Skid Resistance Prediction Research Articles

Prediction of the Skid Resistance of Recycled Hot Mix Asphalt Mixture

Typically, the skid resistance of pavement surfaces is used to indicate road safety levels. The initial value and long-term behavior of skid resistance can be reportedly determined by modulating asphalt mixture properties during their mix design process. In this study, a skid-resistance prediction equation for recycled asphalt concrete was formulated based on ANOVA test results. An accelerated polishing device was used to replicate the abrasion encountered by road surfaces owing to vehicle–tire interactions for characterizing the skid resistance of recycled asphalt concrete. Notably, key parameters, including initial and terminal values and reduction rate of skid resistance induced by vehicle-related abrasion, were determined. These findings suggest that the initial values and reduction rate of skid resistance can be estimated from the mixed binder properties and initial MTD value. However, the final/terminal skid resistance predicted using aggregate gradations has a limited degree of confidence. To further improve the accuracy of the prediction equation, additional data collection and validation tests are required. In addition, field tests using recycled asphalt concrete are necessary to refine the accuracy and precision of the prediction equations for practical road construction scenarios. This study represents a significant step toward the development of safe and effective road infrastructure.

Prediction of Skid Resistance of Steel Slag Asphalt Mixture Based on Grey Residual GM(1,1)-Markov Model

Prediction of Skid Resistance of Steel Slag Asphalt Mixture Based on Grey Residual GM(1,1)-Markov Model

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.

Surface particle lubrication and wearing mechanism for asphalt pavement with aeolian sand in deserts

• Wear and lubrication mechanism of aeolian sand on asphalt pavement was studied. • Rolling-sliding friction exists between tire/road surface with particulate contaminants. • Angularity of the sand accelerates the wear of the asphalt membrane on pavement. • The wearing rate is proposed to evaluate the third body wear effect of aeolian sand. Particulate pollutants have a significant lubricating and abrasive effect on asphalt pavements. In this research, an experimental evaluation using an indoor accelerated wearing apparatus was used to simulate the wearing of Aeolian sand on asphalt pavements in a desert area. The British Pendulum Tester and a 3D scanner have also been used to examine the changes in skid resistance and texture morphology over time. Then, to evaluate the wearing process of sand particles on pavements, the wearing rate was proposed, and ultimately, a prediction model for the pavement with aeolian sand was constructed. The results shows that the wear rates of clean pavement, 50%Ψ and 100%Ψ of sand are 25.6%, 28.6% and 33.8% respectively. In terms of particle lubrication, the pavement is in a mixed lubrication state at 50%Ψ of sand; and in a fluid lubrication state when aeolian sand completely covers asperities of the pavement. From the wearing point of view, tire wearing on clean road is two-body wear; when there is aeolian sand between the tire/road contact interface, it is abrasive wear. The skid resistance prediction model developed by integrating the Pearson Correlation and Ridge Regression methods to identify-five texture indexes (ZMPD, Z Ra , λ q , Z Rs , S dr ) and the British Pendulum Number (BPN) can effectively predict the lubricating and wearing effects of aeolian sand on the pavement.

A simplified skid resistance predicting model for a freeway network to be used in a pavement management system

ABSTRACT The available skid resistance, or friction, in a pavement surface is a vital parameter for functional evaluations of roads due to its relation with crashes. Therefore, highway administrations must collect friction data on their road network to provide safe roads to users. Additionally, a prediction model that can forecast the available skid resistance in each road segment is necessary for an efficient pavement management system (PMS). The aim of this paper is to develop a skid resistance prediction model for the bituminous pavements of the motorway network of federal state of Bavaria, in Germany, with information that is typically available in a PMS: the Annual Average Daily Traffic , the Annual Average Daily Heavy Traffic , and the number of lanes in each segment. Despite its simplicity, with 6410 road segments of 2 and 3 lanes of the Bavarian motorway network, the model achieves a determination coefficient (R2 ) of 0.405. If information about the surface layer material is added, R2 increases to 0.480. Consequently, apart from predicting the minimum available friction in each lane in a motorway, the study underlines the necessity that a PMS should contain the recommended elements and additional surface layer material, because the quality of the prediction improves.

Promoting the pavement skid resistance estimation by extracting tire-contacted texture based on 3D surface data

The pavement skid resistance is of vital importance for road safety and maintenance. The existing studies mainly calculates various parameters of pavement texture to evaluate the skid resistance. However, most researchers mainly calculate the whole texture parameters, but rarely consider which part of the texture is actually involved in the friction. This paper studies the measurement and calculation of the tire-pavement contact area and its influence on the skid resistance prediction. In this study, we proposed a set of methods to predict the rubber-contacted pavement area percentage and to extract the rubber-contacted texture. Besides, the skid resistance prediction models based on high-resolution three-dimensional (3D) surface data of the whole and the contacted texture are built and compared. The results show that when the rubber-contacted percentage is set at 20% or ̃% (the predicted percentages), the accuracy of the prediction model is higher. It is demonstrated that the performance of the skid resistance prediction will be improved by extracting the contacted texture. Also, the amount of data can be considerably reduced, which will effectively relieves computational cost.

