A probabilistic method for evaluating wedge stability based on blind data theory

Ma, Zhongjun; Qin, Shengwu; Chen, Junjun; Lv, Jiangfeng; Chen, Jianping; Zhao, Xin

doi:10.1007/s10064-017-1204-3

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…To assess the efficiency and accuracy of the JDRV method for determining the PDF of the safety factor, a rock wedge example described by Ma et al (2019) is presented. In the example, the wedge has a height of 33 m and a slope angle of 65 .…”

Section: Illustrative Examplementioning

confidence: 99%

“…Many scholars have devoted a great number of contributions to developing the probabilistic method for analysing the stability of a rock wedge. In summary, these methods can presumably be classified into four main types: the blind data theory (Ma et al 2019), Monte Carlo simulations (MCs) (Park and West 2001;Li et al 2009a, 2009b), the point estimate method (PEM) (Park et al 2012), and the first-order reliability method (FORM) (Low 1997;Jimenez-Rodriguez and Sitar 2007;Lee et al 2012). Theoretically, some disadvantages may be associated with these methods.…”

Section: Introductionmentioning

confidence: 99%

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Analytical stochastic analysis of rock wedge stability using a JDRV method considering the residual factor as a random variable

Song

Chen

2020

Geomatics, Natural Hazards and Risk

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The stability of a rock wedge is one of the main failure models of rock slopes. Although the strength parameters of the discontinuity planes are commonly deemed to be between the peak and residual strengths, the effect of a residual factor on the stability of a rock wedge still has not been considered. This paper presents an analytical stochastic method, considering the residual factor as a random variable, for determining the stability of a rock wedge. First, based on the Hoek-Bray method, this paper derives the formulas for evaluating the wedge stability in terms of the residual factor, and then, based on a jointly distributed random variables (JDRV) method, the calculation formulas for the probability distribution function of the safety factor is established. In the analytical method, the friction angle and cohesion are selected to satisfy the truncated normal distribution, and the residual factor is assumed to obey the generalized beta distribution. The study of a typical rock wedge shows that the results of the analytical method and the Monte Carlo simulation are almost consistent. Besides, it is also shown that the residual factor has a substantial influence on the estimation accuracy of the failure probability of the wedge.

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Section: Illustrative Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytical stochastic analysis of rock wedge stability using a JDRV method considering the residual factor as a random variable

Song

Chen

2020

Geomatics, Natural Hazards and Risk

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“…The lithology controls the stability of the slope and determines the amount of material that is available for debris flow [ 46 , 47 ]. According to the anti-weathering ability of rock, the lithology was divided into four types: soil, soft rock, hard rock and extremely hard rock.…”

Section: Data Preparationmentioning

confidence: 99%

A Method for Improving Controlling Factors Based on Information Fusion for Debris Flow Susceptibility Mapping: A Case Study in Jilin Province, China

Dou

Qin

Zhang

et al. 2019

Entropy

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Debris flow is one of the most frequently occurring geological disasters in Jilin province, China, and such disasters often result in the loss of human life and property. The objective of this study is to propose and verify an information fusion (IF) method in order to improve the factors controlling debris flow as well as the accuracy of the debris flow susceptibility map. Nine layers of factors controlling debris flow (i.e., topography, elevation, annual precipitation, distance to water system, slope angle, slope aspect, population density, lithology and vegetation coverage) were taken as the predictors. The controlling factors were improved by using the IF method. Based on the original controlling factors and the improved controlling factors, debris flow susceptibility maps were developed while using the statistical index (SI) model, the analytic hierarchy process (AHP) model, the random forest (RF) model, and their four integrated models. The results were compared using receiver operating characteristic (ROC) curve, and the spatial consistency of the debris flow susceptibility maps was analyzed while using Spearman’s rank correlation coefficients. The results show that the IF method that was used to improve the controlling factors can effectively enhance the performance of the debris flow susceptibility maps, with the IF-SI-RF model exhibiting the best performance in terms of debris flow susceptibility mapping.

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“…In bivariate statistical analysis, the weights of the landslide conditioning factors are assigned based on landslide density using different methods-including frequency ratio (FR) [13,15,[20][21][22], the information content model (ICM) [23,24], weight of evidence (WoE) [16], certainty factors (CF) [25], favorability functions (FF) [26], and the likelihood ratio model (LRM) [27]. The multivariate statistical methods evaluate the combined relationship between a dependent variable (landslide occurrence) and a series of independent variables (landslide controlling factors), and the most popular methods to analyze the resulting matrix include logistic regression (LR) [6,13,[28][29][30][31][32][33], discriminant analysis (DA) [34,35], random forest (RF) [36][37][38] and active learning statistical analysis, such as the artificial neural networks (ANNs) [3,6,[39][40][41][42].Physically based methods, such as deterministic techniques, are based on mathematical modeling of the physical mechanisms controlling slope failure [43][44][45][46][47][48][49]. However, it is reported that the methods are only applicable over large areas when the geological and geomorphological conditions are fairly homogeneous and the landslide types are simple [17].Moreover, several studies have used two or more models to produce landslide susceptibi...…”

mentioning

confidence: 99%

“…Physically based methods, such as deterministic techniques, are based on mathematical modeling of the physical mechanisms controlling slope failure [43][44][45][46][47][48][49]. However, it is reported that the methods are only applicable over large areas when the geological and geomorphological conditions are fairly homogeneous and the landslide types are simple [17].…”

mentioning

confidence: 99%

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Qin

Cao

et al. 2019

Entropy

Self Cite

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Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.Entropy 2019, 21, 372 2 of 24 involving land use management as an efficient approach to reduce property damage and economic loss in landslide-prone areas [1,[6][7][8][9]. The outcome maps would be useful for general planned development activities and disaster management in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.Landslide susceptibility maps can be obtained using both qualitative (inventory-based and knowledge-driven methods) or quantitative approaches (data-driven methods and physically based models) [4,[10][11][12][13][14][15][16][17].Landslide inventory-based techniques, as a prelude to all other methods, include the collection of past landslide data, construction of databases, and production of susceptibility maps based on those data [18]. Landslide inventory mapping can be carried out using a variety of methods that were updated and summarized by Corominas et al. [17].Knowledge-driven methods that estimate landslide potential from the practical experience and expertise of the researcher are used by geomorphologists to analyze...

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A probabilistic method for evaluating wedge stability based on blind data theory

Cited by 12 publications

References 30 publications

Analytical stochastic analysis of rock wedge stability using a JDRV method considering the residual factor as a random variable

Analytical stochastic analysis of rock wedge stability using a JDRV method considering the residual factor as a random variable

A Method for Improving Controlling Factors Based on Information Fusion for Debris Flow Susceptibility Mapping: A Case Study in Jilin Province, China

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

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