Influence of parameters on the development of landslides in the Estrada de Ferro Vitória-Minas slopes

Landslides have been the object of extensive studies in the world, not only for their importance as active agents of modifications of relief forms, but also because can damages and losses to people and exposed structures, affecting various kinds of enterprises. This study had as objective the determination of influencing parameters on the development of landslides in the slopes aside of Estrada de Ferro Vitória-Minas (EFVM). EFVM is located in the southeastern region in Brazil and is an important railroad for the transportation of iron ore to the steel mills and for exportation, as well as for passenger transportation. The database used herein was collected from field work in EFVM, together with image processing and data in laboratory tests. The parameters selected to be evaluated were Atterberg limits, cohesion, friction angle, permeability and classification of soil in the slopes. Estimates were done on the volumes and areas of landslides that have already occurred in the slopes. Among the studied parameters, the results obtained for the Atteberg limits and soil cohesion were the most relevantly correlated with the field results, which is in accordance with other studies from literature. It is concluded that Atterberg limits are directly related to soil ruptures, and soil cohesion contributes to soil stabilization in slopes.

Effect of Nano-Carbon on Geotechnics Features of Gypseous Soils

A Collapsing soil usually causes problems, this kind of soil has a substantial strength while it is dry, but it loses its strength while inundating and be subjected to extreme settlement. It is impossible to predict in advance the reactions of soils subjected to inundating (i.e. landslide otherwise an important soil settlement). The reduction in irreversible volumes of collapsing soil happens quickly as well as suddenly, once the reduction starts there will be no measurement to be executed which could halt such difficulty. As a result of the soak and leach that are resulting from the dissolute and clean out of gypsum, the collapsing potentials increase during the time. There are many studies in this field that indicated the possibility of modifying this soil by using nanomaterials. In this study, the nanomaterial used is nanocarbon and the soil is gypseous soil taken from Al-Najaf city in Iraq. This work studies the effect of adding nanomaterials on the gypseous soil and investigates its behavior before and after adding nanomaterial. The results showed that adding the nanocarbon affects the collapse potential which decreases by a percent meanwhile the soil cohesion decreased partly when the nanocarbon is added with 0.8% but the friction angle increased about 19%. The best proportion of using of the nanocarbon ranges between 0.8-1.2%.

Estimation of Soil Shear Strength Indicators Using Soil Physical Properties of Paddy Soils in the Plastic State

Saturated soil shear strength is a primary factor that reflects the driving resistance of agricultural machinery in paddy soils. The determination of soil shear strength indicators, such as cohesion and internal frictional angle, is crucial to improve the walking efficiency of agricultural machinery in paddy soils. However, the measurement of these indicators is often costly and time-consuming. Soil moisture content, density, and clay content are crucial factors that affect the cohesion and internal friction angle, while very limited studies have been performed to assess the interactive effects of the three factors on soil shear characteristics, especially on paddy soils. In this study, eight soil samples were taken from eight paddy fields in Southeastern China, and the central composition rotatable design was used to classify the soil samples into five levels based on different clay content (X1), moisture content (X2), and density (X3). The direct shear tests were carried out indoors on the remolded paddy soil using a self-made shear characteristic measuring device. Then, both individual and interactive effects of X1, X2, and X3 on soil cohesion and internal friction angles on paddy soils were systematically investigated and analyzed using the regression analysis method in the data processing software Design-Expert. Our results indicated that the effects of the three environmental factors on soil cohesion were in the order of X1 > X2 > X3, while the order was X2 > X3 > X1 for the impact on internal friction angle. The interactive effects were in the order of X1X2 > X1X3 > X2X3 for cohesion and X1X2 > X2X3 > X1X3 for internal friction angle. Two prediction models were successfully established to quantify the soil cohesion and internal friction angle as affected by soil physical properties, and the coefficient of determination (R2) was 0.91 and 0.89 for the two equations, respectively. The model validations using new soil samples suggested that the models were capable of predicting the shear characteristic parameters under different physical parameters effectively, with errors between predicted and measured soil shear strength indicators within 15% and relative root mean square error less than 11%.

