Time-Based Modeling of Concrete Bridge Deck Deterioration Using Probabilistic Models

The goal of the research is to develop a robust, self-learning, probabilistic model to predict the service life of concrete bridge decks, and subsequently other infrastructure components. The model will originate from the existing performance data for 22,000 bridge decks in the state of Pennsylvania and will utilize advanced statistical tools, including machine learning systems and Bayesian probabilistic networks. The newly developed tool will allow State Departments of Transportation to A) accurately predict the lifetime of concrete bridge decks and B) establish more efficient and accurate management decisions, resulting in an increased longevity of the Nation’s infrastructure.

Project

• Status: Active
• Funding: $Federal $101,432 Match $101,432
• 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:

  Center for Integrated Asset Management for Multi-Modal Transportation Infrastructure Systems

  Pennsylvania State University
  University Park, PA  United States  16802
• Project Managers:

  Donnell, Eric

  Rajabipour, Farshad

• Performing Organizations:

  Pennsylvania State University, University Park

  Thomas D. Larson Pennsylvania Transportation Institute
  Research Office Building
  University Park, PA  United States  16802-4710
• Principal Investigators:

  Guler, Ilgin

  Radlinska, Aleksandra

• Start Date: 20190301
• Expected Completion Date: 20200529
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Bridge decks; Bridge management systems; Concrete bridges; Deterioration; Forecasting; Machine learning; Probability; Service life
• Geographic Terms: Pennsylvania
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01698494
• Record Type: Research project
• Source Agency: Center for Integrated Asset Management for Multi-Modal Transportation Infrastructure Systems
• Files: UTC, RiP
• Created Date: Mar 4 2019 4:12PM