Developing a skid resistance prediction model for newly built pavement: application to a case study of steel bridge deck pavement

Skid resistance is vital to road safety. The skid resistance of steel bridge deck pavement (SBDP) is particularly important due to the special operating conditions and unique structure features for bridges. This study aims to propose an effective method to predict the skid resistance of newly built SBDP at the design stage. First, the experiment was designed by Taguchi method to get the skid resistance-related data. Secondly, sensitivity analysis was conducted to evaluate the influence of material design and construction factors on the skid resistance. Finally, a prediction model based on GA-BP neural network (back propagation network optimised by genetic algorithm) was developed. Results show that the mixture design parameters have stronger influences on the skid resistance than the construction factors of SBDP. From this study, the 9-14-1 (Input neurons – Hidden neurons – Output neuron) GA-BP model with the population size of 60 could yield an acceptable prediction result for the skid resistance.

Asphalt pavement friction coefficient prediction method based on genetic-algorithm-improved neural network (GAI-NN) model

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a genetic-algorithm-improved neural network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover the missing data and perform denoising. In particular, these data were filled using cubic spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed by modifying the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.

A skid resistance prediction model for an entire road network

This article predicts the available minimum skid resistance in the road network of Biscay (Spain) with data collected in the summer season when friction values are at a minimum. Firstly, it was observed that pavement structure does not influence skid resistance. Therefore, roadway segments with available data about the surface layer of single or double carriageway roads were analyzed. Two models were developed: 1) short model with only the surface material, average annual daily traffic, and number of lanes (no pavement history required) and 2) a long model which adds the required Polished Stone Value to improve the prediction. These models can help road agencies to identify the roads where lower skid resistance values are more probable to be obtained to focus their attention and efforts.

Finite Element Method-Based Skid Resistance Simulation Using In-Situ 3D Pavement Surface Texture and Friction Data.

Skid resistance is an important surface characteristic that influences roadway safety. Various studies have been performed to understand the interaction between pavement and tires through numerical simulation for skid resistance prediction. However, the friction parameters required for simulation inputs are generally determined by objective assumptions. This paper develops a finite element method (FEM)-based skid resistance simulation framework using in-situ 3D pavement surface texture and skid resistance data. A 3D areal pavement model is reconstructed from high resolution asphalt pavement surface texture data. The exponential decay friction model is implemented in the simulation and the interface friction parameters required for the simulation are determined using the binary search back-calculation approach based on a trial process with the desired level of differences between simulated and observed skid numbers. To understand the influence of texture characteristics on interface friction parameters, the high-resolution 3D texture data is separated into macro- and micro-scales through Butterworth filtering and various areal texture indicators are calculated at both levels. Principal component analysis (PCA) regression analysis is conducted to quantify the relationship between various texture characteristics and the interface friction parameters. The results from this study can be used to better prepare the inputs of friction parameters for FEM simulation.

Skid resistance prediction for new two-lane roads

Skid resistance is a vital issue in pavement management, mainly in relation to road safety; hence, road agencies must assure a minimum friction level of their road networks. The regional government of Biscay (Spain) uses a sideways force coefficient routine investigation machine to assess the present pavement surface condition and to allocate the available funding better. The aim of this paper is to develop a deterioration model to predict the minimum skid resistance in the rural two-lane bituminous roads of Biscay by means of the factors that affect it. In an attempt to include all possible variables that could influence the friction, 23 sections of new roads in Biscay, constructed over the last 25 years, were selected, with different pavement structures (flexible and semi-rigid pavements), ages, surface layers and traffic volumes; a multiple linear regression was then performed. It was concluded that only the average annual daily traffic of heavy vehicles and the required minimum polished stone value of aggregates reflect their importance and, consequently, they are the only variables introduced in the model. The age of pavement, total thickness of bituminous layers and average annual daily traffic of all vehicles showed no influence and were discarded. The proposed model can forecast future skid resistance.

Skid resistance determination for pavement management and wet-weather road safety

Abstract Road accidents during wet weather have been a topic of major concern of road engineers in regions of wet-tropical climate and in other parts of the world during the wet season of the year. Road safety studies indicate that approximately 20% of all road accidents occurred during wet weather, and that the skid resistance of wet pavements have a major influence on the occurrences of wet-weather accidents. Monitoring of wet pavement skid resistance has been an integral part of a typical pavement management system. However, because of the lack of prediction capability of pavement skid resistance under various rainfall intensities, the minimum skid resistance threshold for safe wet-weather driving has been specified by highway agencies based on either engineering judgement or past experience. It is shown in this paper that the single-point minimum skid resistance threshold is inadequate to offer a complete description of the skid resistance performance of the pavement sections in question for effective management of a road network. It is unable to assess the risk involved in an actual wet-weather condition where the pavement surface water-film thickness and vehicle speed are different from standard test conditions. This limitation of the current system of specifying a minimum skid resistance threshold can be overcome by adopting a theoretically sound approach to represent pavement skid resistance under different conditions of water-film thickness and vehicle speed. This paper describes the theoretical basis of the approach and the development of a mechanistically derived three-dimensional finite-element skid resistance simulation model to predict skid resistance. The application of the proposed approach and the skid resistance prediction procedure in pavement management system and wet-weather driving safety assessment is presented.

