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Abstract

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, we 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.

How to Cite

Jia, Gaofeng, and Min Li. Development of Age and State Dependent Stochastic Model for Improved Bridge Deterioration Prediction, MPC-24-560. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2024.