| dc.description.abstract | Predicting water film thickness (WFT) is essential due to its significant impact on road safety, as reduced skid resistance caused by hydroplaning is strongly associated with increased vehicle crash rates. Tining is a widely employed surface texture for concrete pavements. However, existing WFT models, primarily developed for asphalt pavement and broom-finished concrete, often fail to provide accurate predictions for tining concrete. This study introduces a reliable prediction model tailored for tining concrete surfaces, considering WFT measurements from three test slabs, including a smooth surface. Tining surfaces with 16 and 25 mm spacing were analyzed under various conditions, including pavement slope (0%–10%), rainfall intensity (0–130 mm/h), and drainage path length (0–5 m). This statistical model, referred to as the Gangneung-Wonju National University (GWNU) model, was developed to predict WFT as a function of pavement slope, rainfall intensity, drainage path length, and mean texture depth. The GWNU model demonstrated consistent accuracy in predicting WFT for tining concrete pavements, whereas existing models, designed for asphalt concrete, significantly underestimated WFT, particularly on tining surfaces with 16 and 25 mm spacing. | |
