Water Film Depth Prediction Model for Highly Textured Pavement Surface Drainage

Abstract

Water film depth (WFD) is an important factor for road traffic safety because of its direct connection with skid resistance, hydroplaning speed, and the tendency of splash and spray. Increasing the pavement macrotexture reduces WFD. However, existing models for WFD prediction have not been developed on highly textured surfaces such as chip seal. Furthermore, the rainfall intensities used for developing most of these models were relatively low, leaving no or low WFD on chip seal surfaces. To propose a WFD prediction model suitable for highly textured surfaces and to consider the effect of surface material type, an experimental study was conducted with 154 different combinations of mean texture depth (MTD), surface material type, surface slope, drainage length, and rainfall intensity. The tests were carried out on chip seal specimens using a full-scale rainfall simulator. Test results from 1,784 WFD readings indicated that the Gallaway and PAVDRN models were not accurate for highly textured surfaces used in this study with MTD ranging from 0.05 to 0.20 in. Two experimental models were, therefore, proposed to predict the WFD; both models displayed a significantly higher correlation between the measured and predicted WFD compared with the existing models. Furthermore, the eco-friendly rubberized chip seal showed an enhanced drainage capability compared with conventional chip seal, especially in low slopes, because of the hydrophobic nature of crumb rubber versus the hydrophilic character of mineral aggregates. Accordingly, the proposed model incorporated a term to consider the effect of surface material type.

Department(s)

Civil, Architectural and Environmental Engineering

Comments

The work in this research project was funded by the Missouri Department of Natural Resources (MoDNR).

Keywords and Phrases

Chip Seals; Classification Description: Infrastructure; Infrastructure Management and System Preservation; Pavement Maintenance

International Standard Serial Number (ISSN)

2169-4052; 0361-1981

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2022 National Academy of Sciences: Transportation Research Board, All rights reserved.

Publication Date

01 Feb 2022

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