Pavement Texture Research Articles

Texture characteristic and its enhancement mechanism in stone mastic asphalt incorporating steel slag

Much uncertainty exists about the mechanisms that how steel slag contributes to the adhesion and hysteresis component of friction. This study explored the enhancement mechanism of pavement texture indexes originating from steel slag based on laser texture scanner. The long-term texture attenuation of the mixture with gradation of stone mastic asphalt (SMA) was also evaluated using accelerated pavement loading tester. The results indicated that the advancement of adhesion mechanism originates from the more asperities and sharper form of asperities due to the rougher texture of steel slag. The improvement of hysteresis component is derived from the larger proportion of positive texture on surface profile generated by incorporation of steel slag. This study provides an opportunity to advance the understanding of enhancement mechanism of surface texture by recycling steel slag.

Deep learning based image reconstruction at any speeds for faster pavement texture measurement using 3D laser technology

ABSTRACT Recently, the super-resolution network PT-SRGAN was developed for faster pavement texture measurement using 3D laser technology at 0.1 mm resolution from only six predetermined faster speeds. This paper further introduces an extended application of PT-SRGAN in combination with bicubic interpolation to enhance the low-resolution 3D pavement data collected at any speeds for more flexible practices. The research team collected two datasets on ten field pavement sections: (1) Dataset-1 was the true 0.1 mm 3D texture data collected at very low speeds (<1.5 mph); and (2) Dataset-2 was the low-resolution 3D texture data collected at three faster speeds (7.5, 15, and 30 mph). The efficacy of the extended PT-SRGAN was validated by reconstructing 0.1 mm data from manually downsized low-resolution Dataset-1 and real low-resolution Dataset-2. First, the superior performance of the proposed method was demonstrated by examining two evaluation metrics calculated between ground truth and reconstructed images at different speeds using Dataset-1. Further, seven 3D areal texture parameters were calculated and averaged for reconstructed Dataset-2, and compared with those of Dataset-1 to demonstrate that the proposed method shows promising performance to enhance real low-resolution 3D texture images to 0.1 mm data for any faster pavement texture evaluation.

Investigation on the fractal characteristic of asphalt pavement texture roughness incorporating 3D reconstruction technology

&lt;abstract&gt; &lt;p&gt;The textural roughness of asphalt pavement surface is an important indicator to characterize pavement skid resistance. In this paper, multi-visual technology was applied to capture the surface image of asphalt pavement which was transformed into a visualized 3D point cloud model. Then, based on the principle of the digital elevation model (DEM), the disordered 3D point cloud is rasterized and projected into a 2D matrix which contains generalized point cloud elevation information. Meanwhile, the 2D matrix is transformed into grayscale to build the equivalent grayscale image. Furthermore, the fractal dimensions were calculated in terms of one-dimensional pavement section profile, two-dimensional grayscale, and equivalent grayscale to characterize the pavement roughness. The results showed that the fractal dimensions are positively correlated with the mean texture depth (MTD), and the fractal dimension of equivalent grayscale has the best correlation with MTD. It should be highlighted that the equivalent grayscale image is directly transformed by the reconstruction of the three-dimensional point cloud, and the grayscale value of each point can represent the elevation of different pavement surfaces. Therefore, the equivalent grayscale image can better reflect the real roughness of the pavement surface. Meanwhile, the proposed method in this paper can effectively reduce the influence of some factors (e.g., light and color, etc..) on the texture detection of the pavement surface.&lt;/p&gt; &lt;/abstract&gt;

Determination of tire rubber-pavement directional coefficient of friction based on contact mechanism considerations

A common shortcoming of the conventional approaches of calculating pavement coefficient of friction (COF) from pavement texture data is that they fail to recognize the directionality of pavement texture and COF. The present study considered a directional tire-pavement contact mechanism to correctly calculate directional COFs of pavements. The proposed approach considered the directional characteristics of pavement texture found in practically all in-service pavements. The concept of topographic prominence was employed to identify those texture peaks that would be in contact with tire rubber, and their directional contributions to tire rubber-pavement sliding friction. The calculated directional COFs were validated with experimentally measured values.

Microsurfacing Pavement Solutions with Alternative Aggregates and Binders: A Full Surface Texture Characterization

The road surface texture is responsible for controlling several quality/safety road indicators, such as friction, noise, and fuel consumption. Road texture can be classified into different wavelengths, and it is dependent on the material used in the paving solution. With the aim of evaluating and characterizing the surface texture of a microsurfacing road pavement, six microsurfacing samples were made in the laboratory with both traditional materials (basaltic aggregates and bituminous emulsion) and with innovative materials from recycling procedures (crumb rubber (CR) and artificial engineered aggregate (AEA)). The characterization was performed through the use of a conoscopic holography profilometer with high precision and post-processing of the profiles detected through consolidated algorithms (ISO standards). We found that the aggregate type plays a very important role in the pavement texture. The binder agent seems to be highly important, but more studies regarding this are necessary. The use of crumb rubber as an aggregate proved to be feasible, and the texture parameters that were obtained were in accordance with the benchmark ones. In addition, the study shows that the use of artificial engineered aggregates does not impair the surface texture. Finally, the use of the texture parameters defined by the ISO standards, together with a statistical analysis, could be useful for defining the surface texture characteristics of microsurfacing.

