Predictive Modeling of Pavement Damage Using Machine Learning and Big Data Processing

Abstract

Potholes, soil settlement, and road subsidence have become major road safety hazards in South Korea. Such problems not only impede driver and pedestrian safety but also cause secondary accidents, economic losses, and damage the nation's image. To this end, we developed local predictive models that can be extrapolated to national estimation models. These models were developed from a specific area (Seoul Metropolitan City) that has the highest occurrences of potholes and road subsidence. This research utilized big data and artificial intelligence techniques to develop these models. The first step involved the dimensional reduction of independent variables using a mechanical-statistical approach. A data standardization process was then used for reducing the uncertainty of these variables. A total of 19 machine learning optimization methods were used to train the standardized variables. The optimized models were finally determined by an error comparison. As a result, the optimized prediction models for potholes, soil settlement, and road subsidence were found to be multiple regression analysis that showed an accuracy of 70% and robust regression analysis that showed an accuracy of 73%. Keywords: Pothole, Soil Settlement, Road Subsidence, Big Data, Machine Learning