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• W4377966300 endingPage "104177" @default.
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• W4377966300 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." @default.
• W4377966300 created "2023-05-25" @default.
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• W4377966300 date "2023-07-01" @default.
• W4377966300 modified "2023-09-30" @default.
• W4377966300 title "Multi-scale asphalt pavement deformation detection and measurement based on machine learning of full field-of-view digital surface data" @default.
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• W4377966300 doi "https://doi.org/10.1016/j.trc.2023.104177" @default.
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