Rapid Assessment of Network-Level Pavement Conditions Using Novel Tools

Details

• Creators:
  Ghos, Sagar ; Ali, Syed Ashik ; Hobson, Kenneth ; Zaman, Musharraf ; Li, Joshua Qiang
• Corporate Creators:
  University of Oklahoma. School of Civil Engineering and Environmental Science
• Corporate Contributors:
  The Southern Plains Transportation Center (SPTC) Region 6 University Transportation Center (UTC) ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Condition Surveys Deflectometers Falling Weight Deflectometers Ground Penetrating Radar Pavement Distress Ratings
• Publication/ Report Number:
  CY1-OU-OSU-TTI-06
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Oklahoma
• Edition:
  Final Report (October 2023 – January 2025)
• Corporate Publisher:
  The Southern Plains Transportation Center (SPTC) Region 6 University Transportation Center (UTC)
• Abstract:
  Efficient network-level pavement condition assessment is essential for optimizing maintenance and rehabilitation strategies. Traditional methods, such as visual inspections and manual distress surveys, are often subjective, time consuming, and inefficient for large-scale pavement management. The focus of this collaborative project was to evaluate tools for rapidly and cost-effectively assessing network-level pavement conditions for Oklahoma. As part of ODOT's engagement in a pooled fund study (TPF-5 (385)), pavement conditions data from I-35 and I-40 in Oklahoma were collected recently using a Traffic Speed Deflectometer (TSD). This study focused on analyzing the TSD data for network-level assessment or rating of the associated pavement. A complementary objective of this study was to collect data from the same pavements using in-house technologies, namely Pave3D 8K available at OSU and an air-coupled Ground Penetrating Radar (GPR) and Fast Falling weight Deflectometer (FFWD) and compare with TSD data. TSD enabled continuous deflection measurements under moving loads, providing a rapid and comprehensive assessment of pavement structural capacity. A total of six pavement sections were selected on the I-35 and I-40 road network in Oklahoma based on the initial TSD rating. These sections were further tested using FFWD, GPR Pave 3D 8K and field investigations. The data from different technologies were used for performing regression analysis using advanced machine learning models. Finally, pavement condition rating parameters and thresholds were proposed for categorizing the pavement sections into good, fair and poor sections. Findings from this research will contribute to the development of a more efficient, data-driven framework for large-scale pavement condition assessment.
• Format:
  PDF
• Funding:
  69A3552348306
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:2cbfaaa1aea9b5637d89b70a562c46f62c2816bddf228668ceba470f84029da68f19dfe92e1f3de52f5628d0066de0026fda059ef718041122186077834e6ecb
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88287/dot_88287_DS1.pdf
• File Type:
  [PDF - 9.40 MB ]