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The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.

Title:

Using High-Speed Texture Measurements to Improve the Uniformity of Hot-mix Asphalt
Authors:
Flintsch, Gerardo W.
León Izeppi, Edgar David de,
Kevin K. McGhee
Kevin K. McGhee
Year: 2003
VTRC No.: 03-R12
Abstract: This study introduces Virginia's efforts to apply high-speed texture measurement as a tool to improve the uniformity of hot-mix asphalt (HMA) pavements. Three approaches for detecting and quantifying HMA segregation through measuring pavement surface macrotexture were evaluated: (1) applying the methods proposed in NCHRP Report 441, which build on the ability to predict the expected "non-segregated" macrotexture; (2) using acceptance bands for texture similar to those used for HMA density; and (3) considering the standard deviation of the macrotexture as a measure of construction uniformity. Based on the findings from a series of field tests, the researchers concluded that macrotexture measurement holds great promise as a tool to detect and quantify segregation for quality assurance purposes. None of the available equations for predicting non-segregated macrotexture (the approach in NCHRP Report 441) was found to work for all the construction projects evaluated. Additional information is necessary to establish target macrotexture levels. The acceptance bands approach produced reasonable results in most of the field-verification experiments, but it was significantly influenced by the actual variability within the section. An approach that used target levels of standard deviations was selected for further testing and implementation on a pilot basis.