1. Home
  2. TRID
  3. View Record
https://www.nationalacademies.org/trb/events

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

Resilient modulus ($M_R$MR) plays the most critical role in the evaluation and design of flexible pavement foundations. $M_R$MR is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in experimental and semi-empirical approaches. To determine $M_R$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 ($XGB$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 ($PSO$PSO), social spider optimisation ($SSO$SSO), sine cosine algorithm ($SCA$SCA), and multi-verse optimisation ($MVO$MVO)) and a database collected from previously published technical literature. The outcomes present that all developed designs have good workability in estimating the $M_R$MR of flexible pavement foundation, but the $PSO-XGB$PSO-XGB models have the best prediction accuracy considering both training and testing datasets.

  • Record URL:
  • Availability:
  • Supplemental Notes:
    • © 2022 Informa UK Limited, trading as Taylor & Francis Group 2022. Abstract reprinted with permission of Taylor & Francis.
  • Authors:
    • Sarkhani Benemaran, Reza
    • Esmaeili-Falak, Mahzad
    • Javadi, Akbar
  • Publication Date: 2023

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01913661
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 1 2024 4:57PM