Deep learning techniques for multi-class classification of asphalt damage based on hamburg-wheel tracking test results

In recent years, advancements in deep learning (DL) have been leveraged in civil engineering, but further exploration is necessary to apply DL techniques to asphalt research. These advances involve employing computer vision tasks and machine learning approaches to solve current challenges and develop innovative solutions for the conservation and monitoring of roads. In the laboratory, the Hamburg-Wheel Tracking (HWT) test simulates expected vehicle traffic and evaluates permanent deformation, such as rutting in asphalt mixtures. Current works focus on detecting the damages in the asphalt but do not classify the level of damage, which is the novelty proposed in the present work for rutting evaluation. This study aims to classify images of asphalt damage based on surface deformation caused by the HWT test. The dataset built and implemented in this study -called by the authors HWTBench2023- consists of 1198 images of asphalt samples subjected to controlled HWT conditions with varying levels of damage. The study employs a multi-class classification model based on convolutional neural networks (CNNs). The CNN was calibrated with transfer learning and fine-tuning hyperparameters. The outcomes demonstrate a high degree of accuracy, where the validation test showed an overall accuracy of 89 %, with F1score values over 89 %. The model developed in this research identifies the presence or absence of damage and quantifies the damage level. In addition, the model was evaluated on a different dataset containing asphalt pavement photographs with rut damages. The model's performance detecting rut damage on asphalt pavement under actual conditions indicates its adaptability to new images not included in the model's learning stage. Therefore, this study improves methods for fast assessing rutting deterioration in asphalt pavements with transfer learning, provides accurate measurements of pavement deformation, and helps identify maintenance and rehabilitation needs.