Stability assessment of slopes subjected to circular-type failure using tree-based models

Amirkiyaei, Vahid; Ghasemi, Ebrahim

doi:10.1080/19386362.2020.1862538

Cited by 14 publications

(4 citation statements)

References 40 publications

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“…One of the benefits of the machine learning approach over traditional statistical approaches such as regression is that they can handle more than two-dimensional data. For data-driven prediction analysis of diverse geotechnical problems, many researchers have adopted the tree-based approach [20,61,62]. As a result, tree-based ML techniques, such as DT, were used to build models and identify the key predictors of pile-soil friction in this work.…”

Section: Decision Tree (Dt) Algorithmmentioning

confidence: 99%

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

et al. 2022

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The major criteria that control pile foundation design is pile bearing capacity (Pu). The load bearing capacity of piles is affected by the various characteristics of soils and the involvement of multiple parameters related to both soil and foundation. In this study, a new model for predicting bearing capacity is developed using an extreme gradient boosting (XGBoost) algorithm. A total of 200 driven piles static load test-based case histories were used to construct and verify the model. The developed XGBoost model results were compared to a number of commonly used algorithms—Adaptive Boosting (AdaBoost), Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) using various performance measure metrics such as coefficient of determination, mean absolute error, root mean square error, mean absolute relative error, Nash–Sutcliffe model efficiency coefficient and relative strength ratio. Furthermore, sensitivity analysis was performed to determine the effect of input parameters on Pu. The results show that all of the developed models were capable of making accurate predictions however the XGBoost algorithm surpasses others, followed by AdaBoost, RF, DT, and SVM. The sensitivity analysis result shows that the SPT blow count along the pile shaft has the greatest effect on the Pu.

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Section: Decision Tree (Dt) Algorithmmentioning

confidence: 99%

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

et al. 2022

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“…Based on the same database, Samui [33] and Yang et al [34] used a support vector machine (SVM) and genetic programming to determine FOS, respectively. Amirkiyaei and Ghasemi [35] constructed two tree-based models to assess circular-type failure slopes based on 87 cases. Zhou et al [36] collected 221 slope cases and employed the gradient-boosting machine to predict the SS.…”

Section: Introductionmentioning

confidence: 99%

Slope Stability Prediction Using k-NN-Based Optimum-Path Forest Approach

2023

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Slope instability can lead to catastrophic consequences. However, predicting slope stability effectively is still challenging because of the complex mechanisms and multiple influencing factors. In recent years, machine learning (ML) has received great attention in slope stability prediction due to its strong nonlinear prediction ability. In this study, an optimum-path forest algorithm based on k-nearest neighbor (OPFk-NN) was used to predict the stability of slopes. First, 404 historical slopes with failure risk were collected. Subsequently, the dataset was used to train and test the algorithm based on randomly divided training and test sets, respectively. The hyperparameter values were tuned by combining ten-fold cross-validation and grid search methods. Finally, the performance of the proposed approach was evaluated based on accuracy, F1-score, area under the curve (AUC), and computational burden. In addition, the prediction results were compared with the other six ML algorithms. The results showed that the OPFk-NN algorithm had a better performance, and the values of accuracy, F1-score, AUC, and computational burden were 0.901, 0.902, 0.901, and 0.957 s, respectively. Moreover, the failed slope cases can be accurately identified, which is highly critical in slope stability prediction. The slope angle had the most important influence on prediction results. Furthermore, the engineering application results showed that the overall predictive performance of the OPFk-NN model was consistent with the factor of safety value of engineering slopes. This study can provide valuable guidance for slope stability analysis and risk management.

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“…Support vector and gradient boosting regression are regarded as the top techniques after examining the assessment indicators. Amirkiyaei and Ghasemi [27] developed two tree-based models utilizing a collection of 87 slope cases: a regression tree-based algorithm for prediction of slope safety factor and a classi cation tree-based algorithm for prediction of slope stability status. Their ndings show that the created tree models are highly reliable and e cient instruments for evaluating slope stability.…”

Section: Introductionmentioning

confidence: 99%

Straightforward slope stability prediction under seismic conditions using machine learning algorithms

Barkhordari,

Armaghani

et al. 2023

Preprint

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One of the most significant and crucial issues in geotechnical engineering works, such as earth dams, embankments, and landfills to name a few, is slope stability assessment. Better methods are required to anticipate slope collapse because of its fatal effects. The goal of this research is to create a straightforward machine learning (ML) model for examining slope stability under seismic conditions. Four ML algorithms are examined, including Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Light Gradient Boosting Machine (LGBM), and Linear Discriminant Analysis (LDA). The models are trained and tested on the database containing 700 slopes. 10-fold cross validation is utilized for parameter tuning, model training and performance estimating of machine learning models using training set. The best model is interpreted using the SHapley Additive exPlanations (SHAP) method, which is built on game theories. Among the studied models, the LGBM model is the most accurate model based on ranking technique. Most influential features for slope stability prediction under seismic conditions are detected by the SHAP method as follows: peak ground acceleration, friction angle, and angle of inclination.

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Stability assessment of slopes subjected to circular-type failure using tree-based models

Cited by 14 publications

References 40 publications

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Slope Stability Prediction Using k-NN-Based Optimum-Path Forest Approach

Straightforward slope stability prediction under seismic conditions using machine learning algorithms

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