Abstract
Raveling is one of the most common asphalt pavement distresses that occur on US highway pavements. Raveling results in safety concerns such as loose stones and hydroplaning; poor ride quality and road/tire noise; and shortened pavement longevity. Traditional raveling survey methods involve manual visual inspection, which is time consuming, subjective, and hazardous to highway workers. With the research project competitively selected and sponsored by the National Cooperative Highway Research Program (NCHRP) Innovation Deserving Exploratory Analysis (IDEA) program, the objective of this study is to develop an accurate raveling detection and classification algorithm using 3D pavement data that has become mainstream technologies for state Department of Transportations (DOTs) in the US for pavement condition evaluation, and to comprehensively validate these methods using large-scale, real-world data based on actual transportation agencies’ distress protocol (Severity levels 1, 2, and 3). A total of 65 miles of 3 D pavement data was collected on I-85 and I-285 in Georgia for training and testing. Three supervised machine learning techniques —AdaBoost with decision trees, support vector machine (SVM) and random forests—were developed for the detection and classification of raveling in the collected data. The random forest classifier had the b est performance, with precision values ranging from 75.6% for level 3 raveling to 97.6% for level 0 (no) raveling and recall values ranging from 86.9% for level 1 raveling to 96.1% for level 0 raveling on real world large-scale data. The developed raveling detection and severity level classification method has been successfully implemented to entire Georgia’s interstate highway system with1452.5 survey miles of asphalt pavements after the large-scale validation and refinement. The proposed method for raveling detection can be deployed to other transportation agencies for safer and more efficient assessment of roadway raveling conditions.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Federal Highway Administration, Distress identification manual for the Long-Term Pavement Performance Project. Publication number FHWA-RD-03-031. FHWA, VA, USA, 2014.
Georgia Department of Transportation, Pavement Condition Survey Manual, GDOT, GA, USA, 2007.
K. Zimmerman, Pavement Management Systems: Putting Data to Work. NCHRP Synthesis 501. Transportation Research Board of the National Academies, Washington DC, USA, 2017.
Y. Tsai, F. Li, Critical Assessment of Detecting Asphalt Pavement Cracks under Different Lighting and Low Intensity Contrast Conditions Using Emerging 3D Laser Technology, J. Transp. Eng. 138 (5) (2012) 649–656.
C. Jiang, Y. Tsai, Enhanced Crack Segmentation Algorithm Using 3D Pavement Data, ASCE J. Comput. Civ. Eng. 30 (3) (2015) 04015050.
C. Jiang, Y. Tsai, Z. Wang, Crack Deterioration Analysis Using 3D Pavement Surface Data: A Pilot Study on Georgia State Route 26, Transp. Res. Rec. 2589 (2016) 154–161.
Y. Tsai, F. Li, Y. Wu, A New Rutting Measurement Method Using Emerging 3D Line-Laser Imaging System, Inter. J. Pavement Res. Technol. 6 (5) (2013) 667–672.
Y. Tsai, Z. Wang, F. Li, Assessment of Rut Depth Measurement Accuracy of Point-based Rut Bar Systems using Emerging 3D Line Laser Imaging Technology, J. Marine Sci. Technol. 23 (3) (2015) 322–330.
Y. Tsai, Y. Wu, C. Ai, E. Pitts, Feasibility Study of Measuring Concrete Joint Faulting Using 3D Continuous Pavement Profile Data, ASCE J. Transp. Eng. 138 (11) (2012) 1291–1296.
Y. C. Tsai, Z. Wang, A Remote Sensing and GIS-Enabled Asset Management System (RS-GAMS) Phase 2. Final Report for USDOT project: RITARS -11 -H-GAT. Washington DC, USA, 2014.
Y. Tsai, Z. Wang, Development of an Asphalt Pavement Raveling Detection Algorithm Using Emerging 3D Laser Technology and Macrotexture Analysis. NCHRP IDEA-163 Final Report, National Academy of Science, Washington DC, USA, 2015.
Y. Tsai, A. Chatterjee, Pothole Detection and Classification Using 3D Technology and Watershed Method, ASCE J. Comput. Civ. Eng. 32 (2) (2017) 04017078.
G. Geary, Y. Tsai, Y. Wu, An Area-Based Faulting Measurement Method Using Three-Dimensional Pavement Data, Transp. Res. Rec. 2672 (40) (2018) 41–49.
