Develop a Methodology for Pavement Drainage System Rating

Details

• Creators:
  Chen, Qiming ; Liu, Jun
• Corporate Creators:
  Louisiana Transportation Research Center
• Corporate Contributors:
  Louisiana. Department of Transportation and Development ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Ditches Drainage Index Tests Pavements Pavement Surface Course Ratings Roadside
• Publication/ Report Number:
  FHWA/LA.25/719 ; 24-2P
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Louisiana
• Edition:
  Final Report: June 2024 - November 2025
• Corporate Publisher:
  Louisiana Transportation Research Center
• Abstract:
  Effective drainage is critical for preserving pavement integrity and extending service life, yet network-level methods for evaluating pavement drainage conditions remain limited. This study presents a practical methodology for assessing pavement drainage conditions using data from the Louisiana Department of Transportation and Development’s (DOTD) Pavement Management System (PMS). The proposed framework evaluates three key components: (1) pavement surface drainage based on cross-slope, longitudinal grade, and rutting; (2) roadside/shoulder drainage assessed through edge drop-off data and PMS imagery for erosion, vegetation, and debris; and (3) ditch drainage evaluated through PMS imagery for sediment accumulation, erosion, and obstructions. The methodology was applied to five roadway sections to demonstrate implementation and identify correlations between drainage conditions and pavement performance. Results indicate that the pavement surface drainage rating is strongly correlated with actual pavement performance, suggesting its value as a stand-alone monitoring indicator. Fine-scale analysis (0.1-mi. resolution) proved critical for capturing localized drainage deficiencies that disproportionately affect roadway performance. The framework provides actionable insights for maintenance prioritization and early-stage screening of operational deficiencies, although it does not evaluate hydraulic capacity or broader flood risk. Future enhancements, such as artificial intelligence (AI)-powered image analysis and Light Detection and Ranging (LiDAR)-based ditch surveys, could further improve automation, objectivity, and network-level monitoring. Overall, this study demonstrates a practical and scalable approach for integrating drainage condition assessments into network-level pavement management and supporting data-driven maintenance decisions.
• Format:
  PDF
• Funding:
  DOTLT1000526
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:91d8e5e33f3d7cddb61dde68c38ca0d749d2b80ac878e4c47e34c4d23b84f74f6bbc8733c942d110aefb1671461959dd1755431b50f93a5fb700a79db93df407
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88881/dot_88881_DS1.pdf
• File Type:
  [PDF - 4.42 MB ]