Pavement Skid Resistance Research Articles

A technical survey on mechanism and influence factors for asphalt pavement skid-resistance

AbstractTo explore the research status on mechanism of the skid-resistance for asphalt pavement, the related achievements of the rubber friction, the tire–road contact, and the influence factors were reviewed. The rubber friction was reviewed from the mechanism and rubber friction model. The tire-road contact was studied from the modeling methods, the evaluation methods, and the skid-resistance mechanisms at different conditions. The influence factors of skid-resistance were summarized from different designing processes. This review showed that the appropriate contact or evaluation model should be selected according to the required parameters. Although the finite element method is widely used, the accuracy of the model, the boundary conditions, and the load cases need to be further improved. The optimized aggregates and asphalt binders, reasonable gradation, reasonable geometric design, and strict construction quality control are necessary for good initial skid-resistance. Pavement texture as an important factor affecting skid-resistance should be considered in pavement gradation design, especially for wearing layer technology.

Evaluation of Asphalt Pavement Texture Using Multiview Stereo Reconstruction Based on Deep Learning

To quickly obtain texture information and accurately evaluate the skid resistance of asphalt pavement, a multiview stereo reconstruction method based on deep learning is proposed for evaluating the texture depth of asphalt pavement. First, a depth camera and a digital camera are used to collect RGB-D (i.e., Red, Green, Blue, Depth) and RGB (i.e., Red, Green, Blue) multiview image datasets on different types of pavements for model training and validation respectively. Then, the model precision is validated by calculating the overlap (intersection over union, IoU) between the ground truth point cloud and the reconstructed point cloud. The model performances under different training strategies are compared to obtain the best model, and the effects of image resolution, number of views, and types of pavement material on the model precision are analyzed. Finally, image processing methods and texture depth characterization indexes are proposed to obtain pavement texture information from the reconstructed depth map, thus validating the effectiveness of the model. The results show that the trained model using the transfer learning strategy achieves a reconstruction precision of 0.77 when the image resolution is 3024×3024 and the number of views is 7. Furthermore, the reconstruction performance remains stable across different pavement materials, suggesting the model is suitable for accurately reconstructing depth maps for asphalt pavements. The errors between the predicted texture depth value calculated based on the volume-based index (MTDp) and the profile-based index (MPDp) using the depth maps after image processing and the measured texture depth value (MTDe) using the sand patch method are 11.72% and 18.85%, respectively. It is believed that the pavement texture depth can be effectively evaluated from the reconstructed depth map using the volume-based metric (MTDp). In contrast to traditional testing methods, this approach requires using only a digital camera and a personal computer, making it a lightweight and intelligent analysis method for obtaining pavement texture depth information.

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

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

Estimation of Road Wetness from a Passenger Car

This paper presents an evaluation of a system aiming at estimating water depths on a road surface. Using accelerometers, the system records the vibrations of a wheel arch liner due to impacts of water droplets. The system setup, including the location of the accelerometers on a wheel arch and the data acquisition, is described. Tests were performed with a passenger car on various road surfaces and at different vehicle speeds and water depths. Signals recorded by the accelerometers are filtered and processed. The link between the acceleration amplitude, the water depth, and the vehicle speed is consistent with results from previous studies. The effect of the surface texture is less obvious and needs further investigations. A mathematical model has been developed to relate the acceleration amplitude to the water depth. The potential application of the developed system to on-board evaluation of pavement wetness, and consequently the pavement skid resistance, is discussed. Perspectives for driver assistance, or more generally, for autonomous driving to improve traffic safety, are also highlighted.

