Technical Degradation Prediction of Bridge Components Based on Semi-Markov Degradation Model

To overcome the limitations of traditional Markov bridge degradation prediction models, which fail to consider the interactions between different component degradation mechanisms and struggle to accurately capture the true degradation conditions of bridge components, introducing an improved version of the traditional Markov model by incorporating the Weibull distribution. This enhancement results in a semi-Markov model that offers a probability distribution for predicting the technical condition of bridge components. Taking advantage of periodic inspection data from a highway section in Shandong Province, China. With this data, the states of bridge components are defined in the semi-Markov degradation model. The improved semi-Markov model integrates a two-parameter Weibull distribution and involves determining the parameters of the Weibull distribution, transition probability matrix, and state distribution vector. The semi-Markov degradation model, in contrast to commonly used Markov degradation models, accounts for both the state and duration of each state, resulting in significantly more accurate predictions of the degradation process of bridge components, achieving a prediction accuracy of 96%. The developed semi-Markov bridge degradation model facilitates the timely detection of changes in the technical condition of bridge components by updating the transition probability matrix according to variations in the duration of each state, thereby improving the efficiency of subsequent bridge maintenance decision-making.