Enhancing Network-Level Pavement Macrotexture Assessment

Pavement macrotexture has been shown to influence a range of safety and comfort issues including wet weather friction, splash and spray, ambient and in-vehicle noise, tire wear, and rolling resistance. While devices and general guidance exist to measure macrotexture, the wide-scale collection and use of macrotexture is neither mandated nor is it typically employed in the United States. This work seeks to improve upon the methods used to calibrate, collect, pre-process, and distill macrotexture data into useful information that can be utilized by pavement managers. This is accomplished by 1. developing a methodology to evaluate and compare candidate data collection devices; 2. plans and procedures to evaluate the accuracy of high-speed network data collection devices with reference surfaces and measurements; 3. the development of a method to remove erroneous data from emerging 3-D macrotexture sensors; 4. development of a model to describe the change in macrotexture as a function of traffic; 5.finally, distillation of the final collected pavement surface profiles into parameters for the prediction of important pavement surface properties aforementioned. Various high-speed macrotexture measurement devices were shown to have good repeatability (between 0.06 to 0.09mm MPD) and interchangeability of single-spot laser dfevices was demonstrated via a limits of agreement analysis. The operational factors of speed and acceleration were shown to affect the resulting MPD of several devices and guidelines are given for vehicle speed and sensor exposure settings. Devices with single spot and line lasers were shown to reproduce reference waveforms on manufactured surfaces within predefined tolerances. A model was developed that predicts future macrotexture levels (as measured by RMS) for pavements prone to bleeding due to rich asphalt content. Finally, several previously published macrotexture parameters along with a suite of novel parameters were evaluated for their effectiveness in the prediction of wet weather friction and certain types of road noise. Many of the parameters evaluated outperformed the current metrics of MPD and RMS.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Civil Engineering

Department dc:contributor.department

Civil and Environmental Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2019

Author dc:creator

• Bongioanni, Vincent Italo

Chair dc:contributor.committeechair

• Flintsch, Gerardo W.

Committee members dc:contributor.committeemember

• Izeppi, Edgar D. de Leon
• Ferris, John B.
• Trani, Antonio A.

• macrotexture
• friction
• noise
• outliers
• dropouts
• Accelerated Pavement Testing
• Surface Properties
• Agreement
• Repeatability
• calibration
• accuracy
• parameters

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:19681

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/89326