<p>Pavement macrotexture contributes
greatly to road surface friction, which in turn plays a significant role in
reducing road incidents. Conventional methods for macrotexture measurement
techniques (e.g., the sand patch method, the outflow method, and laser
measuring) are either expensive, time-consuming, or of poor repeatability. This
thesis aims to develop and evaluate affordable and convenient alternative
approaches to determine pavement macrotexture. The proposed solution is based
on multi-view smartphone images collected in situ over the pavement. Computer
vision techniques are then applied to create high resolution three-dimensional
(3D) models of the pavement. The thesis develops the analytics to determine two
primary macrotexture metrics: mean profile depth and aggregation loss.
Experiments with 790 images over 25 spots of three State Roads and 6 spots of
the INDOT test site demonstrated that the image-based method can yield reliable
results comparable to conventional laser texture scanner results. Moreover, based
on experiments with 280 images over 7 sample plates with different aggregate
loss percentage, the newly developed analytics were proven to enable estimation
of the aggregation loss, which is largely compromised in the laser scanning
technique and conventional MPD calculation approach. The root mean square
height based on the captured images was verified in this thesis as a more
comprehensive metric for macrotexture evaluation. It is expected that the
developed approach and analytics can be adopted for practical use at a large
scale. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/11324177 |
| Date | 14 January 2021 |
| Creators | Xiangxi Tian (8086718) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/IMAGE-BASED_ROAD_PAVEMENT_MACROTEXTURE_DETERMINATION/11324177 |
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