Field Data–Driven Machine-Learning Approach for Estimating Pavement Skid Numbers from LWST Measurements for Improved Asphalt Pavement Safety
- 1 September 2026
- journal article
- Published by American Society of Civil Engineers (ASCE) in Journal of Transportation Engineering, Part B: Pavements
- Vol. 152 (3)
Abstract
No abstract availableThis publication has 27 references indexed in Scilit:
- Learning models for predicting pavement friction based on non-contact texture measurements: Comparative assessmentJournal of Road Engineering, 2025
- Hybrid prediction model for the friction performance of asphalt pavements by combining multi-scale decomposition and transformer networkEngineering Applications of Artificial Intelligence, 2025
- Research on the evolution law of aggregate micro-texture during long-term wearing of asphalt pavementConstruction and Building Materials, 2024
- An investigation of texture-friction relationship with laboratory ring-shaped asphalt mixture specimens via close-range photogrammetryConstruction and Building Materials, 2024
- Predicting pavement condition index based on the utilization of machine learning techniques: A case studyJournal of Road Engineering, 2023
- Comparative studies of RSM Box-Behnken and ANN-Anfis fuzzy statistical analysis for seawater biodegradability using TiO2 photocatalystChemosphere, 2022
- Determination and prediction of pavement skid resistance–connecting research and practiceJournal of Road Engineering, 2021
- Influence of effective texture depth on pavement friction based on 3D texture areaConstruction and Building Materials, 2021
- Laboratory Study on the Frictional Properties of HMA Specimens Using a Newly Developed Asphalt PolisherInternational Journal of Civil Engineering, 2017
- Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: A proof of concept studyAccident Analysis & Prevention, 2016