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AI Applications for Developing Contractors Prequalification Criteria and Safety Matrix for NCDOT

Different state DOTs have attempted to use safety leading indicators for heavy construction projects. The North Carolina Department of Transportation (NCDOT) has contractors’ prequalification guidelines and a formal process for constructability review meetings. However, the safety component is not much considered, which may result in disruption of construction activities due to potential site safety violations. The current prequalification process for contractors requires multiple changes to improve the contractors’ selection process and secure a safe job site. Prequalification process may be revised to incorporate a safety scoring system that considers the contractors history, Occupational Safety and Health Administration (OSHA) citations, availability of in-house safety plans and safety audits, availability of competent persons and OSHA trainers, and formal safety training attended by the contractor’s company personnel. Similarly, NCDOT formal CR meeting needs to incorporate a safety matrix to be revised by project stakeholders during the meeting. The safety matrix should tie different site activities – listed in NCDOT standard bid template – with the respective OSHA big four construction hazard. Performance-based contractor prequalification allows NCDOT personnel to evaluate contractors past performance on similar work to ensure high-quality work in future projects. Occupational safety and health criteria are somehow included in such prequalification to limit construction site accidents and violations. However, current matrix/safety prequalification criteria have significant shortfalls. In this proposed research (implementation plan), the project research team will focus on utilizing artificial intelligence (AI) and machine learning (ML) applications to decide on the attributes relevant to job site safety and the most important parameters to be used in contractors’ prequalification procedures. The research tasks will use the developed criteria through the current project phase, collect information from NCDOT, other state DOTs, and OSHA to train a machine learning model to be developed specifically to evaluate the correlation between different attributes and possible “future” performance of the contractor. Attributes include – but not limited to – contractor’s size, budget, safety practices, previous experience, employee turnover, OSHA records, and experience modification rating. Predictive analytics will provide NCDOT personnel with proper algorithm to assess/predict possible contractor’s performance. The outcomes of this research will result in seamless flow of construction site activities, reduced accidents, and avoiding arbitration and litigation.

Language

  • English

Project

  • Status: Active
  • Funding: $77,500.00
  • Contract Numbers:

    TT 2027-04

  • Sponsor Organizations:

    North Carolina Department of Transportation

    Research and Development
    1549 Mail Service Center
    Raleigh, NC  United States  27699-1549
  • Managing Organizations:

    North Carolina Department of Transportation

    Research and Development
    1549 Mail Service Center
    Raleigh, NC  United States  27699-1549
  • Project Managers:

    Gunthrop, Logan

  • Performing Organizations:

    East Carolina University, Greenville

    Department of Technology Systems
    Greenville, NC  United States  27858
  • Principal Investigators:

    Akhnoukh, Amin

  • Start Date: 20260901
  • Expected Completion Date: 20270831
  • Actual Completion Date: 0
  • USDOT Program: Transportation, Planning, Research, and Development

Subject/Index Terms

Filing Info

  • Accession Number: 02004657
  • Record Type: Research project
  • Source Agency: North Carolina Department of Transportation
  • Contract Numbers: TT 2027-04
  • Files: RIP, STATEDOT
  • Created Date: Sep 30 2026 11:09AM