Identification of road diseases based on cutting cross-section utilizing machine learning

As critical infrastructure in both urban and rural transportation systems, road health significantly impacts regional economies and population mobility. Traditional road disease prediction methods are often costly and time-intensive due to manual inspections. This study introduces an innovative prediction method utilizing the morphological features of rutted cross sections. It combines point cloud technology for cross-section data acquisition and statistical analysis for morphological feature extraction. A machine learning-based prediction model is developed, which has demonstrated that the prediction accuracy can reach 83.05% at a pile of 3. The findings indicate that this method enhances the accuracy of road disease predictions under the dataset. This research not only offers a more precise prediction model for road disease but also provides a scientific foundation and decision-making support for road disease prevention, maintenance, and management, thereby offering considerable practical benefits.