Abstract
AbstractPerformance of asphalt pavements depends on the quality of compaction achieved during construction. Asphalt cores as an indicator of construction quality are not reliable because they typically cover less than 1% of the constructed pavement. Intelligent compaction (IC) estimates the level of compaction of the entire pavement layer during construction. IC rollers are equipped with accelerometers for measuring vibration, a GPS for monitoring spatial location, a temperature sensor for measuring surface temperature and an on-board computer for real-time execution of software and data storage. Although IC shows great promise as a quality control tool, there are concerns regarding the quality and analysis of data including missing data, data accuracy, data filtering, and data interpretation. Also, verifying compliance of the IC output with Department of Transportation (DOT) requirements needs accurate project boundaries. Project boundaries obtained from the onboard GPS may not be adequate for verifying compliance. In this study, the IC data from three pilot projects in Oklahoma were analyzed using the VETA (v 5.1) software, which is a map-based tool for viewing and analyzing IC data. Three different IC providers were used in collecting these IC data. A high degree of variability in collected data was observed, including inconsistent file naming, unspecified target for number of roller passes and inadequate layout of project boundaries. Despite variability, coverage, number of roller pass, compaction temperature, roller speed and roller frequency were found useful as indicators of compaction quality. Project size and operator training were also found to be important factors for successful implementation of intelligent compaction as a quality control tool.KeywordsVETA softwareData qualityData filteringProject boundaryData compliance
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