Experimental study of subsurface erosion in granitic under the conditions of different soil column angles and flow discharges

Liu, Weiping; Wan, Shaofeng; Huang, Faming; Luo, Xin; Ming-fu, FU

doi:10.1007/s10064-019-01519-w

Cited by 17 publications

(6 citation statements)

References 43 publications

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“…More specifically, according to the statistics in Table 2, within the slope range of 16 • -20 • , the values of PS and FR are 0.3 and 1.7, respectively; the IV and WOE show strong and positive correlations with collapse occurrence; the IOE shows that the weight of slope is 0.1458, second only to lithology. The results of these connection method suggest that, the slope has a very important role on the collapse occurrence, and further suggest that all the connection methods can reflect the effect of environmental factors on collapse susceptibility on the whole [84].…”

Section: Collapse-related Environmental Factor and Connection Resultsmentioning

confidence: 77%

Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

Fan

Huang

et al. 2020

Remote Sensing

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To study the uncertainties of a collapse susceptibility prediction (CSP) under the coupled conditions of different data-based models and different connection methods between collapses and environmental factors, An’yuan County in China with 108 collapses is used as the study case, and 11 environmental factors are acquired by data analysis of Landsat TM 8 and high-resolution aerial images, using a hydrological and topographical spatial analysis of Digital Elevation Modeling in ArcGIS 10.2 software. Accordingly, 20 coupled conditions are proposed for CSP with five different connection methods (Probability Statistics (PSs), Frequency Ratio (FR), Information Value (IV), Index of Entropy (IOE) and Weight of Evidence (WOE)) and four data-based models (Analytic Hierarchy Process (AHP), Multiple Linear Regression (MLR), C5.0 Decision Tree (C5.0 DT) and Random Forest (RF)). Finally, the CSP uncertainties are assessed using the area under receiver operation curve (AUC), mean value, standard deviation and significance test, respectively. Results show that: (1) the WOE-based models have the highest AUC accuracy, lowest mean values and average rank, and a relatively large standard deviation; the mean values and average rank of all the FR-, IV- and IOE-based models are relatively large with low standard deviations; meanwhile, the AUC accuracies of FR-, IV- and IOE-based models are consistent but higher than those of the PS-based model. Hence, the WOE exhibits a greater spatial correlation performance than the other four methods. (2) Among all the data-based models, the RF model has the highest AUC accuracy, lowest mean value and mean rank, and a relatively large standard deviation. The CSP performance of the RF model is followed by the C5.0 DT, MLR and AHP models, respectively. (3) Under the coupled conditions, the WOE-RF model has the highest AUC accuracy, a relatively low mean value and average rank, and a high standard deviation. The PS-AHP model is opposite to the WOE-RF model. (4) In addition, the coupled models show slightly better CSP performances than those of the single data-based models not considering connect methods. The CSP performance of the other models falls somewhere in between. It is concluded that the WOE-RF is the most appropriate coupled condition for CSP than the other models.

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Section: Collapse-related Environmental Factor and Connection Resultsmentioning

confidence: 77%

Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

Fan

Huang

et al. 2020

Remote Sensing

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“…(2) Slope According to the slope classification standard and the actual slope characteristics, the slopes of the study area are divided into five classes as flat slope, gentle slope, moderate slope, steep slope and acute slope [39][40][41][42]. The area with gentle slope covers around 33.88% of the total study area.…”

Section: ) Elevationmentioning

confidence: 99%

Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China

Huang

Yang

Zhang

et al. 2020

IJGI

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Regional terrain complexity assessment (TCA) is an important theoretical foundation for geological feature identification, hydrological information extraction and land resources utilization. However, the previous TCA models have many disadvantages; for example, comprehensive consideration and redundancy information analysis of terrain factors is lacking, and the terrain complexity index is difficult to quantify. To overcome these drawbacks, a TCA model based on principal component analysis (PCA) and a geographic information system (GIS) is proposed. Taking Jiangxi province of China as an example, firstly, ten terrain factors are extracted using a digital elevation model (DEM) in GIS software. Secondly, PCA is used to analyze the information redundancy of these terrain factors and deal with data compression. Then, the comprehensive evaluation of the compressed terrain factors is conducted to obtain quantitative terrain complexity indexes and a terrain complexity map (TCM). Finally, the TCM produced by the PCA method is compared with those produced by the slope-only, the variation coefficient and K-means clustering models based on the topographic map drawn by the Bureau of Land and Resources of Jiangxi province. Meanwhile, the TCM is also verified by the actual three-dimensional aerial images. Results show that the correlation coefficients between the TCMs produced by the PCA, slope-only, variable coefficient and K-means clustering models and the local topographic map are 0.894, 0.763, 0.816 and 0.788, respectively. It is concluded that the TCM of the PCA method matches well with the actual field terrain features, and the PCA method can reflect the regional terrain complexity characteristics more comprehensively and accurately when compared to the other three methods.

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“…Cao [18] conducted a sensitivity analysis study on the factors influencing slope stability and concluded that the mechanical property attenuation of slip zone soil has more influence on the slope with large angle inclination. Liu [19] analyzed how rainfall infiltration changed the physical and mechanical properties of slide soil and then studied the changes in slope stability under different rainfall conditions through numerical simulation. Duan [20] conducted an experimental study on the decay law of strength parameters under longterm immersion conditions of slip zone soils, and carried out a simulation analysis with actual slope projects.…”

Section: Introductionmentioning

confidence: 99%

Study on the mechanism of water-induced degradation of slip zone soils and FDEM coupled simulation of slopes based on multi-scale characteristic

Fan,

Xudong,

Taihong

et al. 2023

Environ. Res. Commun.

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The evolution of the mechanical properties of slip zone soils had a greater impact on the structure of slopes with large upper porosity and excellent infiltration conditions. Especially under the rainfall effect, it was easy to be affected by the infiltration effect of internal injury, which will lay a "hidden danger" for the slope body to trigger sliding in the secondary transformation process. In this paper, based on Haijiao ping landslide in Guizhou, SEM image recognition techniques were used to reveal the water-induced degradation mechanism of the sliding zone soil from multiple scales and the damage parameters were obtained. Meanwhile, the FDEM numerical model was established to simulate the stability of the slope coupled with indoor TCT test results. The results proved that the angle of internal friction decreased linearly with the increase of water content on the macroscopic scale, but the cohesive force showed an increase and then a significant decrease. The effect of matrix suction in the microscopic scale was significant at lower water content, the internal cohesion formed a large number of agglomerate structures to resist external deformation, but the microstructure was loose and porous after sufficient water immersion. The pore space spreads directionally and the area increases by 2.66 times. The cross-scale discrete-finite element coupled simulation method based on image recognition can visually respond to the macroscopic mechanical properties and stability change response of the slope body caused by microscopic damage. The water-induced degradation effect of rainfall on slip zone soils was the inherent factors for the initiation deformation of landslide. The artificial excavation was the external factor that triggered the slope to slide. This type of landslide was more concealed in its natural state and prone to deformation when excavated after long-term rainfall.

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Experimental study of subsurface erosion in granitic under the conditions of different soil column angles and flow discharges

Cited by 17 publications

References 43 publications

Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China

Study on the mechanism of water-induced degradation of slip zone soils and FDEM coupled simulation of slopes based on multi-scale characteristic

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