Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Models

The characterization of platoon configuration encompasses three fundamental parameters: the lateral positioning of trucks, the spacing between them, and the total number of trucks within the platoon. The quantification of pavement damage stands as a paramount concern for roadway agencies, which is pivotal for the formulation of effective maintenance and rehabilitation strategies, ensuring the prolonged serviceability of roadways. Consequently, the development of a comprehensive framework capable of calculating pavement distresses as a direct function of these parameters becomes imperative. Therefore, the objective of this study is to introduce an innovative framework tailored to the investigation of pavement damage induced by truck platooning: (1) developing a new framework to simulate repetitive loading and predict accumulating pavement responses, including rutting prediction via a mechanistic model, and (2) proposing a physics-guided artificial intelligence (AI) model to predict pavement responses using the extensive 3D pavement finite element (FE) response database.

• Record URL:
  https://ccat.umtri.umich.edu/research/uiuc/prediction-of-pavement-damage-under-truck-platoons-utilizing-a-combined-finite-element-and-artificial-intelligence-models/

Language

• English

Project

• Status: Active
• Funding: $254996
• Contract Numbers:

  69A3552348305

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of Michigan Transportation Research Institute

  2901 Baxter Road
  Ann Arbor, Michigan  United States  48109
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of Illinois, Urbana-Champaign

  Illinois Center for Transportation
  1611 Titan Drive
  Rantoul, IL  United States  61866
• Principal Investigators:

  Al-Qadi, Imad

• Start Date: 20230601
• Expected Completion Date: 20240930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Connected vehicles; Finite element method; Pavement cracking; Rutting; Traffic platooning; Truck traffic
• Subject Areas: Highways; Maintenance and Preservation; Motor Carriers; Pavements; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01906156
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3552348305
• Files: UTC, RIP
• Created Date: Jan 28 2024 12:35PM