Search Results (97)

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 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

The Lijiang River Basin (LRB) is a karst ecosystem that presents unique challenges for agricultural land planning. Evaluating cultivated land suitability based on natural factors is critical for ensuring food security in this region. This study was based on the cultivated land distribution data of the LRB in the China Land-Use and Land-Cover Chang dataset, selecting 22 restriction factors across five dimensions: climate, topography, soil, hydrology, and social conditions, and the suitability of cultivated land (paddy fields and drylands) in the LRB was evaluated using the MaxEnt model to further identify the main restricting factors affecting the spatial distribution. The research showed that (1) For paddy fields, high-suitability areas covered 2875.05 km², medium-suitability 1670.58 km², low-suitability 3187.25 km², and non-suitable 9368.46 km². The main restriction factors were distance to villages, slope, surface gravel content, soil thickness, soil pH, and total phosphorus content. (2) For drylands, high-suitability areas covered 3282.3 km², medium-suitability 2260.93 km², low-suitability 4536.27 km², and non-suitable 6836.85 km². The main restriction factors were soil thickness, distance to roads, surface gravel content, elevation, soil pH, and soil texture. This research can provide a scientific basis for the layout of food security and planning agricultural land use in the LRB. 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

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

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

Traditional visual simultaneous localization and mapping (SLAM) systems typically generate sparse or semi-dense point cloud maps, which are insufficient for effective navigation and path planning. Constructing navigation maps through dense depth estimation generally entails high computational costs, and depth estimation is prone to errors in weakly textured regions such as road surfaces. Furthermore, traditional visual SLAM methods rely on local relative coordinate systems, making it extremely challenging to merge mapping results from different coordinate frames in navigation systems lacking global positioning constraints. To address these limitations, this paper presents a multi-submap fusion mapping method based on semantic ground fitting and incorporates global navigation satellite system (GNSS) to provide global positioning information via occupancy grid maps. The method emphasizes the integration of low-cost sensors into a unified system, aiming to create an accurate and real-time mapping solution that is cost-effective and highly applicable. Simultaneously, a multi-submap management mechanism is introduced to dynamically store and load maps, updating only the submaps surrounding the vehicle. This ensures real-time map updates while minimizing computational and storage resource consumption. Extensive testing of the proposed method in real-world scenarios, using a self-built experimental platform, demonstrates that the generated grid map meets the accuracy requirements for navigation tasks. 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

Rolling resistance is a critical factor that influences vehicle energy consumption, emissions, and overall performance. It directly impacts fuel efficiency, tire longevity, and driving dynamics. Traditional rolling resistance tests are conducted on smooth steel drums, which fail to replicate real-world road surface textures, potentially skewing results. This article presents the process of designing surface replicas using 3D printing technology, which consisted of selecting the internal structure, material, and print parameters of the surface sample. In order to verify the designed structures, an original mechanical strength test was performed. The test was based on pressing the tire onto the test sample with an appropriate force that corresponded to typical conditions during rolling resistance measurements. The test results included surface texture profiles before and after the application of load, which were then superimposed to detect any possible sample deformation. The obtained strength test results confirmed the validity of using 3D printing technology in the process of obtaining road surface replicas. Full article