Implementing an Advanced Friction Testing Program for Seamless Coverage of INDOT System

Details

• Creators:
  Tao, Chengcheng ; Lin, Yizhou ; Duan, Junyi ; Peng, Cheng ; Bao, Jieyi ; Jiang, Yi ; Li, Shuo
• Corporate Creators:
  Purdue University. Joint Transportation Research Program
• Corporate Contributors:
  Indiana Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Data Analysis Durability Finite Element Method Friction Implementation Infrastructure LWST Machine Learning Performance Road Markings SCRIM Testing Transportation Safety

  More +
• Publication/ Report Number:
  FHWA/IN/JTRP-2025/22
• DOI:
  https://doi.org/10.5703/1288284317908
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Indiana
• Edition:
  Final Report
• Corporate Publisher:
  Purdue University. Joint Transportation Research Program
• Abstract:
  The interaction between tires and the road surface is crucial for maintaining vehicle stability and safety. The frictional forces at the tire-road interface, along with the coefficient of friction, affect vehicle dynamics, influencing transportation safety. Rapid advancements in transportation technology, infrastructure demands, and safety regulations pose complex transportation challenges, requiring adaptive solutions. The goal of this study is to provide actionable insights for highway agencies, offering improved friction assessment methodologies that support safer and more effective roadway maintenance and design. This study investigates and verifies Locked Wheel Skid Tester (LWST) friction measurement on horizontal curves using Sideway-force Coefficient Routine Investigation Machine (SCRIM), pavement friction performance adjustment factor through a numerical approach incorporating thermomechanical finite element analysis (FEA), and advanced pavement marking assessments. The research aims to enhance friction measurement accuracy and improve roadway safety by integrating predictive modeling and field validation. Machine learning algorithms refine the interpretation of friction data, enabling the development of adjustment factors for various road geometries and speeds. Additionally, optimized pavement marking materials, including angular glass beads and ceramic particles, are explored to enhance long-term durability and friction performance.
• Format:
  PDF
• Funding:
  SPR-4940
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:4b06055e9e1c9bb09ff25a27605af3a84e53a197757d959f371dfe0d41186ddfc73dde85ee937ec441687e3eba902c09330aa6d51d6a6ca444ead9599273df11
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/90734/dot_90734_DS1.pdf
• File Type:
  [PDF - 16.69 MB ]