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24 pages, 6855 KiB  
Article
Estimation of the Kinetic Coefficient of Friction of Asphalt Pavements Using the Top Topography Surface Roughness Power Spectrum
by Bo Sun, Haoyuan Luo, Yibo Rong and Yanqin Yang
Materials 2025, 18(15), 3643; https://doi.org/10.3390/ma18153643 (registering DOI) - 2 Aug 2025
Abstract
This study proposes a method for estimating the kinetic coefficient of friction (COF) for asphalt pavements by improving and applying Persson’s friction theory. The method utilizes the power spectral density (PSD) of the top surface topography instead of the full PSD to better [...] Read more.
This study proposes a method for estimating the kinetic coefficient of friction (COF) for asphalt pavements by improving and applying Persson’s friction theory. The method utilizes the power spectral density (PSD) of the top surface topography instead of the full PSD to better reflect the actual contact conditions. This approach avoids including deeper roughness components that do not contribute to real rubber–pavement contact due to surface skewness. The key aspect of the method is determining an appropriate cutting plane to isolate the top surface. Four cutting strategies were evaluated. Results show that the cutting plane defined at 0.5 times the root mean square (RMS) height exhibits the highest robustness across all pavement types, with the estimated COF closely matching the measured values for all four tested surfaces. This study presents an improved method for estimating the kinetic coefficient of friction (COF) of asphalt pavements by employing the power spectral density (PSD) of the top surface roughness, rather than the total surface profile. This refinement is based on Persson’s friction theory and aims to exclude the influence of deep surface irregularities that do not make actual contact with the rubber interface. The core of the method lies in defining an appropriate cutting plane to isolate the topographical features that contribute most to frictional interactions. Four cutting strategies were investigated. Among them, the cutting plane positioned at 0.5 times the root mean square (RMS) height demonstrated the best overall applicability. COF estimates derived from this method showed strong consistency with experimentally measured values across all four tested asphalt pavement surfaces, indicating its robustness and practical potential. Full article
(This article belongs to the Section Construction and Building Materials)
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19 pages, 5548 KiB  
Article
Predicting Asphalt Pavement Friction by Using a Texture-Based Image Indicator
by Bingjie Lu, Zhengyang Lu, Yijiashun Qi, Hanzhe Guo, Tianyao Sun and Zunduo Zhao
Lubricants 2025, 13(8), 341; https://doi.org/10.3390/lubricants13080341 - 31 Jul 2025
Viewed by 86
Abstract
Pavement skid resistance is of vital importance for road safety. The objective of this study is to propose and validate a texture-based image indicator to predict pavement friction. This index enables pavement friction to be predicted easily and inexpensively using digital images, with [...] Read more.
Pavement skid resistance is of vital importance for road safety. The objective of this study is to propose and validate a texture-based image indicator to predict pavement friction. This index enables pavement friction to be predicted easily and inexpensively using digital images, with predictions correlated to Dynamic Friction Tester (DFT) measurements. Three different types of asphalt surfaces (Dense-Grade Asphalt Concrete, Open-Grade Friction Course, and Chip Seal) were evaluated subject to various tire polishing cycles. Images were taken with corresponding friction coefficients obtained using DFT in the laboratory. The aggregate protrusion area is proposed as the indicator. Statistical models are established for each asphalt surface type to correlate the proposed indicator with friction coefficients. The results show that the adjusted R-squared values of all relationships are above 0.90. Compared to other image-based indicators in the literature, the proposed image indicator more accurately reflects the changes in pavement friction with the number of polishing cycles, proving its cost-effective use for considering pavement friction in the mix design stage. Full article
(This article belongs to the Special Issue Tire/Road Interface and Road Surface Textures)
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18 pages, 10294 KiB  
Article
High-Precision Normal Stress Measurement Methods for Tire–Road Contact and Its Spatial and Frequency Domain Distribution Characteristics
by Liang Song, Xixian Wu, Zijie Xie, Jie Gao, Di Yun and Zongjian Lei
Lubricants 2025, 13(7), 309; https://doi.org/10.3390/lubricants13070309 - 16 Jul 2025
Viewed by 323
Abstract
This study investigates measurement methods for and the distribution characteristics of normal stress within tire–road contact areas. A novel measurement method, integrating 3D scanning technology with bearing area curve (BAC) analysis, is proposed. This method quantifies the rubber penetration depth and calculates contact [...] Read more.
