Integration and Deployment of Novel Tools for Rapid Assessment of Pavement Conditions and Remaining Life

Details

• Creators:
  Dorsett, Garrett ; Scullion, Tom
• Corporate Creators:
  Texas Transportation Institute
• 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:
  Forecasting Machine Learning Neural Networks Pavement Management Systems Pavement Performance Roughness Service Life
• Publication/ Report Number:
  CY1-TTI-01
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report, September 2023- September 2024
• Corporate Publisher:
  The Southern Plains Transportation Center (SPTC) Region 6 University Transportation Center (UTC)
• Abstract:
  Extreme heat events expose pavements to more intense and prolonged stress. These events, coupled with soil movement from heavy rainfall and drought also threaten pavement functional and structural life. State and local agencies have benefited from innovative technologies and tools developed to rapidly assess pavement condition, but data has not yet been widely implemented into pavement management systems that provide cohesive, wholistic assessments of state roadways. A hybrid approach combining traffic speed deflectometer (TSD) and air coupled ground penetrating radar (GPR) is examined that will allow for large-scale data collection and analysis for use in state pavement management systems. The ideas presented in this study outline best practices for leveraging deflection data produced by TSD at the network- and project-levels, including merging TSD and GPR field data to enhance pavement engineers’ understanding of in-situ pavement structural conditions. These strategies were applied in the field and greatly assisted in identifying failed and at-risk pavements and prioritizing future projects. The remaining life estimation and material modulus back calculation methods produced in this study will continue to be modified and improved to better match predictions generated by existing state practices.
• Format:
  PDF
• Funding:
  69A3552348306
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:e6d258ef3fc8f6e9d58b1f4bc784beefde4f47aa080dca7efc8e3954ae3f895f791ee14eb1eaee3a5a5af84ba6323e640c53fe4e81fca42707b66674ee366a1d
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88280/dot_88280_DS1.pdf
• File Type:
  [PDF - 7.86 MB ]