Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models

Abstract

Resilient modulus (MR) plays the most critical role in the evaluation and design of flexible pavement foundations. MR is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in experimental and semi-empirical approaches. To determine MR, cyclic triaxial compressive experiments under different confining pressures and deviatoric stresses are needed. However, such experiments are costly and time-consuming. In the present study, an extreme gradient boosting-based (XGB) model is presented for predicting the resilient modulus of flexible pavement foundations. The model is optimised using four different optimisation methods (particle swarm optimisation (PSO), social spider optimisation (SSO), sine cosine algorithm (SCA), and multi-verse optimisation (MVO)) and a database collected from previously published technical literature. The outcomes present that all developed designs have good workability in estimating the MR of flexible pavement foundation, but the PSO−XGB models have the best prediction accuracy considering both training and testing datasets.