, and 88.96%, respectively, for the curvature watershed method, which means that the curvature watershed method is more effective to produce landslide susceptibility map of the study area. [ABSTRACT FROM AUTHOR]

The objective of this study was to produce a landslide susceptibility map along the rapidly uplifting section of the upper Jinsha River. Firstly, a total of 40 landslides were identified in the study area from the interpretation of remote sensing (RS) and field survey data. Following landslide identification, ten variables including slope angle, slope aspect, curvature, land use, normalised difference vegetation index (NDVI), rainfall, lithology, distance to river, distance to fault, and Strahler's integral value were selected as the influencing factors in landslide susceptibility mapping. All of the influencing factors were extracted by the slope unit. The Strahler's integral value was used to represent the relationship between the rate of uplift and rate of denudation in each slope unit. Furthermore, three methods, including logistic regression, a support vector machine, and an artificial neural network, were applied to landslide susceptibility modelling. Five-fold cross validation, a statistical analysis method, and the area under the receiver operating characteristic curve (AUC) were used to compare the evaluation results of the three models. Finally, the variance-based method was used to find the key factors associated with landslides in the study area. The results show that the mean prediction accuracies of the logistic regression model, artificial neural network model, and support vector machine model were 80.47%, 87.30%, and 83.94% in the training stage, respectively, and 81.08%, 82.16%, and 83.51% in the validating stage, respectively. The mean AUCs of the three models were 88.16%, 93.96%, and 89.68% in the training stage, respectively, and 87.68%, 92.60%, and 89.88% in the validating stage, respectively. These results show that the artificial neural network model is the best model for evaluating landslide susceptibility in this study. The landslide susceptibility map produced by the artificial neural network model was divided into five classes, including very low, low, moderate, high, and very high, and the percentages of the areas of the five susceptibility classes were 17.23%, 28.32%, 22.73%, 16.73%, and 15.00%, respectively. Furthermore, the distance to river, slope aspect, lithology, and distance to fault are the most important influencing factors for landslide susceptibility mapping in the study area. Consequently, this study will be a useful guide for landslide prevention, mitigation, and future land planning in the study area. [ABSTRACT FROM AUTHOR]

Debris flows could cause large amounts of damage in the alluvial fan where many houses and transport infrastructures exist. Thus, runout analysis of potential debris flows in these areas is necessary for protecting human lives, houses and infrastructure. Currently, the numerical simulation method is an effective way to intuitively display the runout of a potential debris flow. In this paper, a well-balanced finite volume scheme over complex topography was employed for studying the runout of a potential debris flow in the Dongwopu gully. Through model testing and validation, the numerical model had a good potential to adapt to the complex topography and could reasonably simulate the runout of debris flow events. Then, runout distance and inundation path, as well as flow depths and flow velocities, for the potential debris flow were obtained using the model. The simulation results appropriately reflected the runout characteristics. [ABSTRACT FROM AUTHOR]