Raveling is one of the most common asphalt pavement distresses. The survey of its condition is required for transportation agencies to ensure roadway safety and appropriately apply preservation and rehabilitation treatments. However, the traditional raveling condition survey, including the determination of the raveling severity, is typically manually conducted by in-field visual inspection methods that are time consuming, labor intensive, and error prone. Although automated raveling detection and severity classification models have been developed, these existing models have shortcomings. Therefore, there is an urgent need to develop a more accurate and reliable model to automatically detect and classify raveling. This study proposes the first convolutional neural network (CNN)-based model for automated raveling detection and classification. Compared with general CNNs, the proposed model combines the data-driven features learned from training data and macrotexture features of 3D pavement surface data to achieve better performance. The proposed model was evaluated and compared with existing machine learning models using real-world 3D pavement surface data collected from the state of Georgia, U.S. By combining data-driven features with macrotexture features, the proposed model achieved the highest accuracy of 90.8% on raveling classification. The proposed model also achieved classification precision and recall higher than 85% for all raveling severity levels, which is more accurate and robust than existing models. It is concluded that, with multi-type features extraction and proper model design, the proposed model can provide more accurate and reliable predictions for raveling detection and classification.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Automated Asphalt Pavement Raveling Detection and Classification using Convolutional Neural Network and Macrotexture Analysis


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:


    Erscheinungsdatum :

    2021-04-04




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    How to Prevent Asphalt Pavement Raveling

    Sontowski, P. | British Library Online Contents | 1995


    Raveling of Drain Asphalt

    Kotte, J. F. A. K. / Tolman, F. / Vag-och transport-forskningsinstitutet | British Library Conference Proceedings | 1992


    Experimental Study on Macrotexture of Asphalt Pavement

    Li, Zhi / Wu, Wenliang / Qiu, Zhixiong et al. | ASCE | 2017


    Quantitative Analysis of Macrotexture of Asphalt Concrete Pavement Surface Based on 3D Data

    Huyan, Ju / Li, Wei / Tighe, Susan et al. | Transportation Research Record | 2020


    Pavement Surface Macrotexture Measurement and Applications

    Flintsch, Gerardo W. / de León, Edgar / McGhee, Kevin K. et al. | Transportation Research Record | 2003