Composite Material in Road Pavement: A Review of Experimental, Numerical and Machine Learning Approaches
Mazlan RAFIDAH, Mohd Khairul Afzan Mohd LAZI, Fauzan Mohd JAKARNI, Yusra Aulia SARI, Mohamad Saifullah SAMSUDIN, Rosmawati MAMAT, Ekarizan SHAFFIE, Dylan Tay WANG AN, Nurul Najihah MAD ROSNI, Ikhwanul IHSAN
Abstract. Fiber-reinforced asphalt is increasingly used to improve pavement durability and sustainability. Experimental studies show that synthetic, natural, and recycled fibers can enhance rutting resistance, cracking resistance, fatigue life, moisture durability, and binder stability. Finite element and discrete element modelling provide complementary insight into pavement-scale response and fiber-matrix mechanisms, while machine learning (ML) supports rapid performance prediction and mix optimisation. This review compares the principal fiber types and integrates experimental, numerical, and data-driven approaches. Key remaining challenges are optimum fiber selection and dosage, long-term ageing and moisture durability, natural-fiber degradation, dataset limitations, and field-scale validation. An integrated experimental-numerical-ML framework is recommended for performance-based design of durable and sustainable fiber-reinforced asphalt pavements.
Keywords
Composite, Pavement, Numerical, Machine Learning
Published online 11/15/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mazlan RAFIDAH, Mohd Khairul Afzan Mohd LAZI, Fauzan Mohd JAKARNI, Yusra Aulia SARI, Mohamad Saifullah SAMSUDIN, Rosmawati MAMAT, Ekarizan SHAFFIE, Dylan Tay WANG AN, Nurul Najihah MAD ROSNI, Ikhwanul IHSAN, Composite Material in Road Pavement: A Review of Experimental, Numerical and Machine Learning Approaches, Materials Research Proceedings, Vol. 74, pp 165-172, 2026
DOI: https://doi.org/10.21741/9781644904473-21
The article was published as article 21 of the book Composite Science and Technology
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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