The Effect of Pavement Texture on the Performance of Skid Resistance of Asphalt Pavement Based on the Hilbert-Huang Transform

Yu, Miao; Kong, Yaozhong; Wu, Chuanhai; Xu, Xinquan; Li, Shanqiang; Chen, Haifeng; Kong, Lingyun

doi:10.1007/s13369-021-05915-x

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…(2016) adopted an effective water evacuation area to postulate tire–pavement noise pollution and friction performance. Similar applications can also be seen in pavement performance predictions using 3D statistical parameters (Cesbron et al., 2009; Q. J. Li et al., 2017; Y. Wang et al., 2020), Hilbert–Huang transform (Kane et al., 2015; Rado et al., 2014; M. Yu et al., 2021), power spectrum (D. Chen et al., 2015; Zhan et al., 2020, 2022), wavelet transform (Yang et al., 2018, 2020), and fractal analysis (Kokkalis & Olympia, 1998; Kokkalis et al., 2002; Cheng Liu et al., 2021). Among these parameters, the fractal dimension (

{D}_f

, Miao et al., 2014; Kokkalis, 1998; Villani et al., 2014), with the corresponding root mean square roughness (RMS), seems to be the most effective.…”

Section: Introductionmentioning

confidence: 79%

“…Similar applications can also be seen in pavement performance predictions using 3D statistical parameters (Cesbron et al, 2009; Q. J. Li et al, 2017;, Hilbert-Huang transform (Kane et al, 2015;Rado et al, 2014;M. Yu et al, 2021), power spectrum (D. Chen et al, 2015;Zhan et al, 2020, wavelet transform (Yang et al, 2018(Yang et al, , 2020, and fractal analysis (Kokkalis & Olympia, 1998;Kokkalis et al, 2002;Cheng Liu et al, 2021).…”

Section: Introductionmentioning

confidence: 83%

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Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Pan

Ren

et al. 2023

Computer aided Civil Eng

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The high cost of collecting surficial textures has been the bottleneck problem for many decades. To bridge this gap, this study aims to propose a complete framework based on the generative adversarial networks (GANs) architecture for texture regeneration using limited texture data samples. Four variants of the GAN were compared to achieve the best regeneration performance. Prior to running GAN, specifically improved data augmentation and modified sigmoid normalization operations were dedicated to avoiding the loss of texture information and the non‐convergence problem. After running GAN, the estimation metrics were introduced and discussed separately to quantitatively evaluate the rationality and diversity of the regenerated textures. The results demonstrated that the use of improved data augmentation and normalization methods significantly enhanced the regeneration capability of GAN, and more importantly the skewed distribution of surficial heights which is not achievable by present texture regeneration methods was most effectively regenerated by deep regret analytic GAN with well generalization ability. Our study provides a promising choice to generate virtually realistic pavement surface textures.

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{D}_f

, Miao et al., 2014; Kokkalis, 1998; Villani et al., 2014), with the corresponding root mean square roughness (RMS), seems to be the most effective.…”

Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 83%

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Pan

Ren

et al. 2023

Computer aided Civil Eng

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“…Riahi et al [17] considered the interaction between the pavement macrotexture and tyre rolling process to propose a model based on the dissipation of energy by the rubber block during the cyclic deformation of the pavement in concave and convex cycles. Yu et al [18] selected seven parameters to characterise pavement texture features and analysed the impact of those features on pavement skid resistance. Sharma et al [19] developed a multi-physics tyre-pavement contact model based on the temperature and pavement texture, focusing on the impact of pavement macrotexture on tyre-pavement interaction.…”

Section: Introductionmentioning

confidence: 99%

Finite Element Analysis of Tyre Contact Interaction Considering Simplified Pavement with Different Aggregate Sizes

Li,

Chen,

et al. 2023

Applied Sciences

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This study considered the effect of pavement aggregate grain size on tyre–pavement contact interaction during the late stages of pavement skid resistance. First, hemispherical shells 7, 9, and 13 mm in diameter were used to simulate coarse pavement aggregates. Subsequently, a three-dimensional finite element tyre–pavement contact model developed using ABAQUS was employed to analyse the contact interaction between each simplified pavement type and the tyre under steady–state rolling and braking conditions. Finally, the concept of occlusal depth was proposed and applied to characterise pavement skid resistance. The results showed that under steady–state rolling conditions, the peak contact stress of the simplified pavement increased with the pavement mean texture depth, whereas the contact area decreased. Under steady–state braking conditions, the effect of the contact interaction between the tyre and simplified pavement aggregates was ranked in order of superiority as aggregate grain sizes of 9, 7, and 13 mm, indicating that aggregate grain size did not exhibit any correlation with tyre–pavement contact interaction. Additionally, the squares of linear correlation coefficients between the pavement cumulative occlusal depth and horizontal braking force reached 0.921, 0.941, 0.889, and 0.894 for vehicle speeds of 30, 60, 90, and 120 km/h, respectively, indicating that they could be used to assess pavement skid resistance.

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“…[13][14][15]. Some scholars also processed the pavement texture data from the perspective of a signal system and characterized it by the 2D wave spectrum and power spectral density (PSD) [16][17][18][19]. Several studies have characterized the skid resistance of asphalt mixture aggregates in terms of fractal dimension as well as multiple fractal spectral features [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Incep-FrictionNet-Based Pavement Texture Friction Level Classification Prediction Method

Xu,

Lin,

Wang

et al. 2023

Lubricants

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Pavement skid resistance is crucial for driving safety, and pavement texture significantly impacts skid resistance performance. To realize the application of pavement texture data in assessing pavement skid resistance performance, this paper proposes a convolutional neural network model based on the InceptionV4 module to predict the pavement friction level from the pavement texture dataset. The surface texture data of indoor test-rutted slabs were collected using a portable laser scanner. The surface friction coefficient of rutted slabs was measured using a pendulum tribometer. After data pre-processing, a total of nine types of texture data that are in the range of 0.4 to 0.8 skid resistance levels are selected at an interval of 0.05 for training, validation, and testing of the network model. The same dataset and training parameters were also used to train a conventional convolutional network model for comparison. The results showed that the proposed network model achieved 97.89% classification accuracy on the test set, which was 11.94 percentage points higher than the comparison model. This demonstrates that the proposed model in this paper can evaluate pavement friction levels by non-contact scanning of textures and has higher evaluation accuracy.

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The Effect of Pavement Texture on the Performance of Skid Resistance of Asphalt Pavement Based on the Hilbert-Huang Transform

Cited by 15 publications

References 12 publications

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Finite Element Analysis of Tyre Contact Interaction Considering Simplified Pavement with Different Aggregate Sizes

Incep-FrictionNet-Based Pavement Texture Friction Level Classification Prediction Method

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