This study investigates measurement methods for and the distribution characteristics of normal stress within tire–road contact areas. A novel measurement method, integrating 3D scanning technology with bearing area curve (BAC) analysis, is proposed. This method quantifies the rubber penetration depth and calculates contact stress based on rubber deformation. The key innovation of this method lies in this integrated methodology for high-precision stress mapping. In the spatial domain, stress distribution is characterized by the percentage of area occupied by different stress intervals, while in the frequency domain, stress levels are analyzed at various frequencies. The results demonstrate that as the Mean Profile Depth (MPD) of the road texture increases, the areas under stress greater than 1.0 MPa increase, while the areas under stress less than 0.8 MPa decrease. However, when the MPD exceeds 0.7 mm, this effect becomes less pronounced. Higher loads and harder rubber reduce the proportion of areas under lower stress and increase the proportion under higher stress. Low-frequency (<800 1/m) stress components increase with an MPD up to 0.7 mm, beyond which they exhibit diminished sensitivity. Stress at the same frequency is not significantly affected by load variation but increases markedly with increasing rubber hardness. This research provides crucial insights into contact stress distribution, establishing a foundation for analyzing road friction and optimizing surface texture design oriented towards high-friction pavements. Full article
(This article belongs to the Special Issue Tire/Road Interface and Road Surface Textures)
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17 pages, 2032 KiB  
Article
Intelligent Evaluation of Permeability Function of Porous Asphalt Pavement Based on 3D Laser Imaging and Deep Learning
by Rui Xiao, Jingwen Liu, Xin Li, You Zhan, Rong Chen and Wenjie Li
Lubricants 2025, 13(7), 291; https://doi.org/10.3390/lubricants13070291 - 29 Jun 2025
Viewed by 462
Abstract
The permeability of porous asphalt pavements is a critical skid resistance indicator that directly influences driving safety on wet roads. To ensure permeability (water infiltration capacity), it is necessary to assess the degree of clogging in the pavement. This study proposes a permeability [...] Read more.
The permeability of porous asphalt pavements is a critical skid resistance indicator that directly influences driving safety on wet roads. To ensure permeability (water infiltration capacity), it is necessary to assess the degree of clogging in the pavement. This study proposes a permeability evaluation model for porous asphalt pavements based on 3D laser imaging and deep learning. The model utilizes a 3D laser scanner to capture the surface texture of the pavement, a pavement infiltration tester to measure the permeability coefficient, and a deep residual network (ResNet) to train the collected data. The aim is to explore the relationship between the 3D surface texture of porous asphalt and its permeability performance. The results demonstrate that the proposed algorithm can quickly and accurately identify the permeability of the pavement without causing damage, achieving an accuracy and F1-score of up to 90.36% and 90.33%, respectively. This indicates a significant correlation between surface texture and permeability, which could promote advancements in pavement permeability technology. Full article
(This article belongs to the Special Issue Tire/Road Interface and Road Surface Textures)
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24 pages, 16234 KiB  
Article
A Contrast-Enhanced Feature Reconstruction for Fixed PTZ Camera-Based Crack Recognition in Expressways
by Xuezhi Feng and Chunyan Shao
Electronics 2025, 14(13), 2617; https://doi.org/10.3390/electronics14132617 - 28 Jun 2025
Viewed by 167
Abstract
Efficient and accurate recognition of highway pavement cracks is crucial for the timely maintenance and long-term use of expressways. Among the existing crack acquisition methods, human-based approaches are inefficient, whereas carrier-based automated methods are expensive. Additionally, both methods present challenges related to traffic [...] Read more.
