Effective contact texture region aware pavement skid resistance prediction via convolutional neural network

Journal Article (2023)
Author(s)

Weibo Shi (Chang'an University)

Dongyu Niu (Chang'an University)

Z. Li (TU Delft - Transport and Planning, Beijing Institute of Technology)

Yanhui Niu (Chang'an University)

Transport and Planning
Copyright
© 2023 Weibo Shi, Dongyu Niu, Z. Li, Yanhui Niu
DOI related publication
https://doi.org/10.1111/mice.13063
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Weibo Shi, Dongyu Niu, Z. Li, Yanhui Niu
Transport and Planning
Issue number
13
Volume number
39
Pages (from-to)
2054-2070
Reuse Rights

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Abstract

The surface texture of asphalt pavement has a significant effect on skid resistance performance. However, its contribution to the performance of skid resistance is non-homogeneous and subjects to local validity. There are also a few deep learning models that take into account the effective contact texture region. This paper proposes a convolutional neural network model based on the effective contact texture region, containing macro- and micro-scale awareness sub-modules. In this study, the asphalt mixture with varying gradations was designed to accurately obtain the effective contact texture region. Then, the textures were disentangled into macro- and micro-texture scales by applying the fast Fourier transform and fed into the model for training. Finally, the area of effective contact texture region was calculated, and the effective contact ratio parameter was then proposed using the triangulation algorithm. The results showed that the effective contact texture area of pavement varies by the asphalt mixture type. The effective contact ratio parameter exhibited a significant positive correlation (Pearson correlation coefficient is 0.901, R2= 0.8129) with skid resistance performance and was also influenced by key sieve aggregate content from 2.36 to 4.75 mm. The data of effective contact texture region following disentanglement significantly released the model performance (the relative error dropped to 1.81%). The model exhibited improved precision and performance, which can be utilized as an efficient, non-contact alternative method for skid resistance analysis.

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