Novel application of machine learning for estimation of pullout coefficient of geogrid

Abstract

Pullout behaviour of geogrids is critical to understand in the design of mechanically stabilized earth walls. The pullout coefficients are determined through laboratory testing on geogrids embedded in structural fill. Random forest (RF) is a data-driven ensemble learning method that uses decision trees for classification and regression tasks. In the present study, the use of random forest regression technique for estimation of pullout coefficient of geogrid embedded in different structural fills and at variable normal stress based on 198 test results has been investigated using five-fold cross-validation. 80 % of the data has been trained on the model algorithm and the accuracy of the model is then tested on 20 % of the remaining dataset. The performance of the model has been checked using statistical indices, namely R², mean square error, as well as external validation methods. The validity of the model has also been checked against laboratory tests conducted on geogrid embedded in four different fills. The results of the RF model have been compared to results obtained with three other regression models namely, Multivariate Adaptive Regression Splines, Multilayer Perceptron, and Decision Tree Regressor. The results demonstrate superiority of the RF-based regression model in predicting pullout coefficient values of geogrid.