Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

TLDR

In Korea, pavement condition data such as cracks, rutting depth, and the international roughness index are annually collected by automatic equipment (ARAN and KRISS) to manage national highways. This study predicts road pavement deterioration using Korean National Highway Pavement Management System data and a recurrent neural network algorithm. The recurrent neural network learns from 10 years of time‑series data to forecast each section’s pavement condition index for the next year, with sequence lengths tuned per section to reflect differing pavement type, traffic load, and environmental characteristics. The model reduced prediction error by 58.3–68.2% and achieved a coefficient of determination of 0.71–0.87, enabling accurate maintenance timing that can extend pavement life and lower maintenance costs.

Abstract

In Korea, data on pavement conditions, such as cracks, rutting depth, and the international roughness index, are obtained using automatic pavement condition investigation equipment, such as ARAN and KRISS, for the same sections of national highways annually to manage their pavement conditions. This study predicts the deterioration of road pavement by using monitoring data from the Korean National Highway Pavement Management System and a recurrent neural network algorithm. The constructed algorithm predicts the pavement condition index for each section of the road network for one year by learning from the time series data for the preceding 10 years. Because pavement type, traffic load, and environmental characteristics differed by section, the sequence lengths (SQL) necessary to optimize each section were also different. The results of minimizing the root-mean-square error, according to the SQL by section and pavement condition index, showed that the error was reduced by 58.3–68.2% with a SQL value of 1, while pavement deterioration in each section could be predicted with a high coefficient of determination of 0.71–0.87. The accurate prediction of maintenance timing for pavement in this study will help optimize the life cycle of road pavement by increasing its life expectancy and reducing its maintenance budget.