Evaluate asphalt pavement frictional characteristics based on IGWO-NGBoost using 3D macro-texture data

Friction, or skid resistance, is an important feature of road pavements. It is influenced by a combination of factors. Accurately predicting the friction coefficient and thus effectively assessing the skid resistance performance is a complex nonlinear problem. For multi-feature and non-linear data, the accuracy of traditional methods is limited. In addition, the prediction accuracy of some methods depends on the original sample size, so it is difficult to achieve accurate prediction under the condition of small sample size. To solve the above problem, the friction coefficient prediction model based on the improved gray wolf optimization (IGWO) and natural gradient boosting (NGBoost) is proposed. First, to ensure the diversity of samples, asphalt mixture specimens with different gradation types are made. Then, friction and three-dimensional (3D) macro-texture data are collected from the specimen surfaces. Next, twenty-seven 3D macro-texture features are extracted from macro-texture data to describe macro-texture details. A correlation coefficient evaluation method is used to eliminate redundant features, and a feature importance analysis model based on gradient boosting model is constructed to obtain the key factors affecting the skid resistance. Finally, the friction coefficient prediction model based on IGWO-NGBoost is constructed. The IGWO algorithm is used to adjust the hyperparameters of NGBoost to optimize the model structure. The results show that IGWO-NGBoost can effectively fit the friction coefficient with a goodness of fit R2 of 97.31% compared with state-of-the-art methods. The model can effectively analyze the change mechanism of pavement skid resistance under the combined influence of multiple factors.

High friction surface treatment with silicone resin materials (HFST-S): A preventive maintenance method of asphalt pavements

The skid resistance of asphalt pavements is the key to ensuring traffic safety. Silicone resin is often used as a maintenance material for fog seal because of its waterproof performance and weather resistance, but it is rarely mentioned in the field of skid resistance. In this paper, a high friction surface treatment with silicone resin (HFST-S) was prepared by adding skid-resistance aggregates to improve the skid resistance of pavements. Firstly, the curing time, chemical composition, group content, and hydrophobic performance of silicone resin were studied. Then the morphology characteristics and mechanical performance of skid-resistance aggregates were investigated. Finally, the type and particle size of skid-resistance aggregates, the ratio of silicone resin to skid-resistance aggregates (RSA), and silicone resin dosage were optimized by laboratory tests. The results indicate that silicone resin cured basically at 60 °C for 24 h and Si-OH content decreased during curing. The cured silicone resin was mainly composed of C, O, and Si elements and had remarkable hydrophobicity. In addition, emery sands had richer angularity and better mechanical performance than basalt sands. Therefore, the HFST-S prepared by silicone resin and emery sands has outstanding waterproof performance and skid resistance. The optimum formula of HFST-S was determined as 0.60 – 1.18 mm particle size of emery sands, 40:60 of RSA, and 800 g/m2 of silicone resin.

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

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

An analytical solution for friction coefficients of grooved pavements considering tire rubber-groove interaction

To reduce the loss of skid resistance of pavement in wet weather, pavement grooving is used to improve aircraft operation safety on runways. This study presents an analytical solution to analyze mechanical interactions between tire tread rubber and pavement surface grooves for calculating friction coefficients of aircraft tires on runway pavements. The energy dissipation theory is utilized to calculate the contribution of rubber-groove interlock and the friction on plane part of pavement surface due to surface texture is accounted using Brush model. The comparison of model results and field measurements proves the capability of the proposed method to predict friction coefficients of aircraft tire on runway pavements with various groove configurations. The evaluation of trapezoidal grooved pavement shows similar friction coefficients compared to the standard square grooved pavement regulated by the Federal Aviation Administration (FAA). The friction coefficients of grooved pavement vary depending on the inclined angles of groove and edge-to-edge spacing that are two important geometry parameters. The findings demonstrate the pronounced influences of rubber deposits and groove deterioration on skid resistance of runway pavements. The analysis results can be further used to design optimized groove configuration and determine maintenance threshold for safe operation of aircraft on runways.