Efficient and accurate recognition of highway pavement cracks is crucial for the timely maintenance and long-term use of expressways. Among the existing crack acquisition methods, human-based approaches are inefficient, whereas carrier-based automated methods are expensive. Additionally, both methods present challenges related to traffic obstruction and safety risks. To address these challenges, we propose a fixed pan-tilt-zoom (PTZ) vision-based highway pavement crack recognition workflow. Pavement cracks often exhibit complex textures with blurred boundaries, low contrast, and discontinuous pixels, leading to missed and false detection. To mitigate these issues, we introduce an algorithm named contrast-enhanced feature reconstruction (CEFR), which consists of three parts: comparison-based pixel transformation, nonlinear stretching, and generating a saliency map. CEFR is an image pre-processing algorithm that enhances crack edges and establishes uniform inner-crack characteristics, thereby increasing the contrast between cracks and the background. Extensive experiments demonstrate that CEFR improves recognition performance, yielding increases of 3.1% in F1-score, 2.6% in mAP@0.5, and 4.6% in mAP@0.5:0.95, compared with the dataset without CEFR. The effectiveness and generalisability of CEFR are validated across multiple models, datasets, and tasks, confirming its applicability for highway maintenance engineering. Full article
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14 pages, 4510 KiB  
Article
Analysis of the Suitability of 3D-Printed Road Surface Replicas for Laboratory Testing of Rolling Resistance
by Wojciech Owczarzak, Sławomir Sommer and Grzegorz Ronowski
Coatings 2025, 15(7), 766; https://doi.org/10.3390/coatings15070766 - 28 Jun 2025
Viewed by 352
Abstract
This study investigates the influence of the method and materials used for creating road surface replicas on the evaluation of rolling resistance using the oscillatory method. While casting resin is commonly employed for this purpose, the research explores 3D printing as a viable [...] Read more.
This study investigates the influence of the method and materials used for creating road surface replicas on the evaluation of rolling resistance using the oscillatory method. While casting resin is commonly employed for this purpose, the research explores 3D printing as a viable alternative. To assess the effectiveness of the proposed approach, replicas of three road surfaces with differing rolling resistance characteristics were created using both techniques. The conventional resin-based replicas served as a reference. A range of tires—summer, winter, and all-season—were tested on the prepared samples. The results were compared to evaluate the consistency between the two replica fabrication methods and to determine the suitability of 3D-printed surfaces as substitutes for those made with casting resin. Full article
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19 pages, 3685 KiB  
Article
Extraction of Pavement Texture–Friction Surface Density Index Using High-Precision Three-Dimensional Images
by Niangzhi Mao, Shihai Ding, Xiaoping Chen, Changfa Ai, Huaping Yang and Jiayu Wang
Lubricants 2025, 13(7), 288; https://doi.org/10.3390/lubricants13070288 - 27 Jun 2025
Viewed by 422
Abstract
Pavement surface texture significantly affects its skid resistance. To characterize pavement surface texture and analyze its correlation with skid resistance, this paper proposes a novel three-dimensional (3D) texture evaluation index: mean texture surface area density (MTSAD). First, field tests were conducted on Chengdu [...] Read more.
Pavement surface texture significantly affects its skid resistance. To characterize pavement surface texture and analyze its correlation with skid resistance, this paper proposes a novel three-dimensional (3D) texture evaluation index: mean texture surface area density (MTSAD). First, field tests were conducted on Chengdu Greenway pavement using a portable laser scanner to collect high-precision texture data, while a pendulum friction tester was employed to measure the British Pendulum Number (BPN). Subsequently, digital image processing technology was employed for the 3D reconstruction of pavement texture. Leveraging the high-resolution data characteristics and incorporating the concept of infinite subdivision, an innovative method for calculating the pavement texture surface area was developed, ultimately yielding the MTSAD. Finally, polynomial regression analysis was performed to examine the correlation between MTSAD and BPN, revealing a coefficient of determination (R2) of 0.8302. The results demonstrate a close relationship between MTSAD and pavement friction, while proving that texture indices that are easy to promote can be obtained through high-precision 3D point cloud images, and validating the potential of non-contact texture measurement as a viable alternative to conventional contact-based friction testing methods. Full article
(This article belongs to the Special Issue Tire/Road Interface and Road Surface Textures)
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29 pages, 7501 KiB  
Article
Theoretical Analysis of Suspended Road Dust in Relation to Concrete Pavement Texture Characteristics
by Hojun Yoo, Gyumin Yeon and Intai Kim
Atmosphere 2025, 16(7), 761; https://doi.org/10.3390/atmos16070761 - 21 Jun 2025
Viewed by 324
Abstract
Particulate matter (PM) originating from road dust is an increasing concern in urban air quality, particularly as non-exhaust emissions from tire–pavement interactions gain prominence. Existing models often focus on meteorological and traffic-related variables while oversimplifying pavement surface characteristics, limiting their applicability across diverse [...] Read more.