Comparison of the Disturbance of Soil Physical Quality Indices Between Different Underground Mining Stages in Semi-Arid Regions of Western China

Based on soil sampling, lab experiment and support resistance monitoring, the disturbance of soil physical quality indices between different underground mining stages of No 52303 working face was studied in semi-arid region of western China. Soil sampling was conducted in same locations before and after mining in 2014. This study proved that soil water content, soil cohesion and soil porosity were greatly decreased, while bulk density and dry density were increased by coal mining. In comparison, coal mining had slight effect on organic matter, internal fraction angle, and D1 and D2 percent. Underground pressure monitoring showed that P1 during stage 2 was significantly greater than that during stage 1, indicating the large difference of pressure characteristics in tail areas of working face between two stages. Both soil water content and soil cohesion were decreased during two stages in two sites. Soil cohesion was strongly correlated to soil water content, and D1 and D2 percent in 2013 and 2014. Coal mining subsidence increased the cumulative probability to reach the same value of soil water content and soil cohesion. The cover depth produced different elastic and plastic zone widths between sites by theoretical model calculation, consistent with the support resistances in tail areas of working face. Higher pressure might cause a more serious destructive rock-soil body and a larger groundwater level decrease. The dryer and more serious erosive soil column induced by coal mining is a non negligible matter for the semi-arid region.

Introducing SlideforMap; a probabilistic finite slope approach for modelling shallow landslide probability in forested situations

Worldwide, shallow landslides repeatedly pose a risk to infrastructure and residential areas. To analyse and predict the risk posed by shallow landslides, a wide range of scientific methods and tools to model shallow landslide probability exist for both local and regional scale However, most of these tools do not take the protective effect of vegetation into account. Therefore, we developed SlideforMap (SfM), which is a probabilistic model that allows for a regional assessment of shallow landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SfM uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated from observed events. For each randomly generated landslide, SfM calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters, i.e. angle of internal friction, soil cohesion and soil depth, are assigned to the generated landslides from normal distributions based on mean and standard deviation values representative for the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed based on root proximity and root strength derived from single tree detection data. Ultimately, the fraction of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. Inputs for the model are a digital elevation model, a topographic wetness index and a file containing positions and dimensions of trees. We performed a calibration of SfM for three test areas in Switzerland with a reliable landslide inventory, by randomly generating 1000 combinations of model parameters and then maximising the Area Under the Curve (AUC) of the receiver operation curve (ROC). These test areas are located in mountainous areas ranging from 0.5–7.5 km2, with varying mean slope gradients (18–28°). The density of inventoried historical landslides varied from 5–59 slides/km2. AUC values between 0.67 and 0.92 indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modeling of shallow landslide probability are the soil depth, soil cohesion and the root reinforcement. Further, the use of single tree detection in the computation of root reinforcement significantly improved model accuracy compared to the assumption of a single constant value of root reinforcement within a forest stand. In conclusion, our study showed that the approach used in SfM can reproduce observed shallow landslide occurrence at a catchment scale.

Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

Can we use the Curve Number approach to predict gully head occurrence at the continental scale of Africa?

<p>Gully erosion is an important land degradation process, threatening soil and water resources worldwide. However, in contrast to sheet and rill erosion, our ability to simulate and predict gully erosion remains limited, especially at the continental scale. Nevertheless, such models are essential for the development of suitable land management strategies, but also to better quantify the role of gully erosion in continental sediment budgets. We aim to bridge this gap by developing a first spatially explicit and process-oriented model that simulates average gully erosion rates at the continental scale of Africa.</p><p>We are developing a model that predicts the likelihood of gully head occurrence by means of the Curve Number (CN) method. This model will allow to simulate the spatial patterns of gully density at high resolution (30m) based on the physical principles that control the gully erosion process by using GIS and spatial data sources that are available at the continental scale. To calibrate and validate this model, we make use of an extensive database of 44 000 gully heads mapped over 1680 sites that are randomly distributed across Africa. The exact location of all gully heads was manually mapped by trained experts, using high resolution optical imagery available in Google Earth. This allows to extract very detailed information at the level of the gully head, such as the local slope and the area draining to the gully.</p><p>Based on an explorative analysis on a subset of this dataset we found that the CN method does not directly allow to make reliable predictions on gully head occurrence within a pixel. Although land use and land cover seem to play an important role (with gully heads being clearly located in erosion-prone land use classes), the hydrological soil groups (HSGs) based on soil texture do not provide a clear relation between soils with high runoff risk and gully occurrence. A potential cause for this is likely that compensating soil effects occur: i.e. HSGs that produce low runoff volumes may be characterized by a lower soil cohesion, making them nonetheless prone to gullying. This may then cause the combination of HSG and land use to be an insignificant predictor of gully occurrence. Also uncertainties on the input data likely play an important role in this.</p><p>Overall, our results indicate that modelling gully densities using a process-oriented and spatially explicit method offers opportunities to better quantify this important land degradation process at the global scale. Nevertheless, a key challenge lies in accurately quantifying the importance of soil characteristics and especially in better understanding their relative contribution to runoff production and soil cohesion.</p>