Enhancement of Network Level Macrotexture Measurement Practices through Deterioration Modeling and Comparison of Measurement Devices for Integration into Pavement Management Systems

dc.contributor.authorMaeger, Kyle Franklinen
dc.contributor.committeechairFlintsch, Gerardo W.en
dc.contributor.committeememberKaticha, Samer Wehbeen
dc.contributor.committeememberHeaslip, Kevin Patricken
dc.contributor.departmentCivil and Environmental Engineeringen
dc.date.accessioned2018-12-14T09:00:27Zen
dc.date.available2018-12-14T09:00:27Zen
dc.date.issued2018-12-13en
dc.description.abstractThis research sought to integrate measurement and prediction of surface macrotexture for use in pavement management systems. This was achieved through two experiments, the first modeled the behavior of a binder-rich stone matrix asphalt when subjected to traffic loading using a heavy vehicle simulator to report the effect on pavement macrotexture. The second experiment compared high-speed macrotexture measurement devices on a variety of surfaces and under various operating conditions. The change in macrotexture due to traffic loading showed that as the cumulative load increased, the macrotexture decreased due to bleeding on the pavement's surface. A regression model determined that, on average, the macrotexture's root mean square (RMS) decreased 0.14 mm per million equivalent single axle load applied. A comparison of RMS and mean profile depth (MPD) outputs indicated that RMS was more sensitive to changes in macrotexture due to bleeding. In comparing devices, pairwise device agreement was evaluated using a Limits of Agreement. The results demonstrate good repeatability for each of the devices tested. The agreement analysis showed that not all high-speed devices can be used interchangeably for all pavement surfaces. Data acquisition speed was found to be a factor in macrotexture parameter calculation for two of the devices. The effect of speed was found to be worse on randomly textured surfaces than on transversely textured surfaces.en
dc.description.abstractgeneralThis thesis sought to integrate the collection and prediction of a pavement surface property known as macrotexture for use in the management of pavement networks. This was achieved through two experiments, the first of which modeled the behavior of asphalt concrete with a higher than typical asphalt content when subjected to simulated traffic to determine the effect on pavement macrotexture. The second experiment compared five high-speed macrotexture measurement devices on a variety of pavement surface types and under various operating conditions. The change in macrotexture due to traffic loading showed that as the cumulative traffic increased, the macrotexture decreased due to the asphalt coming out on the surface, referred to as bleeding. For the comparison of measurement devices data were processed using current industry standards. The results demonstrate good repeatability for each of the devices tested. The analysis also showed that not all high-speed devices can be used interchangeably for all pavement surface types. Vehicle speed was found to be a factor for two of the devices. The effect of speed was found to vary by surface type. Finally, vehicle acceleration was shown to influence the parameters produced by one of the devices, demonstrating that care should be taken to gather high-quality datasets for the critical pavement characteristic of macrotexture.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:18023en
dc.identifier.urihttp://hdl.handle.net/10919/86385en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectmacrotextureen
dc.subjectpavementen
dc.subjectsurface propertiesen
dc.titleEnhancement of Network Level Macrotexture Measurement Practices through Deterioration Modeling and Comparison of Measurement Devices for Integration into Pavement Management Systemsen
dc.typeThesisen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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