Search Results (40)

This study introduces a novel and user-centered surface abrasion test designed to assess the injury potential of pavement surfaces, particularly for vulnerable road users such as micromobility users. Traditional pavement evaluation methods focus on skid resistance and texture but do not account for the surface’s mechanical aggressiveness during a fall. To address this gap, the proposed test simulates fall conditions by dragging a paraffin wax specimen—used as a low-cost and reproducible proxy to approximate the abrasive response that could affect human skin—over pavement at a controlled speed and load, quantifying material loss as an indicator of surface abrasiveness. The method was validated on three pavement types (smooth ceramic, bituminous, and concrete), demonstrating its sensitivity and repeatability. Unlike conventional point-based tests, it enables continuous evaluation along a predefined length, offering more representative results. A full-scale case study on a micromobility-dedicated bike lane confirmed the test’s responsiveness to surface changes over time. Results suggest the method is practical, reproducible, and applicable to a wide range of pavements. Beyond micromobility, it can be extended to other vulnerable users, such as motorcyclists. The test represents a new metric for infrastructure safety audits focused on injury mitigation. Full article

This study presents the evaluation of the photometric performance and energy-saving potential of light-coloured pavement mixtures (LCPMs) in road lighting applications, along with their effects on surface friction, macrotexture, and specularity. The application of LCPMs in tunnels can enhance road surface illumination, thereby improving driver visibility, increasing road safety and comfort, and reducing energy consumption per kilometre. While such surface treatments enable more efficient and cost-effective lighting, maintaining an optimal balance in surface performance poses many challenges due to the impact on concurrent targets in terms of friction, macrotexture, noise contribution, and specularity. Indeed, issues related to friction performance, macrotexture characteristics, and the concurring energy-saving potential of LCPMs remain insufficiently explored. To this end, investigations were conducted to assess the energy-saving potential of light-coloured surface treatments and to evaluate the photometric, frictional, and macrotexture properties of different densely graded LCPMs. A new method was set up and implemented to compare different surface treatments. The results indicate that light-coloured surface treatments increased the average luminance coefficient (up to 0.2406), with glass-containing mixtures offering greater potential for improved surface texture, friction, and energy-efficient road lighting. Full article

Preventive maintenance treatments are widely applied to asphalt pavements to mitigate deterioration and extend service life. This study evaluated four common technologies: a high-elasticity ultra-thin overlay, an Stone Mastic Asphalt (SMA)-10 thin overlay, micro-surfacing (MS-III), and a chip seal. Laboratory testing focused on skid resistance, surface texture, and low-temperature cracking resistance. Skid resistance was measured with a tire–pavement dynamic friction analyzer under controlled load and speed, while surface macrotexture was assessed using a laser scanner. Low-temperature cracking resistance was determined through three-point bending beam tests at −10 °C. The results showed that chip seal achieved the highest initial friction and texture depth, immediately enhancing skid resistance but exhibiting rapid texture loss and gradual friction decay. Micro-surfacing also demonstrated good initial skid resistance but experienced a sharp reduction of over 30% due to fine aggregate polishing. By contrast, the high-elastic ultra-thin overlay and SMA thin overlay provided more stable skid resistance, lower long-term friction loss, and excellent crack resistance. The polymer-modified ultra-thin overlay achieved the highest low-temperature bending strain ≈40% higher than untreated pavement, indicating superior crack resistance, followed by the SMA thin overlay. Micro-surfacing with a chip seal layer only slightly improved low-temperature performance. Overall, the high-elastic ultra-thin overlay proved to be the most balanced preventive maintenance option under heavy-load traffic and cold climate conditions, combining durable skid resistance with enhanced crack resistance. Full article

