Development of an Asphalt Pavement Raveling Detection Algorithm Using Emerging 3D Laser Technology and Macrotexture Analysis

The objective of this study is to develop successful and effective raveling detection, classification, and measurement algorithms using three-dimensional (3D) pavement data and macro-texture analysis, and to comprehensively validate these methods using large-scale, real-world data. The developed algorithms, using 3D pavement data and the accompanying two-dimensional (2D) intensity data, include the following five components: 1) data pre-processing to remove data outliers, detect pavement markings and edge drop-off, and extract the candidate pavement portion for raveling detection; 2) computation of each subsection with the newly developed feature set for raveling analysis; 3) raveling classification using random forest models; post-processing to smooth isolated subsections in the six subsection-based raveling classification outcomes for determining the raveling severity level; and aggregation of the detection outcomes to segment-level raveling measurement and report the raveling condition at the segment level based on highway agencies’ survey practices. Without loss of generality, the developed algorithms have been tested and validated using the pavement condition survey protocol in the Georgia Department of Transportation (GDOT). The algorithms can be extended to other highway agencies’ pavement condition survey protocols by re-training the classification components using corresponding ground truth data. The proposed algorithms demonstrated promising capabilities to automatically detect and measure asphalt pavement raveling. Using the proposed algorithms will, potentially, save manual effort in field surveys, improve data accuracy, and help highway agencies make more informed decisions on pavement maintenance and rehabilitation. The developed algorithms are based on 3D pavement data that had been already collected, which eliminates the need to make additional effort for data collections.

  • Record URL:
  • Supplemental Notes:
    • This NCHRP-IDEA investigation was conducted by Georgia Institute of Technology.
  • Authors:
    • Tsai, Yichang (James)
    • Wang, Zhaohua
  • Publication Date: 2015-12

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Photos; References; Tables;
  • Pagination: 50p
  • Serial:
  • Publication flags:

    Open Access (libre)

Subject/Index Terms

Filing Info

  • Accession Number: 01593950
  • Record Type: Publication
  • Report/Paper Numbers: NCHRP IDEA Project 163
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 18 2016 2:57PM