A novel method for locating the critical slip surface of a soil slope

Li, S. H.; Wu, Lizhou; Luo, Xiaohui

doi:10.1016/j.engappai.2020.103733

Cited by 26 publications

(15 citation statements)

References 54 publications

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“…Machine learning methods are data-driven tools that learn from the relationships of existing data [24]. Hence, ML does not assume a statistical model [25][26][27]. Additionally, these techniques have been widely applied in engineering [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset

Zhu

2022

Applied Sciences

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Predicting clay sensitivity is important to geotechnical engineering design related to clay. Classification charts and field tests have been used to predict clay sensitivity. However, the imbalanced distribution of clay sensitivity is often neglected, and the predictive performance could be more accurate. The purpose of this study was to investigate the performance that extreme gradient boosting (XGboost) method had in predicting multiclass of clay sensitivity, and the ability that synthetic minority over-sampling technique (SMOTE) had in addressing imbalanced categories of clay sensitivity. Six clay parameters were used as the input parameters of XGBoost, and SMOTE was used to deal with imbalanced classes. Then, the dataset was divided using the cross-validation (CV) method. Finally, XGBoost, artificial neural network (ANN), and Naive Bayes (NB) were used to classify clay sensitivity. The F1 score, receiver operating characteristic (ROC), and area under the ROC curve (AUC) were considered as the performance indicators. The results revealed that XGBoost showed the best performance in the multiclassification prediction of clay sensitivity. The F1 score and mean AUC of XGBoost were 0.72 and 0.89, respectively. SMOTE was useful in addressing imbalanced issues, and XGBoost was an effective and reliable method of classifying clay sensitivity.

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Section: Introductionmentioning

confidence: 99%

“…For example, Zhang et al [34] used XGBoost and Bayesian optimization to predict the shear strength of soft clays. Machine learning methods outperform traditional methods [27,28]. Moreover, XGBoost is an excellent ensemble method, better than conventional ML, and shows great potential in geotechnical engineering [34].…”

Section: Introductionmentioning

confidence: 99%

Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset

Zhu

2022

Applied Sciences

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“…Many geotechnical engineering problems involve unsaturated seepage, such as the soil slope stability and dam infiltration involved in rainfall or groundwater changes, and the transportation of pollutants in landfill (Morway et al, 2013;Ku et al, 2017;Li et al, 2020). Therefore, effectively simulating and analyzing unsaturated seepage is of great practical significance (Wang et al, 2011;Zang et al, 2016;Wu et al, 2016;2020a).…”

Section: Introductionmentioning

confidence: 99%

Modelling Unsaturated Flow in Porous Media Using an Improved Picard Iteration Scheme

Zhu

2021

Preprint

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The numerical solution of various systems of linear equations describing fluid infiltration uses the Picard iteration (PI). However, because many such systems are ill-conditioned, the solution process often has a poor convergence rate, making it very time-consuming. In this study, a control volume method based on non-uniform nodes is used to discretize the Richards equation, and adaptive relaxation is combined with a multistep preconditioner to improve the convergence rate of PI. The resulting adaptive relaxed PI with multistep preconditioner (MP(m)-ARPI) is used to simulate unsaturated flow in porous media. Three examples are used to verify the proposed schemes. The results show that MP(m)-ARPI can effectively reduce the condition number of the coefficient matrix for the system of linear equations. Compared with conventional PI, MP(m)-ARPI achieves faster convergence, higher computational efficiency, and enhanced robustness. These results demonstrate that improved scheme is an excellent prospect for simulating unsaturated flow in porous media.

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“…Rainfall-induced landslides are the most common type of landslide disaster (Higgitt et al 2014;Li et al 2020a;2020b). The rainfall-induced landslide caused devastating disasters to mountain residents, such as many economic losses and casualties (Saito et al 2017;Fustos et al 2020;Yang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Stability and Time-Delay Effect of Rainfall-induced Landslide Considering Air Entrapment

Zhang

Zhao

et al. 2021

Preprint

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Rainfall-induced landslide is a typical geological disaster in the Three Gorges reservoir area. The air entrapment in the pores of soils has a hindrance to the infiltration of the slope. It is mainly reflected in the hydraulic hysteresis after rainfall and the decrease of the slope anti-sliding force. A method considered the air entrapment of the closed gas in soil particles’ pores is developed to study the time-delay effect and slope stability under the rainfall process. The Green-Ampt infiltration model is used to obtain the explicit analytical solution of the slope infiltration considering air entrapment. Moreover, the relationship between the safety factor, the rainfall duration, and the depth of the wetting front under the three rainfall conditions (qrain=12, 26, 51 mm/h) is discussed. The results show that the air entrapment causes a significant time-delay effect of the landslide, and the hydraulic hysteresis is the strongest under the condition of heavy rainfall (qrain= 51mm/h). The time-delay effect lasts longer than low rainfall and heavy rainfall when the rainfall intensity (qrain= 26 mm/h) is slightly greater than saturated hydraulic conductivity Ks. Parameter analysis shows that when air entrapment is considered, the smaller the slope angle and the effective internal friction angle, the more significant the air entrapment has on the slope stability; the smaller the effective cohesion, the longer the air resistance lasts. Finally, the application of the Bay Area landslide is consistent with the actual state of the landslide.

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A novel method for locating the critical slip surface of a soil slope

Cited by 26 publications

References 54 publications

Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset

Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset

Modelling Unsaturated Flow in Porous Media Using an Improved Picard Iteration Scheme

Stability and Time-Delay Effect of Rainfall-induced Landslide Considering Air Entrapment

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