A comparative study of supervised machine learning approaches for slope failure production

Abstract

Over the years, machine learning, which is a well-known method in artificial intelligent (AI) field has become a new trend and extensively applied in various applications to solve a realworld problem. This includes slope failure prediction. Slope failure is among the major geo-hazard phenomenon which gives the significant impact to the environment or human beings. The estimation of slope failure in slope stability analysis is a complex geotechnical engineering problem that involves many factors such as geology, topography, atmosphere, and land occupancy. Generally, slope failure can be estimated based on traditional methods such as limit equilibrium method (LEM) or finite equilibrium method (FEM). However, beside the methods are quite tedious and time consuming, LEM and FEM have their own limitations and do not guarantee the effectiveness when dealing against problem with various geometry or assumptions. Hence, the introduction of machine learning approaches provides the alternative tools for the prediction of slope failure. Current study applies two mostly used supervised machine learning approaches, support vector machine (SVM) and decision tree (DT) to predict the slope failure based on classification problem using historical cases. 148 of slope cases with six input parameters namely “unit weight, cohesion, internal friction angle, slope angle, slope height and pore pressure ratio and factor of safety (FOS) as an output parameter”, was collected from multinational dataset that has been extracted from the literature. For development of the prediction model, the slope data was divided into 80% training data and 20% testing data. The prediction result from testing data was validated based on statistical analysis. The result shows that SVM model has outperformed DT model by giving the prediction accuracy of 97%. ith the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades. This study employs an “artificial neural network” (ANN) to predict the slope failures based on historical circular slope cases. Using the feed-forward backpropagation algorithm with a multilayer perceptron network, ANN is a powerful ML method capable of predicting the complex model of slope cases. However, the prediction result of ANN can be improved by integrating the statistical analysis method, namely grey relational analysis (GRA), to the ANN model. GRA is capable of identifying the influencing factors of the input data based on the correlation level of the reference sequence and comparability sequence of the dataset. This statistical machine learning model can analyze the slope data and eliminate the unnecessary data samples to improve the prediction performance. Grey relational analysis-artificial neural network (GRANN) prediction model was developed based on six slope factors: unit weight, friction angle, cohesion, pore pressure ratio, slope height, and slope angle, with the factor of safety (FOS) as the output factor. The prediction results were analyzed based on accuracy percentage and receiver operating characteristic (ROC) values. It shows that the GRANN model has outperformed the ANN model by giving 99% accuracy and 0.999 ROC value, compared with 91% and 0.929.

Estimation of slope stability using ensemble-based hybrid machine learning approaches

“…Other researchers used a variety of novel soft computing techniques to predict FOS, including Multiple Regression (MR), Genetic Algorithm (GA), Support Vector Machine (SVM), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme gradient boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and hybrid models, Gradient Boosting Decision Tree was used in several applications, and the results were found to be noticeably superior to those attained by employing traditional techniques. (Marrapu and Jakka, 2017;Lin et al, 2018a;Bui et al, 2020a;Huang et al, 2020;Deris et al, 2021;Jingjing et al, 2021;Kardhani et al, 2021;Sina et al, 2021;Christoph et al, 2022;Feezan et al, 2022;Gagan et al, 2022;Gexue et al, 2022;Zhihao and Zhiwei, 2022;Mahmoodzadeh and Mohammadi, 2023;Xu et al, 2023). Arunav Chakraborty and Diganta Goswami (Arunav and Diganta, 2017) carried out their work on slope stability prediction utilizing artificial neural networks, very advanced modeling methods that can be suitable for modeling highly complicated functions.…”

Safety Factors Investigation Based on FEM and LEM Approach in Toll Road Embankment Slope

“…Slope stability analysis is normally used to estimate slope failure and this is a challenging geotechnical engineering problem that involves the combination of different variables such as topography, geology, and land occupancy [2]. The main output of this calculation is the safety factor value.…”