... and properties of both the geosynthetic and the soil. This paper introduces a machine learning approach, specifically a random forest algorithm, for predicting interface friction angles. The dataset comprises 495 interfaces involving geomembranes and sand, with 14 influencing parameters recorded for each...

... strength of GM liner against pull-out failure from anchorage with the help of machine-learning (ML) techniques. Five ML models, namely multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector regression (SVR), random forest (RF) and locally weighted regression (LWR) were employed...

... Thomas Telford Ltd 2021 Geosynthetics random forest machine learning pullout test Mechanically stabilized earth walls are subject to external and internal modes of failure. According to FHWA (2001) , the primary modes of failure against internal stability of a mechanically stabilized...