Development of Age and State Dependent Stochastic Model for Improved Bridge Deterioration Prediction

Reliable and accurate assessment and prediction of bridge condition deterioration is critical for effective bridge preservation. Deterioration models, combined with current condition information, can guide inspection, maintenance, repair, and rehabilitation planning, and support risk and life-cycle analysis. Existing Markov deterioration models, which assume stationary transition probabilities, often fail to capture the non-homogeneous nature of bridge deterioration influenced by factors such as age, current condition, climate, protective systems, and traffic. This project develops a general age, state, and environment dependent stochastic deterioration model that accounts for these variables. For this purpose, non-homogeneous Markov deterioration models with time-variant transition probabilities are developed. Surrogate models are used to relate these probabilities to explanatory variables such as age, current bridge condition, and operation environmental factors. A Bayesian approach is employed to calibrate the model using bridge inspection and environmental data. By establishing non-homogeneous Markov models, the authors can better predict bridge conditions. The proposed approach is applied to deterioration modeling for bridges in Colorado. Deterioration models are developed for different types of bridges and different bridge components. Comparisons with existing models showed the advantages of the proposed model.

• Record URL:
  • https://www.ugpti.org/resources/reports/details.php?id=1204
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/78891
• Summary URL:
  • https://rosap.ntl.bts.gov/view/dot/79673
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Colorado State University, Fort Collins

  Department of Civil and Environmental Engineering
  Fort Collins, CO  United States  80525

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Jia, Gaofeng
  • Li, Min
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; References; Tables;
• Pagination: 35p

Subject/Index Terms

• TRT Terms: Bayes' theorem; Bridges; Deterioration; Markov processes; Predictive models; Stochastic processes
• Geographic Terms: Colorado
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01940008
• Record Type: Publication
• Report/Paper Numbers: MPC-536, MPC 24-560
• Files: UTC, NTL, TRIS, USDOT
• Created Date: Dec 17 2024 9:04AM