Highlights An integrated system was proposed for multi-scale asphalt pavement deformation detection and measurement. A two-step rutting detection method was developed using 1D-CNN classification followed with localization. Full-lane digital IRI measurement was carried out for spatial distribution analysis of road roughness. An unsupervised CNN model was presented for large-span deformation segmentation on digital elevation maps. A digital surface dataset for asphalt pavement deformation monitoring was established.

    Abstract Asphalt pavement deformation is a common phenomenon due to the material property and traffic loads. Aiming at ensuring traffic comfort and safety, it is essential to constantly monitor the multi-scale pavement deformation with a non-destructive and automatic system. This paper presents a full field-of-view asphalt pavement deformation inspection framework based on multi-dimensional surface data and machine learning, which can detect and measure pavement rutting, roughness and large-span deformation simultaneously. In this integrated system, one-dimensional convolutional neural network (1D CNN) classification and localization models are developed for two-step rutting detection. The quarter-car model with multiple measuring lines is employed for full-lane international roughness index (IRI) measurement and spatial analysis. The unsupervised K-means convolutional neural network (K-CNN) model is proposed for large-span deformation detection. The results show that the overall F1 scores of rutting classification and localization are 99.56% and 97.24%, respectively. The full-lane measurement suggests that IRI presents a bimodal distribution in transverse space due to the concentrated traffic loads on the wheel path. In addition, the unsupervised K-CNN achieves an average consistency index (CI) of 91.61% on large-span sunken and heave deformation segmentation.


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    Titel :

    Multi-scale asphalt pavement deformation detection and measurement based on machine learning of full field-of-view digital surface data


    Beteiligte:
    Guan, Jinchao (Autor:in) / Yang, Xu (Autor:in) / Liu, Pengfei (Autor:in) / Oeser, Markus (Autor:in) / Hong, Han (Autor:in) / Li, Yi (Autor:in) / Dong, Shi (Autor:in)


    Erscheinungsdatum :

    2023-05-15




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch