基于机器学习的边坡稳定性分析方法——以国内618个边坡为例.

In order to quickly and accurately predict the slope stability, an intelligent evaluation method of slope stability based on machine learning was proposed. Based on the characteristics of 618 slopes in China, 6 typical slope parameters, including gravity (γ), cohesion (C), internal friction angle (φ), slope angle (β), slope height (H) and pore water pressure (P), were selected to establish the slope stability evaluation dataset. Slope stability prediction models were established using gradient boosting machine (GBM), support vector machine (SVM), artificial neural network (ANN) and random forest (RF) in the machine learning algorithm. The models were trained and learned using the training set. The model parameters were adjusted using the 5-fold cross validation and grid-search. The ability of the model to classify the slope stability was tested through the test set, and the optimal model was determined. The results show that by comparing the area under the receiver operating characteristic curve (AUC) and F1Score of GBM, SVM, ANN and RF algorithms, the AUC value and F1Score of RF algorithm are 0.969 and 0.904, respectively; RF algorithm has the best evaluation index and is more suitable for analyzing slope stability. Based on the different slope stability prediction models established by deleting different characteristic variables of RF algorithm, the characteristic parameters in descending order of sensitivity are γ, β, H, φ, P, C. According to the different sensitivity factors, the slope protection measures are proposed based on the sensitivity of the characteristic parameters. [ABSTRACT FROM AUTHOR]