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Evaluation of permeability of Superpave asphalt mixtures Mohammad, Louay N ; Herath, Ananda ; Huang, Baoshan

By: Contributor(s): Publication details: Transportation Research Record, 2003Description: nr 1832, s. 50-8Subject(s): Bibl.nr: VTI P8169:2003 Ref ; VTI P8167Location: Abstract: The presence of water in a pavement system is detrimental to its life. Permeable asphalt concrete pavement structures are vulnerable to stripping, which causes premature damage under heavy traffic. To assess the permeability of asphalt mixtures, a research study was conducted at the Louisiana Transportation Research Center (LTRC). Laboratory permeability tests were performed on field cores taken from 17 Superpave projects in Louisiana. An LTRC-modified version of Karol-Warner's falling-head permeameter was used to conduct the permeability test. A sensitivity analysis was performed to relate the permeability test results to mixture volumetric properties such as air void content, compaction effort, mixture gradation, and lift thickness. A statistical regression model was developed to predict the permeability of Superpave mixtures from the mixture volumetric properties. The model successfully predicted the coefficient of permeability of asphalt mixtures from a separate data set that was not included in the model development.
Item type: Reports, conferences, monographs
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The presence of water in a pavement system is detrimental to its life. Permeable asphalt concrete pavement structures are vulnerable to stripping, which causes premature damage under heavy traffic. To assess the permeability of asphalt mixtures, a research study was conducted at the Louisiana Transportation Research Center (LTRC). Laboratory permeability tests were performed on field cores taken from 17 Superpave projects in Louisiana. An LTRC-modified version of Karol-Warner's falling-head permeameter was used to conduct the permeability test. A sensitivity analysis was performed to relate the permeability test results to mixture volumetric properties such as air void content, compaction effort, mixture gradation, and lift thickness. A statistical regression model was developed to predict the permeability of Superpave mixtures from the mixture volumetric properties. The model successfully predicted the coefficient of permeability of asphalt mixtures from a separate data set that was not included in the model development.

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