Predicting permanent strain accumulation of unbound aggregates using machine learning algorithms

The objective of this study is to propose a machine learning-based framework for predicting the permanent strain accumulation of unbound aggregates. To develop the machine learning-based framework, material properties of 15 crushed aggregates determined at two different gradations are combined with the applied stress states on aggregate specimens during repeated load triaxial testing by considering the aggregate shear strength characteristics. Three single machine learning algorithms (K-nearest neighbor, neural network, and decision tree) and two ensemble learning algorithms (random forest and extreme gradient boosting) are used in this study. Based on the dataset obtained from 90 permanent deformation tests, hyperparameters, which are used to control the learning process, are optimized. The numerical results demonstrate that all five of the tuned machine learning models exhibit good generalization capacity and performance with the coefficient of determination exceeding 0.99. Among the five models, the extreme gradient boosting model outperforms the others, accurately predicting permanent strain accumulation even for load cycles of 10,000. The contributions of individual input features to the performance of each learning model depend on the dataset. The strength characteristics and the applied deviatoric stress are the two most important features. Therefore, the machine learning-based framework introduced in this study may be effectively used for predicting the permanent strain accumulation of unbound aggregate layer.