Machine learning application in geotechnics: predicting sand behavior with direct shear tests

In geotechnical engineering, soil strength parameters are fundamental to design. Field and laboratory tests are essential, yet they often involve practical and financial constraints. Traditional approaches based on empirical or theoretical relationships may not adequately capture the complexity of soil behavior. This study investigates the use of artificial intelligence as an alternative approach to address these limitations. A predictive model was developed to represent the stress–strain response obtained from direct shear tests on sandy materials. Data compiled and digitized from multiple published sources were used to build an experimental dataset for training three machine learning (ML) algorithms: Support Vector Regression (SVR), Random Forest (RF), and Feedforward Neural Network (FNN). The models were evaluated using performance metrics and validation test curves. Among them, RF produced the most consistent performance. SVR and FNN also showed satisfactory results, although to a lesser extent. These findings indicate that ML techniques, particularly RF, can provide useful support for geotechnical engineers and researchers in predicting the behavior of sands, even when working with a relatively limited dataset.