Particulate matter (PM) originating from road dust is an increasing concern in urban air quality, particularly as non-exhaust emissions from tire–pavement interactions gain prominence. Existing models often focus on meteorological and traffic-related variables while oversimplifying pavement surface characteristics, limiting their applicability across diverse spatial and traffic conditions. This study investigates the influence of concrete pavement macrotexture—specifically the Mean Texture Depth (MTD) and surface wavelength—on PM10 resuspension. Field data were collected using a vehicle-mounted DustTrak 8530 sensor following the TRAKER protocol, enabling real-time monitoring near the tire–pavement interface. A multivariable linear regression model was used to evaluate the effects of MTD, wavelength, and the interaction between silt loading (sL) and PM10 content, achieving a high adjusted R2 of 0.765. The surface wavelength and sL–PM10 interaction were statistically significant (p < 0.01). The PM10 concentrations increased with the MTD up to a threshold of approximately 1.4 mm, after which the trend plateaued. A short wavelength (<4 mm) resulted in 30–50% higher PM10 emissions compared to a longer wavelength (>30 mm), likely due to enhanced air-pumping effects caused by more frequent aggregate contact. Among pavement types, Transverse Tining (T.Tining) exhibited the highest emissions due to its high MTD and short wavelength, whereas Exposed Aggregate Concrete Pavement (EACP) and the Next-Generation Concrete Surface (NGCS) showed lower emissions with a moderate MTD (1.0–1.4 mm) and longer wavelength. Mechanistically, a low MTD means there is a lack of sufficient voids for dust retention but generates less turbulence, producing moderate emissions. In contrast, a high MTD combined with a very short wavelength intensifies tire contact and localized air pumping, increasing emissions. Therefore, an intermediate MTD and moderate wavelength configuration appears optimal, balancing dust retention with minimized turbulence. These findings offer a texture-informed framework for integrating pavement surface characteristics into PM emission models, supporting sustainable and emission-conscious pavement design. Full article
(This article belongs to the Special Issue Traffic Related Emission (3rd Edition))
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19 pages, 2399 KiB  
Article
The Fine Feature Extraction and Attention Re-Embedding Model Based on the Swin Transformer for Pavement Damage Classification
by Shizheng Zhang, Kunpeng Wang, Zhihao Liu, Min Huang and Sheng Huang
Algorithms 2025, 18(6), 369; https://doi.org/10.3390/a18060369 - 18 Jun 2025
Viewed by 374
Abstract
The accurate detection and classification of pavement damage are critical for ensuring timely maintenance and extending the service life of road infrastructure. In this study, we propose a novel pavement damage recognition model based on the Swin Transformer architecture, specifically designed to address [...] Read more.