V. W. Ooijen, V. D. Bol, High-Speed Measurement of Raveling on Porous Asphalt. Symposium on Pavement Surface Characteristics of Roads and Airports, Toronto, Ontario, Canada, 2004.
S. McRobbie, G. Furness, Automated Detection of Fretting on HRA Surfaces. Report no. PPR299. TRL, Berkshire, UK, 2008.
S. McRobbie, J. Iaquinta, A. Wright, P. Trumper, J. Kennedy, Development and Validation of Algorithms for the Automatic Detection of Fretting Based On Multiple Line Texture Data, Research into Pavement Surface Disintegration. Phase 2 Interim Report. Report no. PPR628. TRL, Berkshire, UK, 2012.
P. Scott, K. Radband, M. Zohrabi, P. Sanders, S. McRobbie, A. Wright, Measuring Surface Disintegration (Raveling or Fretting) Using Traffic Speed Condition Surveys, 7th International Conference on Managing Pavement Assets, Alberta, Canada, 2008.
J. Laurent, J. F. Hebert, D. Lefebvre, Y. Savard, Using 3D Laser Profiling Sensors for the Automated Measurement of Road Surface Conditions, 7th RILEM International Conference on Cracking in Pavements, Delft, the Netherlands, 2012.
J. Laurent, J. F. Hebert, D. Lefebvre, Y. Savard, High-Speed Network Level Road Texture Evaluation Using 1mm Resolution Transverse 3D Profiling Sensors Using A Digital Sand Patch Model, 7th International Conference on Maintenance and Rehabilitation of Pavements and Technological Control, Auckland, New Zealand, 2012.
S. Mathavan, M. Rahman, M. Stonecliffe-Jones, K. Kamal, Pavement Raveling Detection and Measurement from Synchronized Intensity and Range Images, Transp. Res. Rec. 2457 (2014) 3–11.
Y. C. Tsai, Z. Wang, A Remote Sensing and GIS-Enabled Asset Management System (RS-GAMS). Final Report for USDOT project: DTOS59-10-H-0003. Washington DC, USA, 2013.
Y. Tsai, F. Li, Detecting Asphalt Pavement Cracks under Different Lighting and Low Intensity Contrast Conditions Using Emerging 3D Laser Technology. ASCE J. Transp. Eng. 138 (5) (2012) 649–656.
C. Jiang, Y. J. Tsai, Enhanced crack segmentation algorithm using 3D pavement data, J. Comput. Civ. Eng. 30 (3) (2015) 04015050.
C. Jiang, Y. Tsai, Z. Wang, Crack Deterioration Analysis Using 3D Pavement Surface Data: A Pilot Study on Georgia State Route 26, Transp. Res. Rec. 2589 (2016) 154–161.
Y. Tsai, F. Li, Y. Wu, A New Rutting Measurement Method Using Emerging 3D Line-Laser Imaging System, Inter. J. Pavement Res. Technol. 6 (5) (2013) 667–672.
Y. Tsai, Z. Wang, F. Li, Assessment of Rut Depth Measurement Accuracy of Point-based Rut Bar Systems using Emerging 3D Line Laser Imaging Technology, J. Marine Sci. Technol. 23 (3) (2015) 322–330.
Y. Tsai, Y. Wu, C. Ai, E. Pitts, Feasibility Study of Measuring Concrete Joint Faulting Using 3D Continuous Pavement Profile Data. ASCE J. Transp. Eng. 138 (11) (2012) 1291–1296.
Y. Tsai, Y. Wu, Z. Lewis, Full-Lane Coverage Micromilling Pavement-Surface Quality Control Using Emerging 3D Line Laser Imaging Technology, J. Transp. Eng. 14 (2) (2014) 04013006.
Y. Tsai, A. Chatterjee, Pothole Detection and Classification Using 3D Technology and Watershed Method, ASCE J. Comput. Civ. Eng. 32 (2) (2017) 04017078.
Author information
Authors and Affiliations
Corresponding author
Additional information
Peer review under responsibility of Chinese Society of Pavement Engineering.
Rights and permissions
About this article
Cite this article
Tsai, YC.(., Zhao, Y., Pop-Stefanov, B. et al. Automatically detect and classify asphalt pavement raveling severity using 3D technology and machine learning. Int. J. Pavement Res. Technol. 14, 487–495 (2021). https://doi.org/10.1007/s42947-020-0138-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42947-020-0138-5