Mapping Indication Severity Using Bayesian Machine Learning from Indirect Inspection Data into Corrosion Severity for Decision-making in Pipeline Maintenance

The project will develop a fast, reliable and accurate tool that extracts corrosion severity and real corrosion rates by adapting current Direct Assessment practices. This approach will supplement survey technologies with a slightly broader database of environmental data to prioritize threat severity, and excavation schedule that can minimize their number and related integrity risk.

  • Supplemental Notes:
    • USDOT Research Hub DisplayID 157806


  • English


  • Status: Active
  • Funding: $310000
  • Sponsor Organizations:

    Pipeline and Hazardous Materials Safety Administration

  • Managing Organizations:

    Texas A&M University

    College Staion, TX  United States  77843
  • Project Managers:

    Smith, Robert

  • Start Date: 20190901
  • Expected Completion Date: 20211231
  • Actual Completion Date: 0
  • USDOT Program: Pipeline Safety
  • Subprogram: Threat Prevention

Subject/Index Terms

Filing Info

  • Accession Number: 01869522
  • Record Type: Research project
  • Source Agency: Department of Transportation
  • Files: RIP, USDOT
  • Created Date: Jan 3 2023 1:53PM