Prediction of pavement water film depth and estimation of critical rainfall conditions for refined road safety management: A simulation study.

The development of a smart expressway ensuring all-weather safe access represents the future trajectory of transportation infrastructure. A key task in this advancement is the precise prediction of water film depth (WFD) on road surfaces. Conventional WFD prediction models often assume constant grade and cross slope, an oversimplification that may affect predictive accuracy. In this study, typical highway alignments were meticulously modeled in three dimensions (3D) using Building Information Modeling (BIM) technology, and WFD simulations were conducted using a coupled discrete phase model and Eulerian wall film model (DE-WFD model). Simulation results revealed that the DE-WFD model consistently predicts higher WFD compared to the RRL and PAVDRN models. In contrast, its predictions are approximately 0.12 mm (40%) lower than those of the Gallaway model when rainfall intensity is below 7.8 mm/h. At higher rainfall intensities, DE-WFD predictions closely align with the Gallaway model. Field tests conducted with a feeler gauge of 0.01 mm resolution confirmed the accuracy of these predictions, showing a maximum deviation of just 7% between predicted and measured values. Additionally, the study assessed the sensitivity of the DE-WFD model to variations in grade and cross slope along the road length. Results indicated that on road surfaces employing dispersed drainage, WFD is approximately 6% higher at sag vertical curves and lower at crest vertical curves compared to constant slope segments. Moreover, WFD increases by over 35% at superelevation transitions. To quantify the impact of rainfall on road safety, a critical WFD parameter was developed. This parameter defines the maximum WFD under specific rainfall conditions that reduces the pavement-tire tangential friction coefficient to a level corresponding to the standard stopping sight distance. Using the DE-WFD model, simulations of hourly rainfall intensity and duration identified conditions under which WFD reaches this critical value for various roadway geometries. These findings provide valuable references for the precision management of highway operational safety. This suggests that traffic safety authorities should implement warning and intervention measures when critical rainfall conditions are exceeded to ensure driving safety. Copyright: © 2025 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation

Jinliang Xu, Wenzhen Lv, Chao Gao, Tian Xin, Xiantong Liu, Yahao Hou. Prediction of pavement water film depth and estimation of critical rainfall conditions for refined road safety management: A simulation study. PloS one. 2025;20(2):e0318228

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PMID: 39946366

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