The accurate detection and classification of pavement damage are critical for ensuring timely maintenance and extending the service life of road infrastructure. In this study, we propose a novel pavement damage recognition model based on the Swin Transformer architecture, specifically designed to address the challenges inherent in pavement imagery, such as low damage visibility, varying illumination conditions, and highly similar surface textures. Unlike the original Swin Transformer, the proposed model incorporates two key components: a fine feature extraction module and a multi-head self-attention re-embedding module. These additions enhance the model’s ability to capture subtle and complex damage patterns. Experimental evaluations demonstrate that the proposed model achieves a 2.07% improvement in classification accuracy and a 0.97% increase in F1 score compared to the baseline while maintaining comparable computational complexity. Overall, the model significantly outperforms the baseline Swin Transformer in pavement damage detection and classification, highlighting its practical applicability. Full article
(This article belongs to the Section Randomized, Online, and Approximation Algorithms)
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26 pages, 3938 KiB  
Review
Study on Skid Resistance of Asphalt Pavements Under Macroscopic and Microscopic Texture Features: A Review of the State of the Art
by Wei Chen, Zhengchao Zhang, Jincheng Wei, Xiaomeng Zhang, Wenjuan Wu, Yuxuan Sun and Guangyong Wang
Appl. Sci. 2025, 15(12), 6819; https://doi.org/10.3390/app15126819 - 17 Jun 2025
Viewed by 569
Abstract
Pavement skid resistance is one of the most important factors affecting road safety, and pavement texture morphology significantly influences this property. Therefore, it is crucial to investigate the relationship between pavement texture and skid resistance. This article provides an overview of recent research [...] Read more.
Pavement skid resistance is one of the most important factors affecting road safety, and pavement texture morphology significantly influences this property. Therefore, it is crucial to investigate the relationship between pavement texture and skid resistance. This article provides an overview of recent research advancements in key areas, including the anti-skid mechanisms of asphalt pavements, factors affecting pavement anti-skid performance, methods for characterising and evaluating pavement anti-skid performance, and the relationship between the macroscopic and microscopic texture of pavements and their anti-skid performance. Based on a comparative analysis of the intrinsic mechanical interactions between asphalt pavements and rubber tyres, it was determined that the surface texture characteristics of the asphalt pavement are the most critical factor influencing its anti-skid performance. These include both macroscopic and microscopic texture parameters, which, together with the service environment, collectively influence the pavement’s anti-skid performance. The existing texture characteristics, based on the anti-skid performance of asphalt pavements, as detected by various methods and evaluated using established models, are summarised here. Finally, this article discusses the relationship between texture characteristic parameters and asphalt pavement anti-skid performance from both macro- and microtexture perspectives. This synthesis serves as a valuable reference and basis for further research and development in enhancing asphalt pavement skid resistance. Full article
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18 pages, 3957 KiB  
Article
Comparative Analysis of Lab-Data-Driven Models for International Friction Index Prediction in High Friction Surface Treatment (HFST)
by Alireza Roshan and Magdy Abdelrahman
Appl. Sci. 2025, 15(11), 6249; https://doi.org/10.3390/app15116249 - 2 Jun 2025
Viewed by 450
Abstract
High Friction Surface Treatments (HFSTs) are often utilized as a spot treatment to enhance selected areas with high friction demand rather than extended pavement sections and are helpful in increasing skid resistance and minimizing road accidents. A laboratory design approach was created to [...] Read more.
High Friction Surface Treatments (HFSTs) are often utilized as a spot treatment to enhance selected areas with high friction demand rather than extended pavement sections and are helpful in increasing skid resistance and minimizing road accidents. A laboratory design approach was created to assess the fundamental ideas behind the international friction index (IFI) concept and update the present IFI model parameters for HFST applications based on test findings to gain a better understanding of HFST performance. Two aggregate types in three sizes were tested under controlled polishing cycles. Friction and texture were measured using the Dynamic Friction Tester (DFT) and Circular Track Meter (CTM). Three physics-informed empirical models, including logarithmic, power law, and polynomial models, were selected to better represent texture effects, nonlinear scaling, and complex interactions between COF and MPD. Results show that friction performance varies with aggregate type, gradation, and polishing, and that traditional IFI parameters may not fully capture HFST behavior. Model refinements are suggested to better represent HFST surface characteristics with the lowest testing Root Mean Squared Error (RMSE) (0.049) and the highest predictive accuracy R2 (0.821); the logarithmic model was found to be the best. Sensitivity analysis revealed that IFI predictions are more sensitive to COF (ΔIFI: 14.3–17.7%) than MPD (ΔIFI: 1.5–6.0%) across all models. These results demonstrate how these models can improve HFST design and performance assessment while providing useful information for enhancing road safety. This process is a useful tool for evaluating HFST friction resistance in a lab setting since it calculates HFST skid resistance using results measured in the lab. Full article
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21 pages, 5352 KiB  
Article
Optimization of Exposed Aggregate Concrete Mix Proportions for High Skid Resistance and Noise Reduction Performance
by Xudong Zha, Chengzhi Wu, Runzhou Luo and Yaqiang Liu
Appl. Sci. 2025, 15(11), 5881; https://doi.org/10.3390/app15115881 - 23 May 2025
Viewed by 378
Abstract
Conventional cement concrete pavements often suffer from rapid skid resistance degradation and excessive traffic noise, necessitating effective solutions. This study investigates exposed aggregate concrete (EAC) through orthogonal experimental methods to evaluate the effects of four mix design parameters—water–binder ratio, sand ratio, coarse aggregate [...] Read more.
