Deterioration Prediction of Timber Bridge Elements Using the Markov Chain

Abstract

Timber bridges require high accumulated maintenance costs, which can be many times greater than their initial cost. Infrastructure managers need deterioration models to assist with making appropriate decisions concerning repair strategies and program maintenance schedules by accurately predicting the future condition of timber bridge elements. Markov chain–based models have been used extensively in modeling the deterioration of infrastructure facilities. These models can predict the condition of bridge elements as a probabilistic estimate. This paper presents the prediction of future condition of timber bridge elements using a stochastic Markov chain model. Condition data obtained from the Roads Corporation of Victoria, Australia, were used to develop transition probabilities. The percentage prediction method, regression-based optimization method, and nonlinear optimization technique were applied to predict transition matrices and transient probabilities from the condition data. The most suitable deterioration model for timber bridge elements was selected by evaluating the model performances using the goodness-of-fit and reliability tests. It was concluded that the Markov chain developed for deterioration prediction of timber bridges using the nonlinear optimization technique was mathematically acceptable and predicts the deterioration progression with reasonable accuracy.