Development of Pavement Performance Models for MDOT: A Neural Network Approach

The continuous investigation into the mechanical properties and rehabilitation management of existing pavements is vital for the sustainability and efficiency of transportation systems. The Mississippi Department of Transportation (MDOT) database, containing over 40 million records, necessitates modernized decision-making models that align with current design methods and materials. In collaboration with the University of Mississippi, MDOT's pavement management program aimed to update outdated Markov transition matrices with advanced Artificial Neural Networks (ANNs). This four-year project focuses on dynamic sequential ANNs to develop performance prediction models for flexible, rigid, and composite pavements, utilizing extensive distress data. Initial results for flexible pavements showed promising outcomes, with robust statistical accuracy measures for rigid pavements (JCP and CRCP) and composite pavements, although further data and calibration are recommended. The developed models, accessible through a user-friendly interface, are expected to improve the prioritization of maintenance and rehabilitation resources, achieving significant time and cost savings while enhancing the overall efficiency of MDOT's pavement management practices.

• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/75840
• Corporate Authors:

  University of Mississippi, University

  Department of Civil Engineering
  University, MS  United States  38677

  Mississippi Department of Transportation

  Jackson, MS  United States  39215-1850

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Yasarer, Hakan
  • 0000-0003-2156-9899
• Publication Date: 2024-6-24

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 57p

Subject/Index Terms

• TRT Terms: Databases; Decision support systems; Neural networks; Pavement management systems; Pavement performance; Predictive models
• Identifier Terms: Mississippi Department of Transportation
• Geographic Terms: Mississippi
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01925596
• Record Type: Publication
• Report/Paper Numbers: FHWA/MDOT-RD-22-298, 12019-00(004)/ 107895-101000
• Files: NTL, TRIS, USDOT, STATEDOT
• Created Date: Jul 26 2024 10:00AM