Investigation of surface textures deterioration on pavement skid-resistance using hysteresis friction models and numerical simulation method

Many rubber friction theories or some method combined theories and field-experiments are employed to evaluate the pavement skid-resistance deterioration due to the evolution of surface textures. However, these methods are difficult to be implemented in the analysis of situations with multi-factor coupling and some extreme conditions. This study developed a framework to evaluate the skid-resistance deterioration of asphalt pavements. In this framework, the portable laser scanning was used to create the digital worn pavement model, and a hydroplaning finite element (FE) model for these digital worn pavements was constructed to evaluate coupling effects of the texture evolution and factors of slip ratio, slip angle, velocity and water film on braking-cornering characteristics of tire. In this study, the deterioration of skid-resistance of five typical asphalt pavements due the surface texture wear was systematically investigated by this framework. Compared with previous works, this study established the rubber friction models for each digital worn pavement considering the energy hysteresis of rubber and the power spectrum density of surface texture. And the rubber friction model was used to define the interaction behaviors between the tire and corresponding wore pavements in the FE hydroplaning model, rather than using an empirical friction model or a fixed friction coefficient.

Laboratory investigation of traffic effect on the long-term skid resistance of asphalt pavements

To evaluate the skid resistance performance of asphalt pavement under cumulative wheel load and overload, an orthogonal test considering factors such as total traffic volume, occurrence interval, and duration of overload traffic was designed, and lab tests were conducted. This testing was performed based on the self-invented Tire-pavement Dynamic Friction Analyzer (TDFA), by which the real-time data of tire-pavement friction coefficient and pavement textures can be acquired. The results indicated that in the overall process of pavement with different gradation types, all pavement friction coefficients (COF) showed a similar trend of increasing first and then decreasing, and finally tended to be stable. In contrast, the Mean Profile Depth (MPD) values always fluctuated significantly. Moreover, it was found that there is a poor correlation between COF value and MPD value while investigating the degradation laws concerning the skid-resistance performance of asphalt pavement. Compared with the overload traffic applied in the middle and late stage of wear, the deterioration rate of pavement dynamic friction coefficient is faster and the final value of friction coefficient is lower when heavy traffic is applied in the early stage. Finally, the significance of the designed orthogonal test was analyzed. The results show that the total traffic volume has a significant impact on the COF value in the process of pavement service life, followed by the occurrence interval of heavy traffic, and finally the duration of heavy traffic.

A hydrophobic and sustainable anti-icing sand fog seal for asphalt pavement: Its preparation and characterization

Ice can seriously affect the skid resistance of pavement, potentially leading to traffic accidents. To mitigate this problem, this study developed a hydrophobic and sustainable anti-icing sand fog seal (HSSFS) that combines anti-icing functionality with maintenance technology. The HSSFS was prepared using hydrophobic emulsified asphalt (HEA), a thickening agent, corundum, and a sustained-release deicing agent (SRDA). Its composition was determined by single-factor experiments and the Box-Behnken Design (BBD) method, ensuring both stability, anti-icing effects and skid resistance. Subsequently, its anti-icing effect and mechanism, conventional pavement performance, and environmental impact were investigated. Through optimization using single-factor experiments and the BBD design method, it was determined that the optimal composition of the thickening agent, corundum, and SRDA was 26%, 38%, and 36%, respectively. Freezing time, pullout, shear, immersion and freeze–thaw cycle tests indicated that the HSSFS exhibited good anti-icing effects, which can be attributed to the hydrophobic surface and the slow-release deicing agent's capacity. Additionally, pavement function tests indicated that the HSSFS maintained its original functions, such as sealing cracks and pores in the asphalt pavement, thereby reducing water seepage. When the HSSFS spray quantity was 0.6 kg/m2, both the BPN and TD met the specification requirements, indicating good skid resistance for pavement. Furthermore, environmental impact analysis demonstrated that the HSSFS had minimal effects on metals and plants. In conclusion, applying HSSFS to asphalt pavement can provide effective anti-icing effects and preventive maintenance without environmental harm.