The widespread application of granite manufactured sand (GS) concrete in pavement engineering is limited by issues such as suboptimal particle size distribution and an unclear optimal rock powder content. Furthermore, research on the long-term evolution of the skid resistance characteristics of GS concrete remains relatively scarce. This knowledge gap makes it difficult to accurately assess the skid resistance performance of GS concrete in practical engineering applications, thereby compromising traffic safety. To address this research gap, this study utilized a self-developed indoor abrasion tester for pavement concrete to assess the skid resistance of GS concrete. Three-dimensional laser scanning was employed to acquire the concrete’s surface texture parameters. Using the friction coefficient and texture parameters as skid resistance evaluation indicators, and combining these with changes in the concrete’s surface morphology, the study explores how effective sand content, stone powder content, and fine aggregate lithology affect the long-term skid resistance of GS concrete pavements and reveals the evolution trends of their long-term skid resistance. Research results show that as the number of wear cycles increases, low and high effective sand content affect the surface friction coefficient of specimens in opposite ways. Specimens with 95% effective sand content exhibit superior skid resistance. Stone powder content influences the friction coefficient in three distinct variation patterns, showing no clear overall trend. Nevertheless, specimens with 5% stone powder content demonstrate better skid resistance. Among different fine aggregate lithologies, GS yields a higher friction coefficient than river sand (RS), while limestone manufactured sand (LS) shows significant friction coefficient fluctuations across different wear cycles. Adding stone powder substantially enhances mortar strength and delays groove collapse edge formation. Moreover, higher effective sand content and proper stone powder content mitigate bleeding, thereby improving mortar performance. Full article

Diamond grinding is a key concrete pavement restoration technique for concrete pavements. Traffic degrades the serviceability of the concrete pavements, resulting in unsatisfactory skid levels and noise concerns. Diamond grinding is known to enhance longevity and performance by improving smoothness and friction. By removing flaws with a cutting head equipped with diamond blades, the procedure produces a “corduroy” texture that enhances braking and stability. Diamond grinding typically results in a 20–80% reduction in the International Roughness Index, significantly enhancing pavement smoothness. It also improves macrotexture and creates longitudinal drainage channels, which collectively increase skid resistance and lower the chance of hydroplaning. The paper aims to highlight the need for diamond grinding for concrete pavements, which, despite their longevity, have decreased serviceability from traffic. The review further explores emerging innovations and identifies the gaps in long-term performance tracking and life-cycle environmental assessment. This paper reviews the effectiveness of diamond grinding as a pavement rehabilitation technique, with emphasis on ride quality, surface friction, noise reduction, and durability. Field applications and evaluation metrics are discussed to assess their contribution to pavement performance. This review aims to support researchers, pavement engineers, and agencies by providing a comprehensive understanding of diamond grinding’s applications, performance metrics, and potential for sustainable pavement management. 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

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

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

The influence of sidewalk paving materials on pedestrian safety and comfort remains an underexplored topic within the walkability literature. This pilot study aims to address this gap by evaluating the role of five surface-related attributes—roughness, friction, texture, heat retention, and maintenance—through a qualitative approach complemented by a simplified quantitative evaluation. The study was conducted along a pedestrian route in Braga, Portugal, where pedestrian perceptions were collected via a questionnaire and compared with objective measurements obtained at seven testing points with different paving materials. The results indicate a strong preference for concrete and mortar pavements due to their slip-resistant surfaces, smoothness, and overall regularity. Quantitative tests confirmed that these materials exhibited the highest slip resistance and surface regularity, reinforcing the general alignment between pedestrian perceptions and measured performance. Participants rated paving attributes higher than others, such as sidewalk width or obstacle-free paths. Notable demographic differences also emerged: women rated sidewalk attributes more highly than men, seniors preferred traditional stone pavements more, and adults favored concrete. These findings highlight the importance of integrating surface-related sidewalk attributes into walkability assessments and urban design strategies to promote safer, more comfortable, and more inclusive pedestrian environments. Full article