Conventional cement concrete pavements often suffer from rapid skid resistance degradation and excessive traffic noise, necessitating effective solutions. This study investigates exposed aggregate concrete (EAC) through orthogonal experimental methods to evaluate the effects of four mix design parameters—water–binder ratio, sand ratio, coarse aggregate volume ratio, and proportion of aggregates >9.5 mm—on surface texture characteristics, skid resistance and noise reduction (SRNR) performance, and mechanical properties. The optimal EAC mix proportions were developed, and the correlations between surface texture characteristics and SRNR performance were established. Results indicate that the proportion of aggregates >9.5 mm significantly influences surface texture characteristics and SRNR performance. The optimal mix proportions (water–binder ratio: 0.43, sand ratio: 31%, coarse aggregate volume ratio: 42%, and proportion of aggregates >9.5 mm: 50%) exhibited superior mechanical properties, achieving a 31.5% increase in pendulum value and a 6.48 dB reduction in tire/surface noise compared to grooved conventional concrete. The noise reduction frequency range is mainly concentrated in the mid-high frequency range of 1.5~4.0 kHz, which is more sensitive to the human ear. High correlations were observed between the surface texture characteristics and SRNR performance. Specifically, noise value decreased progressively with increasing exposed aggregate depth, while the pendulum value exhibited a trend of initial decrease, followed by an increase and subsequent decrease in response to the elevated exposed aggregate area ratio. Compared to traditional cement concrete pavements, the optimized EAC, while maintaining mechanical properties, exhibits superior SRNR performance, providing a valuable reference for the construction of high SRNR cement concrete pavements. Full article
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27 pages, 10888 KiB  
Article
A Simulation of Tire Hydroplaning Based on Laser Scanning of Road Surfaces
by Weikai Zeng, Wenliang Wu, Zhi Li, Weiyong Chen, Jianping Gao and Bilong Fu
Appl. Sci. 2025, 15(10), 5577; https://doi.org/10.3390/app15105577 - 16 May 2025
Viewed by 464
Abstract
To investigate the influence of pavement texture on tire hydroplaning, this study utilized laser scanning to capture the surface characteristics of three asphalt mixtures—AC-13, SMA-13, and OGFC-13—across fifteen rutting plate specimens. Three-dimensional (3D) pavement models were reconstructed to incorporate realistic texture data. Finite [...] Read more.
To investigate the influence of pavement texture on tire hydroplaning, this study utilized laser scanning to capture the surface characteristics of three asphalt mixtures—AC-13, SMA-13, and OGFC-13—across fifteen rutting plate specimens. Three-dimensional (3D) pavement models were reconstructed to incorporate realistic texture data. Finite element simulations, employing fluid-structure interaction and explicit dynamics in Abaqus, were conducted to model tire-water-pavement interactions. The results indicate that the anti-skid performance ranks as OGFC > SMA > AC. However, despite OGFC and SMA exhibiting comparable anti-skid metrics (e.g., pendulum friction value and mean texture depth), OGFC’s superior texture uniformity results in significantly better hydroplaning resistance. Additionally, tire tread depth critically influences hydroplaning speed. A novel Anti-Slip Comprehensive Texture Index (ACTI) was proposed to evaluate pavement texture uniformity, providing a more comprehensive assessment of anti-skid performance. These findings underscore the importance of texture uniformity in enhancing pavement safety under wet conditions. Full article
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21 pages, 39789 KiB  
Article
An Interpretable Method for Asphalt Pavement Skid Resistance Performance Evaluation Under Sand-Accumulated Conditions Based on Multi-Scale Fractals
by Yuhan Weng, Zhaoyun Sun, Huiying Liu and Yingbin Gu
Sensors 2025, 25(10), 2986; https://doi.org/10.3390/s25102986 - 9 May 2025
Viewed by 470
Abstract
The skid resistance of asphalt pavement is vital for traffic safety and reducing accidents. Existing research using only wavelet transforms or fractal theory to study the pavement surface texture-skid resistance relationship has limitations. This paper integrates a wavelet transform and fractal theory to [...] Read more.
