## 4 Conclusions  
During the bridge maintenance stage, the maintenance cycle tends to be lengthy and the costs high. It is observed that the technical condition of a bridge tends to deteriorate over time due to environmental factors and vehicular effects. Maintenance units must determine the maintenance technology plan and set priorities based on the technical condition level of the bridge. Furthermore, they should implement preventive maintenance measures to enhance the bridge's long-term performance. This necessitates an accurate prediction of the bridge's future technical condition level. In this study, a novel method is proposed to predict bridge condition ratings by combining the Elman neural network and the Markov chain, considering the reasonable selection of features that affect the technical condition level of the bridge. The following conclusions can be drawn based on the work completed:  
(1) The ReliefF algorithm can be effectively used for the extraction of bridge features. From the NBI bridge feature dataset spanning 1992-2022 in Texas, the bridge feature data are analyzed using the ReliefF algorithm. This analysis identifies six significant bridge features, including bridge age, average daily truck traffic, concrete material type, the skew angle, deck structure type, and the type of bridge.  
(2) The original feature database is reclassified according to the extracted features and then input into the Elman neural network. Through the training, validation, and testing of the Elman neural network, the optimal bridge condition rating curves within a 60-year timeframe are achieved. Concurrently, the transition probability matrix for the Markov chain over 60 years is determined through iterative calculations using a genetic algorithm. The results reveal that the Markov prediction curve closely aligns with the predictions from the Elman neural network.  
(3) The prediction model is validated using the condition ratings of an actual bridge. A case study bridge was selected, and its condition levels from 1 to 20 years were compared with the predicted results, showing a good fit with the actual condition levels of the bridge. Additionally, the predicted bridge deterioration curves correspond well with the bridge's actual deterioration patterns.  
Conceptualization, T.Z. and P.L.; Software, H.C. and X.C.; Validation, H.C. and X.C.; Formal analysis, P.L.; Resources, Y.Z.; Writing--original draft preparation, T.Z.; Writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Change curve of the bridge condition levels in 60 years.  
Funding: The research work in this study was supported by the National Natural Science Foundation of China (No. 51608087), the Science and Technology Innovation Program of Hunan Province in China (No. 2021 RC3016), the Open Project of National Engineering Research Center of High-Speed Railway Construction Technology in China (No. HSR202010), and the Scientific Research Project from the Educational Department of Liaoning Province in China (No. LJKMZ20220379). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available from the corresponding author upon reasonable request. Conflicts of Interest: The authors declare no conflict of interest.