Prediction Model on Permeability Coefficient of Porous Asphalt Concrete under Repeated Clogging Based on Void Characteristic Parameters
Porous asphalt concrete (PAC) is commonly applied in locations with heavy rainfall. However, because of the mix’s characteristics and service environment, it is impossible to ensure the duration of its permeability performance. This paper explores the aspects that influence PAC’s permeability performance. A comprehensive clogging model is also developed, which includes PAC mix parameters. First, three clogging materials were produced. Second, the effect of nominal maximum aggregate size, porosity, and clogging material on PAC’s permeability is discussed. Finally, models are proposed to predict PAC’s clogging factor β and clogging times N. The results revealed that the remaining PAC mix parameters, except for the nominal maximum aggregate size, were associated strongly with the β and N. The prediction models established for β and N in PAC mixes were highly reliable, with correlation values over 0.90 in all cases. In addition, the mixture parameters that had the greatest influence on the clogging factor β and clogging times N are differed. The uniformity and curvature coefficients influenced the clogging factor β the most, while the initial permeability coefficient affected the clogging times N the most.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/08991561
-
Supplemental Notes:
- © 2023 American Society of Civil Engineers.
-
Authors:
- Li, Bo
- Kong, Yanhe
- Zhu, Xuwei
- Wei, Dingbang
- Han, Jie
- Publication Date: 2023-5
Language
- English
Media Info
- Media Type: Web
- Pagination: 04023082
-
Serial:
- Journal of Materials in Civil Engineering
- Volume: 35
- Issue Number: 5
- Publisher: American Society of Civil Engineers
- ISSN: 0899-1561
- EISSN: 1943-5533
- Serial URL: http://ascelibrary.org/journal/jmcee7
Subject/Index Terms
- TRT Terms: Air voids; Asphalt concrete; Permeability coefficient; Porous materials; Predictive models
- Subject Areas: Highways; Hydraulics and Hydrology; Materials; Pavements;
Filing Info
- Accession Number: 01877567
- Record Type: Publication
- Files: TRIS, ASCE
- Created Date: Mar 28 2023 9:56AM