Image-based indices of aggregates for predicting the initial skid resistance of bituminous pavements

The objective of this study is to propose image-based indicators of microtexture and macrotexture for pavement surfaces that can predict the initial skid resistance. To achieve this objective, images of different types of aggregates that were collected from quarries were processed. Based on the outcome of the image processing, suitable indicators of shape, angularity, and surface texture of coarse aggregates were proposed. Skid resistance data were collected from pavements constructed with the aggregates used in the image processing, as well as with different surface course mixes commonly used in India, such as Bituminous Concrete (BC), Stone Matrix Asphalt (SMA), and Gap-Graded Rubberized Bituminous mix (GGRB).Through a detailed investigation of the image processing outcome and field data, this study introduces the Aggregate Surface Texture Index (STI) as a means of quantifying microtexture, which can be obtained by processing images of the aggregate using wavelet decomposition technique. Additionally, the study proposes Shape Index (SI) and Angularity Index (AI) as indicators for macrotexture, which can be obtained from the processing of digital images of aggregates. For determining the skid resistance of newly constructed pavements, the study suggests using the Surface Macrotexture Index (SMI), which can be obtained by processing digital images of the pavement surface. Furthermore, this study proposes statistical models for predicting the initial skid resistance of pavement based on these indicators. Overall, the proposed image-based indicators of microtexture and macrotexture have the potential to provide valuable insights for designing and maintaining safe and durable pavement surfaces.

Pavement preventive maintenance decision-making for high antiwear and optimized skid resistance performance

Insufficient pavement skid resistance due to traffic loads and environmental factors is a major cause of vehicular accidents. Hence, continually monitoring the pavement skid resistance has great theoretical significance and is crucial for practical maintenance. A pavement accelerated abrasion system developed by the research team, a high-precision 3D layer scanner, and a pendulum tribometer are used for lab testing. Forty-eight sets of asphalt pavement texture and friction coefficient data for 11 abrasion stages were collected, each comprising 20,000 abrasion cycles. Afterward, a model of asphalt pavement skid resistance deterioration was formulated by analyzing the downward trajectory of the asphalt pavement texture and friction coefficient indexes. The developed deterioration model had a high prediction accuracy with an R2 value of 91.64%, and the corresponding curve met engineering requirements. Subsequently, four pavement maintenance techniques, namely synchronous gravel sealing, microsurfacing, ultrathin overlaying, and thermal generation, were applied to rutted slab specimens after grinding to perform five additional stages of abrasion tests. Through comparisons with the established pavement skid resistance deterioration model, the optimal maintenance timing under the abrasion test conditions of this study was determined as 230,000 abrasion cycles, as determined via the time/pavement condition analysis approach. Cost-effectiveness analysis was performed for different surface treatment techniques, which yielded an equivalent annual cost of 6.7 for the SMA-5 ultra-thin overlay, which was identified as the most promising maintenance approach. This study provides a scientific basis for preventing traffic accidents and improving traffic safety by offering valuable insights into the evolution of pavement skid resistance and offers guidance on decision-making for the preventive maintenance of pavements for high-antiwear and optimized skid-resistance performance.

Anti-Icing Properties of a Polyurethane Superhydrophobic Coating on Asphalt Pavement

Snow and ice accumulation on asphalt pavement can increase the risk of traffic issues. The traditional method of removing snow and ice manually or mechanically from the road surface is time-consuming and labor-intensive. Another common method of spraying salt pollutes the surrounding environment. To overcome these issues, the objective of this study is to study a polyurethane superhydrophobic coating on asphalt pavement and characterize its anti-icing and road properties. A polyurethane superhydrophobic coating (PSC) using polyurethane and carbon nanotube particles (CNTPs) was developed in the lab. The contact angle and roll angle tests, surface roughness test, water droplet icing test, differential scanning calorimetry (DSC) test, ice adhesion test, pavement skid resistance test, water permeability test, and load wheel rolling test were conducted. The results show that the static contact angle and the rolling angle of the water droplet on the PSC surface are 161.176° and 6.6°, which satisfy the requirements to be superhydrophobic. The icing time of the water droplet on the asphalt mixture coated with PSC (AMC) is twice that of the surface of the ordinary asphalt mixture (OAM). In the differential scanning calorimetry test, the PSC released heat slower than polyurethane and asphalt binder during the cooling process. The ice adhesion strength of AMC is 82.78% smaller than that of the OAM. This means the PSC has great anti-icing and deicing properties. In addition, the pendulum value on the AMC surface is 45.7, satisfying the pavement skid resistance requirement. The results of the water seepage test show that the AMC is impermeable to water, thus reducing the risk of water damage to the pavement. Finally, the contact angle of the AMC after the indoor load wheel rolling is still greater than 150°, which proves that the PSC has good wear resistance. In summary, the PSC has good anti-icing properties and road performance.

