Reimagining unbound road pavement technology: Integrating testing, design, construction and performance in the post-digital era

Kodikara, Jayantha; Sounthararajah, Arooran; Chen, Liuxin

doi:10.1016/j.trgeo.2024.101274

Cited by 3 publications

(1 citation statement)

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“…The model validation results showed that the prediction model proposed in this article is more consistent with the results obtained from manual visual discrimination and can realize the rapid calculation and ranking of pavement damage conditions, with potential value in engineering applications. In the future, other less common pavement distress samples will be collected in a targeted manner, and the use of pavement triple-point cloud data to generate depth images will be considered to increase the number of rutting distress samples related to depth [68], thus further optimizing the prediction accuracy of the evaluation model.…”

Section: Discussionmentioning

confidence: 99%

Automated Multi-Type Pavement Distress Segmentation and Quantification Using Transformer Networks for Pavement Condition Index Prediction

Zhang,

Song,

Zhuang

et al. 2024

Applied Sciences

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Pavement distress detection is a crucial task when assessing pavement performance conditions. Here, a novel deep-learning method based on a transformer network, referred to as ISTD-DisNet, is proposed for multi-type pavement distress semantic segmentation. In this methodology, a mix transformer (MiT) based on a hierarchical transformer structure is chosen as the backbone to obtain multi-scale feature information on pavement distress, and a mixed attention module (MAM) is introduced at the decoding stage to capture the pavement distress features across different channels and spatial locations. A learnable transposed convolution upsampling module (TCUM) enhances the model’s ability to restore multi-scale distress details. Subsequently, a novel parameter—the distress pixel density ratio (PDR)—is introduced based on the segmentation results. Analyzing the intrinsic correlation between the PDR and the pavement condition index (PCI), a new pavement damage index prediction model is proposed. Finally, the experimental results reveal that the F1 and mIOU of the proposed method are 95.51% and 91.67%, respectively, and the segmentation performance is better than that of the other seven mainstream segmentation models. Further PCI prediction model validation experimental results also indicate that utilizing the PDR enables the quantitative evaluation of the pavement damage conditions for each assessment unit, holding promising engineering application potential.

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Section: Discussionmentioning

confidence: 99%