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

Abstract

ABSTRACT Resilient modulus ( M R ) plays the most critical role in the evaluation and design of flexible pavement foundations. M R is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in experimental and semi-empirical approaches. To determine M R , 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 ( X G B ) model is presented for predicting the resilient modulus of flexible pavement foundations. The model is optimised using four different optimisation methods (particle swarm optimisation ( P S O ), social spider optimisation ( S S O ), sine cosine algorithm ( S C A ), and multi-verse optimisation ( M V O )) and a database collected from previously published technical literature. The outcomes present that all developed designs have good workability in estimating the M R of flexible pavement foundation, but the P S O − X G B models have the best prediction accuracy considering both training and testing datasets.