The skid resistance of asphalt pavement is vital for traffic safety and reducing accidents. Existing research using only wavelet transforms or fractal theory to study the pavement surface texture-skid resistance relationship has limitations. This paper integrates a wavelet transform and fractal theory to extract the multi-scale fractal features of pavement texture. It proposes an interpretable machine learning model for skid resistance assessments of sand-accumulated pavements. The three-dimensional (3D) texture of asphalt pavements is decomposed at multiple scales, and fractal and multifractal features are extracted to build a dataset. The performance of mainstream machine learning models is compared, and the eXtreme Gradient Boosting (XGBoost) model is optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The SHapley Additive exPlanations (SHAP) method is used to analyze the optimal model’s interpretability. The results show that asphalt concrete with a maximum nominal particle size of 13 mm (AC-13) has the most concentrated fractal dimension, followed by open-graded friction course with a maximum nominal particle size of 9.5 mm (OGFC-10), with stone matrix asphalt with a maximum nominal particle size of 16 mm (SMA-16) being the most dispersed. The singular intensity difference of the multifractal (Δα) changes oppositely to the fractal dimension (D), and the fractal dimension difference of the multifractal (Δf) decreases with the number of wavelet decomposition layers. The CMA-ES-XGBoost model improves R2 by 27.1%, 9%, and 3.4% over Linear Regression, Light Gradient Boosting Machine (LightGBM), and XGBoost, respectively. The 0.4–0.8 mm range fractal dimension most significantly impacts the model output, with complex interactions between features at different scales. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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30 pages, 16943 KiB  
Article
Quantitative Assessment of Road Dust Suspension Based on Variations in Asphalt Pavement Surface Texture
by Ho-Jun Yoo, Sung-Jin Hong, Jeong-Yeon Cho and In-Tai Kim
Atmosphere 2025, 16(5), 552; https://doi.org/10.3390/atmos16050552 - 6 May 2025
Viewed by 475
Abstract
This study explores the correlation between road surface texture, including microtexture (texture depth) and macrotexture (wavelength) in asphalt pavement, and suspended dust generation on asphalt pavements. A detailed analysis of various pavement types, including Hot Mix Asphalt (HMA) and porous pavement, was conducted [...] Read more.
This study explores the correlation between road surface texture, including microtexture (texture depth) and macrotexture (wavelength) in asphalt pavement, and suspended dust generation on asphalt pavements. A detailed analysis of various pavement types, including Hot Mix Asphalt (HMA) and porous pavement, was conducted to assess their impact on dust load and concentration. For HMA pavements, deeper texture depths led to a higher dust load and concentration, attributed to the impermeable nature of the material, which causes dust to become easily suspended in the air. Conversely, porous pavements, which have air gaps in their surface layers, showed reduced dust suspension despite a higher dust load, due to the ability of these voids to trap dust and minimize air-pumping effects from tire–road contact. The study found that a macrotexture depth (MTD) exceeding 1.7 mm stabilized dust concentration, while higher surface wavelengths and silt load (sL) values above 0.1 g/m2 significantly contributed to dust suspension. These findings suggest that optimizing road surface texture and aggregate size, considering the voids and depth, can help reduce suspended dust, providing a balance between road safety and environmental management. This research offers valuable insights for designing pavements that mitigate air pollution while maintaining functional performance. Full article
(This article belongs to the Special Issue Traffic Related Emission (3rd Edition))
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