Developing Field Skid Resistance Prediction Procedure for Louisiana Pavements

Research shows that by improving pavement surface friction resistance, wet pavement crashes can be reduced or prevented. However, the current asphalt mixture design procedure does not directly consider friction as a design requirement. The main objective of this study was to develop a procedure to predict the pavement field skid resistance based on design traffic input, aggregate polishing, and mixture properties commonly available during a mix design. Twenty-two asphalt pavement test sections were considered in this study. The selected asphalt mixtures consisted of eight commonly used aggregates and four typical mix types: 12.5-mm Superpave®, 19-mm Superpave, stone mastic asphalt, and open-graded friction course. Field measurements were conducted with the dynamic friction tester, circular texture meter, and locked-wheel skid tester devices, while the coarse aggregate’s polished stone values were determined by using the accelerated polishing test in the laboratory. Statistical analysis of various field and lab test results led to the development of a procedure for predicting pavement end-of-life skid resistance based on the aggregate blend’s polished stone values, gradation parameters, and design traffic input. In addition, a new aggregate friction rating table was developed so that new aggregate sources can be certified by laboratory dynamic friction tester measurements to fulfill the required mixture friction requirements. The new procedure will allow engineers to check whether a mix design with a selected blend of aggregates would meet field friction requirements during the mix design stage.

Incorporating Surface Microtexture in the Prediction of Skid Resistance of Flexible Pavements

Monitoring and managing the skid resistance of the highway network is necessary for controlling and reducing the number of road accidents. High-speed measurements of surface texture can be used as a surrogate parameter for controlling a pavement's surface friction on the highway network. The two components of surface texture that affect skid resistance are the macrotexture and the microtexture. However, the technologies commonly used for measuring pavement texture at highway speeds account only for the macrotexture. This study explored ways to characterize the microtexture of pavement surfaces with the main objective of quantifying the effect of accounting for both components of the texture on the prediction of skid resistance, as measured by a British pendulum tester. Various methods for characterizing the microtexture were compared to determine which one better predicted surface friction. The study used field measurements of surface texture and friction performed on various in-service flexible pavements. The surface microtexture was characterized by a series of texture parameters calculated in both the spectral and the spatial domains. The impact of incorporating the microtexture on the prediction of the British pendulum number was evaluated through analysis of a series of models specified by each of the proposed parameters. The results of the analysis showed a drastic improvement in predicting the British pendulum value when the authors accounted for both components of the surface texture, as opposed to only the macrotexture. In a comparison of the analyzed methods for characterizing the microtexture, the use of spectral parameters led to the best prediction of the pavement surface friction. A series of recommendations is provided for the calculation of microtexture parameters.

Influence of different polishing conditions on the skid resistance development of asphalt surface

The development of the skid resistance of asphalt road surfaces was simulated in a laboratory. Asphalt plates were taken from a test track. They were polished using the Aachen Polishing Machine (APM) with real tires under various conditions and their skid resistances were determined using the Wehner/Schulze machine (W/S). The process of binder removal and polishing of the aggregate can be accurately described using a five-parameter skid resistance model. It was observed that the asphalt plates showed different skid resistance behaviors and reached different final states under varying polishing conditions. It can be concluded that wetness conditions, polishing agents and the affinity between aggregate and bitumen were decisive for the skid resistance development. They could influence the removal speed of the bitumen film, the peak value as well as the final value of the skid resistance. The polishing using the W/S test could lead to an overestimation of the predicted skid resistance level when the grain size and composition of dust on the road surface deviates excessively from that of the standard polishing agent applied in the W/S test. For a realistic skid resistance prediction, it is therefore recommended that the asphalt specimen should be polished using a polishing agent with similar gradation to that of the dust on the road surface.

Development of a Model for Asphalt Pavement Skid Resistance Based on Aggregate Characteristics and Gradation

Many research studies and field experiences have shown a strong relationship between wet-weather accidents and pavement skid resistance. Therefore, measurement methods and models are needed to evaluate the safety level of driving on an asphalt pavement surface during its service life. The objective of this study was to develop a skid resistance prediction model based on measurable quantities such as aggregate shape characteristics, aggregate gradation, aggregate resistance to polishing, and traffic level. To achieve this objective, the skid number (SN) of asphalt pavement sections and traffic data were acquired and analyzed. In addition, statistical analysis was conducted to determine the relationship between different aggregate properties, pavement surface characteristics, and the measured SN values. The aggregate properties were measured using conventional test methods (acid insolubility, magnesium soundness, micro-Deval, and British polish value), and the Aggregate Imaging System (AIMS). The pavement surface characteristics were measured using the dynamic friction tester and circular texture meter. The statistical analysis led to the development of a new model for predicting the asphalt pavement SN as a function of traffic level, initial and terminal aggregate texture values measured using AIMS, and aggregate gradation described using the two-parameter Weibull distribution function.