Application of Machine Learning Models to the Analysis of Skid Resistance Data

This paper evaluates the ability of some state-of-the-art Machine Learning models, namely SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to predict pavement skid resistance. The study encompasses both regression and classification tasks. In the regression task, the aim is to predict the coefficient of friction values, while the classification task seeks to identify three classes of skid resistance: good, intermediate and bad. The dataset used in this work was gathered through an extensive test campaign that involved a fifth-wheel device to measure the coefficient of friction at different slip ratios on different road surfaces, vehicle speeds, tire tread depths and water depths. It was found that the RBF-SVM model, due to its ability to capture non-linear relationships between the features and the target for a relatively small dataset, is the most adapted tool compared with, on one side, MLR, linear SVM and DT models for the regression task and, on the other side, linear SVM and DT models for the classification task. The paper also discusses the strengths and weaknesses of the investigated models based on the underlying physical phenomena related to skid resistance.

Anti-skid durability of porous drainage asphalt mixture based on discrete element

In order to improve safety indicators such as the skid resistance and durability of road, the discrete-element method is used to study the proportion of the asphalt mixture, and the spatial structure of the coarse aggregates and asphalt mortar are compared and analysed. Relevant models are established to compare their pendulum values and structural depth attenuation. When the fine aggregate is limestone and the surface polishing times do not exceed 3500 times, the 7/3S1 asphalt mixture has the best pendulum value. However, as the polishing times continue to increase, the mixture ratio also changes. Compared with the 7/3S1 asphalt mixture, the 8/2S1 asphalt mixture has better effect. When the fine aggregate is replaced by limestone, no matter what form the coarse aggregate is, the pendulum value has been improved to a certain extent comparing with the previous mixture. A2S2, 5/5S2, 7/3S2 and 8/2S2 blends, compared with A2A2, 5/5A2, 7/3A2 and 8/2A2 blends, the pendulum value was increased by 1.7, 0.5, 2.3 and 2.1%, respectively. The study shows that in different proportions of coarse aggregates, as the surface texture richness of the blended aggregate increases, the differences between the aggregates also increases, and skid resistance and durability are also enhanced. The research results provide a blending method to increase the skid resistance and durability of asphalt pavement.

Influence of Morphological Characteristics of Coarse Aggregates on Skid Resistance of Asphalt Pavement.

This research aims to improve the durability of skid resistance of asphalt pavement from the perspective of coarse aggregates based on on-site investigation. Firstly, the skid resistance of six representative actual roads was tested during two years by employing the Dynamic Friction Tester and the attenuation characteristics of skid resistance of different types of asphalt pavements were analyzed. Secondly, core samples were drilled onsite and coarse aggregates were extracted from the surface layer of the core samples. The morphological parameters of coarse aggregates were collected by a "backlighting photography" system and three-dimensional profilometer, and the variation rules of angularity and micro-texture of coarse aggregates were investigated. Finally, the correlation between the morphological characteristics of coarse aggregates and the pavement skid resistance was established based on the grey correlation entropy. The research results show that with the increase in service time, the attenuation rate of skid resistance of asphalt pavement gradually slows down; the angularity of coarse aggregates gradually decreases, and the micro-texture on the wearing surface gradually wears away. The grey correlation entropy between all the micro-texture indexes of coarse aggregates and dynamic friction coefficient, as well as between the roundness and skid resistance is more than 0.7, whereas the correlation between other evaluation indicators and the dynamic friction coefficient is poor, indicating that compared with the angularity of coarse aggregates, the micro-texture affects the skid resistance of actual asphalt pavement more greatly. In engineering applications, the use of coarse gradation, coarse aggregates with high roughness or high anti-wear performance can slow down the attenuation of pavement skid resistance, so that the pavement can maintain superior long-term anti-skidding performance.

