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

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 ($XGB$) 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 $M_R$ of flexible pavement foundation, but the $PSO-XGB$ models have the best prediction accuracy considering both training and testing datasets.

KEYWORDS:

• Resilient modulus prediction
• artificial neural networks
• extreme gradient boosting
• PSO
• MVO
• SSO
• SCA

Disclosure statement

No potential conflict of interest was reported by the author(s).