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Measurement and Modeling of Skid Resistance of Asphalt Pavement: A Review
... However, what is characterized by this model is usually the pavement friction coefficient under full-locked state, and large errors exist in the practical application [11]. In addition, the microtexture has not been fully considered in the above commonly used pavement friction coefficient models, which will influence the prediction accuracy of friction coefficient [12][13][14][15][16][17]. ough applicable to engineering detection, they are not accurate enough when used in evaluation and research on pavement antiskid performance. ...
The correlations between pavement texture and tire pressure with the actual tire-road contact area were first investigated according to the tire-road static contact characteristics; on this basis, the influence mechanisms of speed and pavement texture on the pavement friction coefficient were systematically explored from the angle of tire-road coupling system dynamics via the self-developed dynamic testing system of tire-pavement friction. By integrating the above influence factors, the BP neural network method was applied to the regression of the prediction model for the asphalt pavement friction coefficient. Through the comparison between the model measured value and estimated value, their correlation coefficient R2 reached 0.73, indicating that this model is of satisfactory prediction accuracy and applicable to the antiskid design of asphalt pavement.
The pavement skid resistance is crucial for ensuring driving safety. However, the reproducibility and comparability of field measurements are constrained by various influencing factors. One solution to these constraints is utilizing laser‐based 3D pavement data, which are notably stable and can be employed to estimate pavement skid resistance indirectly. However, the integration of tire–road friction mechanisms and deep neural networks has not been fully studied. This study employed spatial‐channel attention mechanisms to integrate frictional domain knowledge and convolutional neural networks (CNNs) that predict the friction coefficient as the output. The models’ inputs include 3D texture data, corresponding finite element (FE) simulation outcomes, and 2D wavelet decomposition outcomes. An additional spatial attention (ASA) mechanism guided the CNNs to focus on the tire–road contact region, using tire–road contact stress from FE simulation as domain knowledge. Multi‐scale channel attention (MSCA) mechanisms enabled the CNNs to learn the channel weights of 2D‐wavelet‐based multi‐scale inputs, thereby assessing the contribution of different texture scales to tire–road friction. A multi‐attention and feature fusion mechanism was designed, and the performances of various combinations were compared. The results showed that the fusion of ASA and MSCA achieved the best performance, with a regression R ² of 0.8470, which was a 20.25% improvement over the baseline model.
To address the challenge of simultaneously achieving accuracy at low speeds and stability at high speeds in the lateral control of unmanned vehicles, this paper proposes a feed-forward adaptive weight controller based on model predictive control (FFMPC) aimed at enhancing both accuracy and adaptability during the lateral control of these vehicles. By considering the coupling characteristics of trajectory and heading errors, we derive an error dynamics model that incorporates path curvature and its rate of change into the conventional dynamics model. The main control system is constructed using a model predictive controller with decoupling characteristics, obtained through Taylor series expansion. Additionally, a Sliding Mode Controller (SMC) based on an integral proportional-integral-derivative (PID) sliding mode surface is integrated as a feed-forward component of the control system. To prevent overfitting in the controller, we define an error tolerance threshold. The front steering compensation angle is calculated using the improved preview model, which effectively adjusts the output of the primary controller and enhances system robustness under extreme operating conditions. In this study, trajectory error minimization is employed as a key performance metric for simulation. Comprehensive simulations are conducted in the Carsim and Matlab/Simulink environment under various operating scenarios for comparison and validation. The experimental results indicate that, compared to traditional controllers, the FFMPC demonstrates superior accuracy, robustness and rapid convergence of kinematic states.
In India, 37 kms of national highways are being newly constructed every day and in the last seven years itself, the length of national highways have almost doubled in the country. The road pavement condition plays a crucial role on safety, comfort, traffic, travel times, vehicle operating cost and emission levels. In the present study, an extensive literature survey was carried out on the functional evaluation of pavements which usually involves the three components, namely, the distresses such as cracks, potholes, surface roughness, and skid resistance. It was found that in developed countries like USA, Europe, and China, the visual condition survey is gradually being replaced by use of advanced technologies like Light detection and ranging (LIDAR), where the accurate three-dimensional (3D) model of the road surface was created and using which, mapping of common surface distresses was done, which will eliminate any subjectivity of the evaluator. Though smartphones were used for functional evaluation of pavements in India, however studies using LIDAR could not be found. This necessitates the need for such studies in India, where the manual evaluation is a labor-intensive and time-consuming task considering the length of roads in the country. In addition to the review of literature, a pilot study was also carried out in Vellore, India using a 3D laser scanner in order to explore its usefulness in pavement distress evaluation. A point cloud of 8.23 million data points with X, Y, and Z values was collected and using which the pothole characteristics were calculated. It was found that the LIDAR derived point cloud data has very high potential in mapping the pothole and other distresses as it can record the minute height differences of even 1 mm.KeywordsPavement functional evaluationLIDAR scannerDistress study
To curb the problems of rapid attenuation on skid-resistance and service-life shortening of limestone bituminous pavement, a surface treatment incorporating sand-containing lithium-silicate composite solution (LSCS) was developed utilizing lithium silicate, styrene acrylic, silicone acrylic and silicon carbide in this study. During the research, the processing property, road performance and the corresponding enhancement mechanism were analyzed and investigated through a combination of macroscopic properties test and micro-structure analysis approaches; meanwhile the durability test was implemented due to the long-term exposure in uncontrolled environments. The experimental results demonstrated that the sand-containing LSCS could significantly delay the skid-resistance attenuation of in-service bituminous pavement after shot-blasting through enhancing the anti-abrasion property and retard the decay rate of texture depth concerning limestone aggregate. Furthermore, the sand-containing LSCS might possess good temperature variation resistance, anti-erosion ability, acid corrosion resistance, as well as impermeability. In comprehensive consideration of the performance characteristics, the spraying dosage of 200 g/m² LSCS using styrene-acrylic and silicone-acrylic could be recommended to applying for the skid-resistant maintenance treatment for bituminous pavement. The study laid a foundation for the investigation of skid-resistant surface treatment utilizing lithium silicate as a preventive pavement maintenance method.
Skid resistance is a key road safety factor indicating the provided friction of a pavement’s surface, which deteriorates over its service life mainly due to traffic polishing. Hence, the polishing behaviour of a pavement’s surface course is important for the provided road safety. For this reason, there have been many studies related to the assessment of polishing behaviour of asphalt mixtures. Besides, during the last decades the introduction of recycled materials in pavements has increased in order to face environmental challenges. However, investigations regarding the surface course are mainly centred around aspects of structural performance of the recycled materials. Contrariwise, it appears that the investigation for the provided skid resistance of asphalt mixtures containing recycled materials is limited. On these grounds, the present study investigates skid resistance performance through the polishing behaviour of asphalt mixtures containing recycled materials under the effect of traffic, simulated by the Wehner/Schulze machine. The investigated mixtures include conventional Hot-Mix Asphalt (HMA), as well HMA containing varying contents of Reclaimed Asphalt Pavement (RAP) material or recycled rubber in the form of Crumb Rubber (CR) originating from waste tyres. Analysis results demonstrate that the friction level of the tested asphalt mixtures containing RAP or CR is slightly lower than the conventional ones, but their overall performance seems to present an almost similar trend under the polishing effect. Generally, the study suggests a methodological approach towards the assessment of the skid resistance potentials for asphalt mixtures of a surface course that contain the investigated recycled materials, in an effort to act as a precursor for the enrichment of the related knowledge gap.
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