Evaluation Approaches and Enhancing Strategies for Airport Pavement Skid Resistance: A State-of- the- Art

Airport pavement skid resistance is a critical factor affecting aircraft operational safety during taxiing, takeoff and landing, especially under wet or contaminated runway conditions. This paper systematically summarizes recent progress in the evaluation, prediction, degradation, and enhancement of airport pavement skid resistance. The tire–pavement friction mechanism is reviewed from the perspectives of adhesion, hysteresis, cohesion, and wear, with particular attention to the dominant roles of adhesion and hysteresis and the need to better characterize the real rubber–pavement interfacial contact process. Then, the commonly used friction coefficient-based evaluation methods, including longitudinal friction coefficient, sideways force coefficient, and slider-based measurements, are discussed, together with their applicability and limitations. The review further analyzes texture-based evaluation approaches, including digital image processing, X-ray computed tomography, laser scanning, three-dimensional reconstruction, and point-cloud-based texture characterization. For skid resistance prediction, empirical models, mechanical analysis models, energy-based models, finite element models, and machine learning methods, especially convolutional neural networks, are compared in terms of their prediction capability and applicability. Existing studies show that non-contact texture measurement combined with data-driven modeling has strong potential for improving friction prediction, while finite element modeling provides an effective tool for analyzing the coupled tire–water–pavement–temperature interaction. The long-term degradation of skid resistance is mainly associated with surface wear, aggregate polishing, rubber deposition, contaminant coverage, and environmental effects. Finally, enhancement strategies are summarized as surface treatments and functional overlays, among which functional overlays show greater potential for maintaining long-term skid resistance on existing airport pavements. Future research should focus on more realistic rubber–pavement interfacial contact mechanisms, standardization of friction measurement methods, refined texture modeling, large-scale multi-source datasets, interpretable deep learning models, and field-applicable skid resistance rehabilitation technologies. ● Clarified tire-pavement friction mechanisms and critical factors influencing airport pavement skid resistance. ● Systematized multiple friction measurement methods, providing a comparative overview of direct contact and non-contact measurements. ● Reviewed spatiotemporal variability of runway friction and current predictive models based on texture features. ● Summarized and discussed practical maintenance and rehabilitation strategies for improving airport pavement skid performance.