This study proposes a non-contact framework for evaluating the skid resistance of shared roadside pavements to improve cyclist and pedestrian safety. By integrating a friction tester and a laser scanner, we synchronize high-resolution three-dimensional (3D) surface texture characterization with friction coefficient measurements under dry and wet conditions. Key metrics—including fractal dimension (FD), macro/micro-texture depth density (HL_TX and WL_TX), mean texture depth (MTD), and joint dimensions—were derived from 3D laser scans. A hierarchical regression analysis was employed to prioritize the influence of texture and joint parameters on skid resistance across environmental conditions. Combined with material types (brick, tile, and stone) and drainage performance, these metrics are systematically analyzed to quantify their correlations with skid resistance. Results indicate that raised macro-textures and high FD (>2.5) significantly enhance dry-condition skid resistance, whereas recessed textures degrade performance. The hierarchical model further reveals that FD and MTD dominate dry friction (β = 0.61 and −0.53, respectively), while micro-texture density (WL_TX) and seam depth are critical predictors of wet skid resistance (β = −0.76 and 0.31). In wet environments, skid resistance is dominated by micro-texture density (WL_TX < 3500) and macro-texture-driven water displacement, with higher WL_TX values indicating denser micro-textures that impede drainage. The study validates that non-contact laser scanning enables efficient mapping of critical texture data (e.g., pore connectivity, joint depth ≥0.25 mm) and friction properties, supporting rapid large-scale pavement assessments. These findings establish a data-driven linkage between measurable surface indicators (texture, morphometry, drainage) and skid resistance, offering a practical foundation for proactive sidewalk safety management, especially in high-risk areas. Future work should focus on refining predictive models through multi-sensor fusion and standardized design guidelines. Full article

Asphalt pavement skid resistance, governed by surface texture, is critical for traffic safety. Most research has focused on full-depth textural characteristics, often overlooking the depth of tire–pavement contact under real traffic conditions. This study introduces the concept of the Effective Depth of Skid Resistance (EDSR) to describe the effective depth of tire–asphalt contact, improving skid resistance assessment accuracy. Using blue linear laser scanning, surface textures of three common asphalt pavements with wearing courses—AC-13, AC-16, and SMA-13—were analyzed, and friction coefficients were measured using a British pendulum. After pre-processing three-dimensional texture data, fractal dimensions at various depths were calculated using the box-counting method and correlated with the friction coefficients. Previous studies show an insignificant correlation between full-depth asphalt pavement textures and skid resistance. However, this study found a significant positive correlation between skid resistance and pavement textures at specific depths or the EDSR. A depth with a correlation exceeding 0.9 was defined as the EDSR. Linear formulas were established for each pavement type within these EDSR ranges. A theoretical model was developed for predicting skid resistance, showing an over 80% accuracy against real-world data, indicating its potential for improving road surface performance detection. Full article

High Friction Surface Treatments (HFST) are recognized for their effectiveness in enhancing skid resistance and reducing road accidents. While Epoxy-based HFSTs are widely applied, they present limitations such as compatibility issues with existing pavements, high installation and removal costs, and durability concerns tied to substrate quality. As an alternative to traditional Epoxy-based HFSTs, this study investigated the effects of aggregate gradation as designated by agencies on the performance of asphalt-based HFST. Various aggregate types were assessed to evaluate friction performance and the impact of polishing cycles on non-Epoxy HFST. It was found that adjustments in aggregate size and gradation may be necessary when transitioning to asphalt-based HFSTs, given the different nature of asphalt as more temperature susceptible compared to Epoxy. Various asphalt binder grades were considered in this study. A series of tests, including the British Pendulum Test (BPT), Dynamic Friction Tester (DFT), Circular Track Meter (CTM), Micro-Deval (MD), and Aggregate Imaging Measurement System (AIMS), were conducted to measure Coefficient of Friction (COF), Mean Profile Depth (MPD), texture, and angularity before and after polishing cycles. The results showed that the COF in asphalt-based slabs decreased more significantly than in Epoxy-based slabs as polishing cycles increased for HFST and medium gradations. However, in coarse gradation, the COF of slabs using asphalt-based binder matched or even surpassed that of Epoxy after polishing. Notably, the PG88-16 binder for Calcined Bauxite (CB) had the smallest reduction in COF after 140K polishing cycles, with only a 19% decrease compared to a 23% reduction for Epoxy. Full article