Existing methods for bridge condition deterioration prediction are categorized into deterministic models, stochastic models, and artificial intelligence models. Deterministic models describe the deterioration of bridge components as a fixed relationship with selected independent variables. While linear regression models and nonlinear regression models are commonly employed as deterministic models, they lack the accuracy needed for the long-term prediction of bridge condition performance and do not effectively represent the deterioration of bridge conditions over time. Consequently, nonlinear regression models are generally preferred. Deterministic modeling is a common approach in the prediction of bridge conditions. [PERSON] et al. (2016) developed a cubic polynomial regression model for fitting and prediction based on the inspection data of 2601 bridges, and this model was found to predict bridge conditions better. [PERSON] et al. (2016) used regression modeling to construct a model for predicting bridge conditions based on different bridge characteristics. However, deterministic models lead to different results for different models due to varying assumptions, even when the same data are used. [PERSON] et al. (2017) compared the predictive abilities of different regression models for highway bridge condition deterioration. Although this method is the simplest for predicting the future condition of a bridge, obtaining the best-fit functional form is time-consuming and labor-intensive. A stochastic model assumes that the bridge condition rating is a stochastic process, taking into account inherent uncertainty and variability due to unobserved explanatory variables, measurement errors, and the randomness of the deterioration process. The Markov method, which assumes that bridge deterioration is a stochastic process based on a Markov chain, is the most common probabilistic model and is widely used in the simulation of bridge performance deterioration. The Markov method describes state deterioration in terms of probabilities--that is, the transition probabilities from one condition state to another during an inspection interval. Markov models are capable of predicting the condition of bridges in different situations. [PERSON] et al. (2016) proposed a dynamic Markov bridge failure prediction model, through which they constructed the relationship between bridge components and were able to identify the components that significantly affect the condition of the bridge. [PERSON] et al. (2017) constructed a Markov model considering the maintenance situation and predicted the condition of the entire bridge and each part of it. Markov chain theory relies on two assumptions: memorylessness (i.e., the future state of the process depends only on the current state) and homogeneity (i.e., the transition probability from one state to another remains constant throughout the entire life cycle). [PERSON] et al. (2017) adopted Markov chains to predict the bridge deterioration process and statistically determined the optimal inspection interval for bridges. [PERSON] et al. (2017) proposed a stochastic Markov chain model to predict the stress condition of timber bridge members. Three methods of Markov modeling using state data--including the percentage prediction method, a regression method based on optimization, and a nonlinear optimization technique to predict the transition probability matrix--were compared in order to select the optimal Markov method for predicting the deterioration of timber bridges. [PERSON] et al. (2017) analyzed a variety of Markov models for predicting bridge deterioration and found variability among different models, then proposed a combined deterioration model to improve prediction accuracy. Moreover, with the advancement of computer science, artificial intelligence techniques have been widely applied to the field of bridge engineering, and bridge deterioration prediction is no exception. [PERSON] et al.