Textile-inspired triboelectric nanogenerator as intelligent pavement energy harvester and self-powered skid resistance sensor

Triboelectric nanogenerator (TENG) provides a new idea for harvesting low-frequency energy tire-road interaction and self-powered sensors. A real-time assessment of skid resistance provides valuable information to deliver appropriate driving manner to ensure safety in all weather conditions. However, there is no mature perception technology in the previous study due to the complexity of tire-road interaction. Inventing appropriate devices and solutions for harvesting wasted energy and obtaining skid resistance information in real-time is essential. Here, a textile-inspired TENG is developed, which was integrated into the tire cord for energy harvesting and self-powered skid resistance monitoring. As the theoretical basis, the capability of the proposed textile-inspired TENG for energy harvesting and skid-resistance monitoring was demonstrated based on the coupling of vehicle-road dynamics and the electrodynamics. Consequently, the average power density reaches to 4.219 mW/m2(output power with 82.7 μW). Besides, the short-circuit current waveform of the intelligent tires was firstly applied to distinguish the difference of the pavement skid resistance caused by the roughness characteristics of the pavement structure. The open-circuit voltage amplitude was adopted to evaluate the correlation quantitatively between pavement skid resistance and the TENG's output signal, and the accuracy of this system can reach 96%. This work innovatively proposes a new idea for simultaneously harvesting wasted energy from tire-road interaction and improving the level of traffic safety and expands the application of triboelectric nanogenerators for automatic driving technology.

Effects of road particulate contaminants on pavement skid resistance

To understand the effect of particulate contaminants on pavement skid resistance, the friction numbers of three surfaces under different pollution conditions (particle size, coverage fraction, and shape factor) were measured using a British Pendulum Tester (BPT). The results show that less than 45% coverage fraction of the particles above 0.3 mm even results in almost 50% friction loss at a low speed of 10 km/h. In this situation, the rougher surface has a stronger holding particles capacity. Moreover, the particle size determining the lowest friction level gradually increases as the surface roughness increases. The particle size of 1.18–2.36 mm is the most typical size range leading to a low friction level. In addition, the friction first drops sharply and then increases slowly with the coverage fraction increases, which also conforms to the Stribeck curve.

Effective contact texture region aware pavement skid resistance prediction via convolutional neural network

AbstractThe surface texture of asphalt pavement has a significant effect on skid resistance performance. However, its contribution to the performance of skid resistance is non‐homogeneous and subjects to local validity. There are also a few deep learning models that take into account the effective contact texture region. This paper proposes a convolutional neural network model based on the effective contact texture region, containing macro‐ and micro‐scale awareness sub‐modules. In this study, the asphalt mixture with varying gradations was designed to accurately obtain the effective contact texture region. Then, the textures were disentangled into macro‐ and micro‐texture scales by applying the fast Fourier transform and fed into the model for training. Finally, the area of effective contact texture region was calculated, and the effective contact ratio parameter was then proposed using the triangulation algorithm. The results showed that the effective contact texture area of pavement varies by the asphalt mixture type. The effective contact ratio parameter exhibited a significant positive correlation (Pearson correlation coefficient is 0.901, R2= 0.8129) with skid resistance performance and was also influenced by key sieve aggregate content from 2.36 to 4.75 mm. The data of effective contact texture region following disentanglement significantly released the model performance (the relative error dropped to 1.81%). The model exhibited improved precision and performance, which can be utilized as an efficient, non‐contact alternative method for skid resistance analysis.