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.

Predicting Concrete Pavement Condition for Sustainable Management: Unveiling the Development of Distresses through Machine Learning

“…Due to such characteristics of particle filtering, the more reliable the characteristics of the actual measured training data, the higher the prediction accuracy for the test data to be predicted. Readers should refer to the literature for detailed features and the results of particle filtering, including a recent paper that predicted the overall condition index of concrete pavement [8,[34][35][36].…”

“…Since the 1980s, state departments of transportation (DOTs) across the United States have extensively researched pavement condition ratings (PCRs) based on Mechanistic-Empirical (ME) design for pavement management at the network level [8]. During this period, individual deductive curves for various distresses occurring in road pavements were developed to measure the deterioration rate using time series analysis [4].…”

“…To clarify the meaning of SD on the prediction curve, the maintenance application baselines are shown together (preventive maintenance (1 m 2 ), reactive maintenance (18 m 2 ), and maintenance (36 m 2 )). Based on the number of particles utilized in the previous paper [8], 15,000 particles were generated. The analysis revealed that particle filtering can predict unused data for prediction with a very high accuracy of 98.78%.…”

An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database

“…Recently, the initial step among several to forecast future road pavement condition indices was conducted by utilizing machine learning with PMS DB [25]. While that study predicted future pavement condition indices for roadways, this section aims to develop a CPCI for predicting the future condition of bridge deck pavement and propose a model for more accurate prediction of future conditions.…”

“…Figure 1.An intuitively comprehensible schematic of the particle filtering process of CPCI[25]. Figure1.…”