###### Abstract  
In South Korea, various attempts have been made to utilize the Pavement Management System database (PMS DB) more efficiently as a basis for preventive maintenance. Data for the PMS DB is extensively collected every year. This study aims to predict future pavement conditions by introducing the concept of machine learning instead of regression modeling. We selected 469 sections that satisfied the analysis conditions and used them for analysis. We used particle filtering, a machine learning technique, to predict future pavement conditions. We found that the function of the particle filtering technique itself increases the prediction accuracy for the target section analyzed as the number of particles increases. Furthermore, the number of time series points in one section had a higher impact on the improvement of prediction accuracy than the number of sections analyzed. Finally, the relative error by each predicted age for the same section decreased as the number of time series points increased. These findings indicate that the rate of decrease in the performance index can be quantitatively presented in the future, and the method developed in this study could be used by pavement managers during the decision-making process.  
pavement management system; condition predicting method; particle filtering; machine learning; uncertainty 2022 29 August 2022 29 August 2022 3  
application of partial depth repair, the need for a method to develop roads that perform as newly constructed roads, including full road rehabilitation, and the need for preventive maintenance are gradually increasing [3].  
Therefore, most concrete pavements have been systematically managed through the pavement management system (PMS) since 2006. To understand the pavement condition at the network level of policy makers and road managers and to determine the repair method, the highway pavement condition index and the national highway pavement condition index have been established and operated for express highways and national highways, respectively. In particular, as the necessity and effectiveness of management through PMS have been proven recently, both express highways and national highways are being investigated annually as of 2021.  
However, in the operation of the current PMS of express highways and national highways in South Korea, the investigation results of the current year are generally reported after March of the following year. Accordingly, using the results as the basis for preventive maintenance to efficiently operate the pavement condition and budget has a practical limit. In addition, the PMS, where a database for a vast network is built every year, is mostly operated for managing the current pavement status. Thus, policy decisions such as preventive maintenance and budget requirements for future pavement conditions are still being made based on the subjective judgment of experts.  
Because most research related to the prediction for pavement management thus far has mostly been based on the regression modeling technique, which is used for decision-making at the network level rather than at the project level, modeling that satisfies all cases has been impossible. When establishing the pavement rehabilitation strategy, using an inaccurate result value as the public state expression for the corresponding section affects only the relevant section under the management at the network level; however, there is also an evident limitation that it is difficult to use under the management at the project level. Therefore, this study attempted to develop a predictive model for a data learning-based performance index that can be used at both the network and project levels for concrete pavements in South Korea. Our aim is to allow more effective road pavement management by identifying the rate of decline in the quantitative performance using the current condition index.