Evaluation for Broken Line Slope Stability Based on Ensemble Learning and LEM
Conventional mechanical analysis methods for slope stability have limited computational efficiency and need professional software.Machine learning,as an efficient analysis method,can be applied to slope stability evaluation.Abundant broken line slope samples are randomly generated and the corresponding factors of safety are solved by the limit equilibrium method(LEM)in this paper,so as to build a slope factor of safety database,and the LEM surrogate model is established by integrating neural network models.Two ensemble algorithms,Bagging and AdaBoost.R2,are used to establish a neural network ensemble model to predict factor of safety,which is verified by practical slope engineering cases,contrasting with single neural network model.The performances are evaluated by ROC curve analysis method,and reasonable threshold of factor of safety is determined.Results show that two ensemble models are significantly better than the single neural network model.While the AUC value of the single neural network model is 0.826,the AdaBoost.R2 model is 0.893,and the Bagging model can recognize slope stability situation better with value of 0.929.The proposed method can evaluate broken line slope stability quickly and accurately,providing a tool for rapid stability evaluation of a large number of regional slopes.
neural network ensemblelimit equilibrium methodbroken line side slopeslope stabilityROC curve analysislandslidesengineering geology
NETL
NSTL
万方数据
