A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning

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.