A novel noncontact method for the pavement skid resistance evaluation based on surface texture

Lu, Jiale; Pan, B.; Liu, Quan; Sun, Minghao; Liu, Pengfei; Oeser, Markus

doi:10.1016/j.triboint.2021.107311

Cited by 15 publications

(7 citation statements)

References 27 publications

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“…Ejsmont and Sommer [17] evaluated the depth of tire tread deformation by casting an imprint of tireroad contact in self-vulcanizing rubber. Chen [18], Lu et al [19], and Yang et al [20] tested the skid resistance and the corresponding pavement texture, took the depth corresponding to the good correlation between the texture indicators and the skid resistance as the depth of the enveloped pavement profile. Chen et al [21], Wang et al [22], Gao et al [23],…”

Section: Status Of Current Research On Rubber Enveloping Of Pavement ...mentioning

confidence: 99%

A New Approach for Determining Rubber Enveloping on Pavement and Its Implications for Friction Estimation

Yun,

Tang,

Sandberg

et al. 2024

Coatings

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The depth to which the pavement texture is enveloped by the tire tread rubber (d) is an important parameter related to contact performance. This study presents a new method (S-BAC), which relies on the ratio between the real contact area and the nominal tire-pavement contact area (S) and the bearing area curve (BAC), to measure the depth on pavements. The tire-pavement contact was simulated by contact between a non-patterned rubber block and pavement specimens. After analyzing the affecting factors, the new method was compared with previous methods by the d values and the application on the relationship between pavement texture parameters and friction. The results reveal that though there is a linear regression between the d obtained with the S-BAC and previous methods, the d values obtained with different methods differ. Applying the S-BAC method can strengthen the relationship between texture parameters and friction more than other methods.

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Section: Status Of Current Research On Rubber Enveloping Of Pavement ...mentioning

confidence: 99%

A New Approach for Determining Rubber Enveloping on Pavement and Its Implications for Friction Estimation

Yun,

Tang,

Sandberg

et al. 2024

Coatings

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“…Tong et al (2018) utilized a handheld scanner to acquire the regional surface texture of pavement core specimens, transformed it into a pixel-like two-dimensional matrix, then provided as an input into a CNN model to estimate the level of pavement skid resistance with MPD value. Lu et al (2022) developed a CNN model to investigate the primary relationship between the texture and the fieldlevel skid-resistance measurement of the pavement. They investigated the relationship between the texture acquisition interval and the effective contact.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

Niu

et al. 2023

Computer aided Civil Eng

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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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“…Recently, researchers came up with several novel indexes to better characterize pavement texture, including the amplitude, distribution, shape, and hybrid parameters, to build the correlation model with the ground truth friction (Čelko et al, 2016;Du et al, 2021;Hu et al, 2016;Zuniga-Garcia & Prozzi, 2019). Plentiful signal processing techniques, such as the Hilbert-Huang transform (Kane et al, 2015), fractal analysis (Miao et al, 2014), power spectral analysis (Hartikainen et al, 2014), wavelet analysis (Du et al, 2022;Zelelew et al, 2013), and photosimulated images of surface height maps (Mahboob Kanafi et al, 2015), are applied for pavement friction estimation as well. However, the relationship between texture indexes and pavement friction is implicit and hard to quantize.…”

Section: Introductionmentioning

confidence: 99%

“…Since the image data lost lots of depth information, the image-based DL model can only classify the frictional performance into several levels (Du et al, 2019). Lu et al developed a novel non-contact method for skid resistance evaluation based on an end-to-end convolutional neural network (CNN) for both dry and wet pavement (Lu et al, 2022). Hsieh and Tsai applied a CNN-based model for automated raveling detection and classification, which combines the data-driven features and macrotexture features to achieve better performance (Hsieh & Tsai, 2021).…”

Section: Introductionmentioning

confidence: 99%

Effective pavement skid resistance measurement using multi‐scale textures and deep fusion network

Liu

Weng

et al. 2022

Computer aided Civil Eng

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Pavement skid resistance measurement is a fundamental component of roadway management and maintenance. Most traditional approaches rely on manual operations or heavy devices, which lead to a labor-intensive, inefficient, and vulnerable testing environment. Precise laser scanning technology lays a solid foundation for effective and continuous pavement friction measurement. This paper proposed an automated pavement friction estimation model using 3D point cloud data and a deep neural network. The fine-grained texture data of over 800 pavement sections with various anti-skidding abilities were collected. The impact of the multi-scale textures on pavement friction was separated and analyzed via two-dimensional wavelet decomposition. A multi-input fusion network with deep aggregation modules was designed to fuse the features of sub-images generated by wavelet decomposition. The results show that the average prediction error is 0.0935, outperforming most state-of-the-art models. The impact of different texture scales on friction estimation is then revealed. The proposed method provides a new tool for effective and large-scale pavement friction evaluation.

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A novel noncontact method for the pavement skid resistance evaluation based on surface texture

Cited by 15 publications

References 27 publications

A New Approach for Determining Rubber Enveloping on Pavement and Its Implications for Friction Estimation

A New Approach for Determining Rubber Enveloping on Pavement and Its Implications for Friction Estimation

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

Effective pavement skid resistance measurement using multi‐scale textures and deep fusion network

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