Analysis and Prediction of Multilayered Structures Response under Cross-Anisotropic Conditions Using Closed-Form Models, Finite Element Validation, and Machine Learning Approaches

Description :

Flexible pavements are multilayered systems designed to ensure that stresses and strains within their layers and underlying subgrade remain within acceptable limits, making their accurate evaluation essential for reliable design. While conventional approaches assume isotropic material behavior, field evidence shows that pavement layers often exhibit cross-anisotropic properties due to compaction and material characteristics, which can significantly influence structural response. This research aims to develop a comprehensive framework for analyzing and predicting the response of multilayered flexible pavements under such conditions. The methodology combines theoretical, numerical, and data-driven approaches. Several tasks will be proposed as initial expectations but not limited to the following: - review of existing models to identify current limitations, - develop an advanced analytical formulation that incorporates cross-anisotropy, variable material properties, realistic loading configurations, and interface conditions - three-dimensional finite element validation - develop machine learning techniques to enable rapid and accurate prediction of pavement response The expected outcome is a robust, efficient, and practical tool that enhances the accuracy of pavement analysis and supports improved design, maintenance, and performance evaluation.

Titulaire :
RAHHAL Muhsin Elie

Contact USJ :
muhsin.rahal@usj.edu.lb

Chercheur(s) :
M. Muhsin Elie RAHHAL
Mme Farah HOMSI

Projet présenté au CR, le : 01/01/2026

Projet achevé auprès du CR : 31/12/2028