Search Results (158)

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

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

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

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

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

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

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

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

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

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

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

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 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

The 3D reconstruction of asphalt pavement macrotexture holds significant engineering value for pavement quality assessment and performance monitoring. However, conventional 3D reconstruction methods face challenges, such as high equipment costs and operational complexity, limiting their widespread application in engineering practice. Meanwhile, current deep learning-based monocular image reconstruction for pavement texture remains in its early stages. To address these technical limitations, this study systematically prepared four types of asphalt mixture specimens (AC, SMA, OGFC, and PA) with a total of 14 gradations. High-precision equipment was used to simultaneously capture 2D RGB images and 3D RGB-D point cloud data of the surface texture. An innovative multi-scale feature fusion CNN model was developed based on an encoder–decoder architecture, along with an optimized training strategy for model parameters. For performance evaluation, multiple metrics were employed, including root mean square error (RMSE = 0.491), relative error (REL = 0.102), and accuracy at different thresholds (δ = 1/2/3: 0.931, 0.979, 0.990). The results demonstrate strong correlations between the reconstructed texture’s mean texture depth (MTD) and friction coefficient (f₈) with actual measurements (0.913 and 0.953, respectively), outperforming existing methods. This confirms that the proposed CNN model achieves precise 3D reconstruction of asphalt pavement macrotexture, effectively supporting skid resistance evaluation. To validate engineering applicability, field tests were conducted on pavements with various gradations. The model exhibited excellent robustness under different conditions. Furthermore, based on extensive field data, this study established a quantitative relationship between MTD and friction coefficient, developing a more accurate pavement skid resistance evaluation system to support maintenance decision-making. Full article

Water film depth (WFD) on runways is a key factor contributing to aircraft hydroplaning during takeoff and landing. Thus, the early measurement or prediction of WFD during rain is critical for reducing accidents. Most existing WFD prediction models are derived from experiments conducted on road surfaces. However, an accurate prediction of WFD on runways and reduced hydroplaning risk require a precise empirical prediction model. This study conducted experiments involving four parameters—rainfall intensity, pavement mean texture depth, drainage length, and transverse slope—to develop a WFD dataset specific to different runway conditions. The multiple linear regression method is employed to establish a model for WFD predictions. The proposed National Taiwan University (NTU) model’s predictability is compared with three existing empirical models using NTU and Gallaway datasets. The results clearly demonstrate the superior accuracy and robustness of the NTU model compared to the other evaluated models. The NTU model offers a precise and practical predictive formula, making it highly suitable for integration into contaminated runway warning and management systems. This study employed a laser displacement sensor and a programmable logic controller to obtain high-accuracy, high-sampling-rate WFD data. Modern automated data acquisition enables simultaneous measurement at multiple points and captures the complete WFD curve from zero to a stable depth, which was previously difficult to obtain. Full article