Development of an AI-Powered Dynamic Modulus Test with a Low-Cost Loading Frame

The dynamic modulus (E*) of asphalt mixtures is essential for Mechanistic-Empirical (ME) pavement designs but is seldom measured by the departments of transportation (DOTs) because of the high cost and complexity of traditional E* tests. This research introduces an Artificial Intelligence (AI)-powered IDEAL-E* test that integrates IDEAL cracking test at various temperatures, finite element analysis, and machine learning. First, the Wiczak E* model was used to generate the E* dataset using a full factorial combination of variables (asphalt binder, aggregate gradations, binder content by volumes, and air voids) and ranges of those variables. That resulted in a total of 11,220 mixtures. The AI model for force-displacement and E* was then trained on 8,976 of those mixtures, and the remaining 2,244 mixtures were used to test the AI model's accuracy. Additionally, the AI model was calibrated and verified with 16 mixtures. The comparison between AMPT-measured and AI-predicted E* values is highly promising with a R² value of 0.97. This innovative approach addresses the limitations of existing models that struggle with evolving asphalt compositions. By simplifying E* data generation, it facilitates its use in AASHTOWare Pavement ME design software and is aligned with current practices with IDEAL cracking tests in DOTs’ Quality Assurance laboratories.

• Record URL:
  • http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=5417

Language

• English

Project

• Status: Completed
• Funding: $135,000.00
• Contract Numbers:

  Project 20-30, IDEA 242

• Sponsor Organizations:

  National Cooperative Highway Research Program

  Transportation Research Board
  500 Fifth Street, NW
  Washington, DC  United States  20001

  American Association of State Highway and Transportation Officials (AASHTO)

  444 North Capitol Street, NW
  Washington, DC  United States  20001

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Managing Organizations:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Project Managers:

  Jawed, Inam

• Performing Organizations:

  Texas A&M Transportation Institute

  ,    
• Principal Investigators:

  Zhou, Fujie

• Start Date: 20230101
• Expected Completion Date: 20250331
• Actual Completion Date: 20250331

Subject/Index Terms

• TRT Terms: Artificial intelligence; Asphalt mixtures; Dynamic modulus of elasticity; Test procedures; Viscoelasticity
• Subject Areas: Design; Highways; Pavements;

Filing Info

• Accession Number: 01865730
• Record Type: Research project
• Source Agency: Transportation Research Board
• Contract Numbers: Project 20-30, IDEA 242
• Files: TRB, RIP
• Created Date: Nov 29 2022 8:53AM