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

This research developed a robust, self-learning, probabilistic model to predict the service life of concrete bridge decks and subsequently other infrastructure components. The model originated from the existing performance data for 22,000 bridge decks in the State of Pennsylvania and utilized advanced statistical tools, including Bayesian probabilistic networks. The newly developed tool can allow state departments of transportation to: (1) accurately predict the lifetime of concrete bridge decks, and (2) establish more efficient and accurate management decisions, resulting in increased longevity of the nation’s infrastructure.

• Record URL:
  https://r3utc.psu.edu/files/2021/04/CIAMTIS-PSU-CIAM-UTC-REG10-s.pdf
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/56909
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Pennsylvania State University, University Park

  Department of Civil and Environmental Engineering
  University Park, PA  United States 

  Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS)

  Pennsylvania State University
  University Park, PA  United States  16802

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Guler, S I
  • Radlinska, A
  • 0000-0002-7977-4927
  • Lu, M
  • Hydock, J
• Publication Date: 2021-2-15

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 45p

Subject/Index Terms

• TRT Terms: Bayes' theorem; Bridge decks; Concrete bridges; Deterioration; Forecasting; Probability; Service life
• Geographic Terms: Pennsylvania
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01771711
• Record Type: Publication
• Report/Paper Numbers: CIAM-UTC-REG10, LTI 2021-06
• Contract Numbers: 69A3551847103
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: May 21 2021 10:54AM