Machine learning algorithms for monitoring pavement performance - Cano-Ortiz, Saúl; Pascual-Muñoz, Pablo; Castro-Fresno, Daniel (Automation in construction, 2022)
This work introduces the need to develop competitive, low-cost and applicable technologies to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms. Specifically, the most recent studies are described according to the data collection methods: images, ground penetrating radar (GPR), laser and optic fiber. The main models that are presented for such state-of-the-art studies are Support Vector Machine, Random Forest, Naïve Bayes, Artificial neural networks or Convolutional Neural Networks. For these analyses, the methodology, type of problem, data source, computational resources, discussion and future research are highlighted. Open data sources, programming frameworks, model comparisons and data collection technologies are illustrated to allow the research community to initiate future investigation. There is indeed research on ML-based pavement evaluation but there is not a widely used applicability by pavement management entities yet, so it is mandatory to work on the refinement of models and data collection methods. [Display omitted] •The inefficiency of roads maintenance and the need for automation and solutions based on ML models is stated.•An insight of the ML algorithms most used for the development of pavement management systems is given.•Data collection techniques and related state-of-the-art based on ML algorithms are described.•Drawbacks, possible solutions and future research of ML-based methodologies are provided.