... 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...

...) . The models however were purely empirical and were trained and tested on the same dataset. This limitation can be overcome by regression model based on machine learning (ML). ML techniques are gaining momentum to make accurate estimates in the field of geotechnical engineering. The utility of ML has been...