A complex rock topple-rock slide failure of an anaclinal rock slope in the Wu Gorge, Yangtze River, China

Gu, Dongming; Huang, Da

doi:10.1016/j.enggeo.2016.04.037

Cited by 100 publications

(20 citation statements)

References 30 publications

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“…The variation of lithology may lead to the variation of strength and permeability of rock stratum. Usually, the landslide slides along a rock stratum with low strength and poor permeability [91][92][93][94]. The study area has ten lithology types ( Figure 2l).…”

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.Symmetry 2019, 11, 762 2 of 24years, machine learning method has been gradually applied in landslide susceptibility mapping researches, such as artificial neural network (ANN) [17][18][19], support vector machine (SVM) [20][21][22], logistic model tree (LMT) [23,24], rotation forest (RF) [25,26], classification and regression tree (CART) [27,28], adaptive neuro-fuzzy inference systems (ANFIS) [29,30], and genetic algorithm (GA) [31,32]. Furthermore, statistical approach is another widely-used model which can also be divided into two types: bivariate and multivariate. In statistical approaches, the weight of each class of every factor was calculated by overlying the landslide inventory map and landslide conditioning factors map [33,34]. The frequently-used statistical approaches are frequency ratio (FR) [35][36][37], logistic regression (LR), evidential belief function (EBF) [38,39], weights of evidence (WoE) [40,41], certainty factor (CF) [42,43], and information value (IV) [44,45].In this paper, three bivariate statistical methods, namely weight of evidence (WoE), evidence belief function (EBF), index of entropy (IoE), coupled with logistic regression (LR) were introduced to carry out the landslide susceptibility mapping in Muchuan County, which lies in the southeast of Sichuan Province, China. All the approaches were first applied in Muchuan County and the main purpose of this article is to obtain the landslide susceptibility maps through the above models. The main difference between this study and literatures referenced above is to achieve accurate landslide susceptibility maps for...

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Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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“…The model is built from lots of small blocks of 20 cm (length) × 5 cm (width) × 4 cm (height) (Figure 6a). The density of the blocks is 2.51 g/cm 3 , the tensile strength σ bt is 0.094 MPa, elastic modulus is 138.74 MPa, cohesion c b is 0.711 MPa and internal friction angle θ b is 36.2 • . The friction angle of block side joints is 30 • .…”

Section: An Application Research On An Anti-dip Rock Slope Model Undementioning

confidence: 99%

“…This failure mode is one of the most common seen in natural or excavated rock slopes, e.g., rock slopes induced by construction including of hydropower stations [1][2][3], open-pit mines [4,5], railway [5] and other engineering works [6]. In recent years, quite a number of toppling failures occurred during earthquakes and caused huge losses to lives as well as infrastructures.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Solution for Block Toppling Failure of Rock Slopes during an Earthquake

Guo

Yang

et al. 2017

Applied Sciences

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Toppling failure is one of the most common failure types in the field. It always occurs in rock masses containing a group of dominant discontinuities dipping into the slope. Post-earthquake investigation has shown that many toppling rock slope failures have occurred during earthquakes. In this study, an analytical solution is presented on the basis of limit equilibrium analysis. The acceleration of seismic load as well as joint persistence within the block base, were considered in the analysis. The method was then applied into a shake table test of an anti-dip layered slope model. As predicted from the analytical method, blocks topple or slide from slope crest to toe progressively and the factor of safety decreases as the inputting acceleration increases. The results perfectly duplicate the deformation features and stability condition of the physical model under the shake table test. It is shown that the presented method is more universal than the original one and can be adopted to evaluate the stability of the slope with potential toppling failure under seismic loads.

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“…For rock slopes with potential geologic hazards, the temporal evolution of geomorphology and structure, as well as the influence of lithologic differences and tectonic activity on slope deformation, are of great interest [20][21][22][23][24][25]. For rock slopes that have undergone landslides, mathematical description of the dynamic mechanisms and kinematic processes of disaster triggers is a key point [26][27][28][29][30][31][32]. To prevent the occurrence of rock slides, methods for evaluating the stability of slopes in reservoir areas have been investigated, such as developmental characterization of cracked rock masses, reliability analysis of rock slopes, or predictive models based on monitoring or statistical analysis [33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

The Role of Water and Lithology on the Deformation and Failure of an Anaclinal Rock Slope in a Hydropower Reservoir

Chen

Nie

et al. 2020

Advances in Civil Engineering

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A series of settlement, cracking, and collapse of the Zhengjiaping slope has been increasingly developing since October 2015 during impoundment at the Dagangshan Hydropower Station. It is a dangerous signal, which means thatlandslide potential will be existed for the Zhengjiaping slope and poses greatthreat to the operation of hydropower station and traffic safety. Related slope protection measures and research studies have been implemented accordingly in the Zhengjiaping slope to prevent and control adverse influences on property security and human life in the reservoir area. In order to understand the geological and hydrological settings of the Zhengjiaping slope and its surroundings, a field investigation was carried out to determine the lithological composition and toppling/sliding deformation characteristics using exploratory adit and site boreholes. The large deformation process in the apparently deformed area was analyzed using borehole inclinometers and global navigation satellite systems (GNSS). It was found that the apparently deformed area zone was characterized by crushed rock masses, with only a small amount of slope deposits and the sliding deformation occurring in Zone I. The deformation process of the reservoir landslide was considered to be a complex integration of the geological effects of various adverse factors. Impoundment and heavy rainfall are the direct causes of sliding deformation. During the preparation of the basic conditions for sliding, lithology, tectonic activity, and artificial disturbances play an important role, including the sliding mass and the sliding surface zone.

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A complex rock topple-rock slide failure of an anaclinal rock slope in the Wu Gorge, Yangtze River, China

Cited by 100 publications

References 30 publications

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

An Analytical Solution for Block Toppling Failure of Rock Slopes during an Earthquake

The Role of Water and Lithology on the Deformation and Failure of an Anaclinal Rock Slope in a Hydropower Reservoir

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