Investigating the impact of pavement surface features on skid resistance: A review on machine learning approach
Mohammad Ali KHASAWNEH, Nayeemuddin MOHAMMED, Ahmad Ali KHASAWNEH, Tamer ALNAZER
Abstract. Pavement friction is a critical factor in ensuring road safety and performance. This review explores the correlation between asphalt pavement surface features and locked-wheel skid trailer friction values using Machine Learning (ML) algorithms. The study focuses on how ML algorithms, particularly Artificial Neural Networks (ANNs), can be utilized to analyze and predict pavement surface characteristics and their impact on skid resistance. The review provides an overview of common pavement surface features that influence skid resistance, such as texture and roughness, and describes the measurement techniques used to quantify these features. It also discusses the application of locked-wheel skid trailer testing as a method for measuring pavement friction and highlights the potential of ML algorithms in analyzing skid resistance data. The review concludes with a discussion on the challenges and future directions of using ML algorithms and ANNs in pavement engineering studies, emphasizing the importance of study in improving road safety and pavement performance.
Keywords
Friction, Machine Learning, Locked Wheel, Pavement Surface, Algorithms
Published online 2/25/2025, 8 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mohammad Ali KHASAWNEH, Nayeemuddin MOHAMMED, Ahmad Ali KHASAWNEH, Tamer ALNAZER, Investigating the impact of pavement surface features on skid resistance: A review on machine learning approach, Materials Research Proceedings, Vol. 48, pp 923-930, 2025
DOI: https://doi.org/10.21741/9781644903414-100
The article was published as article 100 of the book Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions
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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