... 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...

...A. Pant; G.V. Ramana Pullout behaviour of geogrids is critical to understand for 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...