Data Augmentation and Intelligent Recognition in Pavement Texture Using a Deep Learning

As one of the most important properties of road, the adaption to roads with different macro-textures may significantly affect the autonomous driving technologies since road texture directly affects the skidding resistance and tire noise. Therefore, it is of great significance to detect and analyze the road macro texture with respect to different pavement types and service conditions. Generally, transportation engineers may face problems such as small dataset size, unbalanced dataset, etc. To solve these problems, this study aims to recognize the pavement texture using the deep learning approaches. The pavement texture data was first visualized using image processing methods, and then augmented using the traditional methods as well as a deep learning approach, i.e. Generative Adversarial Network (GAN) model. The Random Forest (RF) algorithm and the DenseNet network were both employed, where the overall classification accuracy of the original dataset was 50% and 59%, respectively, and the accuracy of the data augmented by the traditional methods was 58% and 70%, respectively. Test results show that, after 250,000 generations of training, GAN model was able to generate new pavement texture images with high quality, and the classification accuracy on the test dataset using DenseNet improved to 82%. It was discovered that the deep learning methods had a better performance for pavement texture recognition than manual classification and traditional machine learning methods. Furthermore, it was also found that adding noise in the original datasets as an augmentation method had a negative impact on the classification accuracy.

Evolution of tire-pavement friction with the regular surface macro-texture characteristics and environmental factors using three-dimensional (3D) printing technology

Conventional gradation-based pavement design makes the constructed pavement surface too complex to quantify the contribution of texture on the tire-pavement friction accurately. This study aims to investigate the influence of regular macro-texture characteristics and environmental factors on friction using three-dimensional (3D) printing technology. The variation of friction with different texture shapes, heights, and gaps was investigated under dry and wet conditions. The evolutions of friction under different water film thicknesses and temperatures were also quantitatively analyzed. The results show that all specimens with different texture shapes, heights, and gaps can contribute to large friction under dry conditions, where the British pendulum number (BPN) is larger than 90. The friction sharply decreases under wet conditions, especially for the semi-circular texture specimens. The specimens with positive textures are much better than those with negative textures. The impact of texture shapes is more significant than texture height and gaps under wet conditions, where the texture gaps are relatively insignificant. The friction is slightly linearly increased with the water film thickness while slightly decreasing with the temperature. This study provides a friction-oriented pavement texture design method and manufacturing techonology.

A Novel 0.1 mm 3D Laser Imaging Technology for Pavement Safety Measurement.

Traditionally, pavement safety performance in terms of texture, friction, and hydroplaning speed are measured separately via different devices with various limitations. This study explores the feasibility of using a novel 0.1 mm 3D Safety Sensor for pavement safety evaluation in a non-contact and continuous manner with a single hardware sensor. The 0.1 mm 3D images were collected for pavement safety measurement from 12 asphalt concrete (AC) and Portland cement concrete (PCC) field sites with various texture characteristics. The results indicate that the Safety Sensor was able to measure pavement texture data as traditional devices do with better repeatability. Moreover, pavement friction numbers can be estimated using 0.1 mm 3D data via the proposed 3D texture parameters with good accuracy using an artificial neural network, especially for asphalt pavement. Lastly, a case study of pavement hydroplaning speed prediction was performed using the Safety Sensor. The results demonstrate the potential of using ultra high-resolution 3D imaging to measure pavement safety, including texture, friction, and hydroplaning, in a non-contact, continuous, and accurate manner.

Applications of Artificial Intelligence Enhanced Drones in Distress Pavement, Pothole Detection, and Healthcare Monitoring with Service Delivery

Artificial Intelligence (AI) has fascinated the present study assigned to multiple areas such as distress detection on the pavement, pothole detection, and healthcare. The distress detection on pavement and roads and delivering healthcare and medical services need to be monitored through state-of-the-art technology, i.e., drone technology. Improvement in construction sites and healthcare delivery are of serious concern. Nowadays, computer vision techniques are commonly used in this area utilizing images and videos of construction sites. Due to confined data, researchers are using Unmanned Aerial Vehicles (UAVs) or Drone to get maximum information through 360° monitoring. This review article presents the useful monitoring techniques using AI-enabled drones for scholars around the world. In this comprehensive review, initially, the image acquisition equipment along with the perks and limitations has been presented. Second, the main constraints related to different computer vision techniques are highlighted for detecting distress in the pavement. Then, the possible research solution to some of the distress issues such that detection of pavement texture, cracks or potholes, joint faulting, temperature segregation, and rutting issues are predicted. Finally, the application of AI-enhanced drones in the healthcare field is elucidated which showed their significance. Moreover, in this research, the comparative image analysis of pavement and path hole detection was presented for the collection of detailed information and accurate detection. In the future, the work can also be enhanced to monitor the live pavement distress detection, especially for busy roads and highways. Moreover, an analysis to determine and reduce the costs in the healthcare sectors and organizations is required for future work.

Evolution of texture and skid resistance change of high-friction surface due to differential-polishing undergoing simulated traffic wear

To enhance the understanding of the pavement texture evolution and improve the skid-resistance durability of the pavement, the differential-polishing design of aggregates was introduced and applied to High-Friction Surface Treatment (HFST). Five HFSTs, prepared with 88# calcined bauxite and limestone blended in different ratios, were polished using the rubber tires of the three-wheel polishing apparatus. The texture evolution and skid resistance of HFST undergoing simulated traffic wear were analyzed via bearing area ratio, 2D Power Spectral Density and dynamic friction coefficients (μ40 and μ60). Furthermore, the generation mechanism of differential-polishing was also investigated from the micro-view. The results show that the differential polishing generated by the large difference between the wear resistance of two aggregates can effectively improve the skid-resistance durability of HFST through reducing the wear loss and maintaining excellent surface roughness by continuously constructing new texture structure, particularly for HFST in which the blending proportion of limestone accounts for 25∼50% of the used aggregates. It is also found the textures with wavelengths above 0.7 mm and 1.1 mm are well correlated with μ40 and μ60, respectively.

A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology

Tire–pavement interaction noise (TPIN) accounts mainly for traffic noise, a sensitive parameter affecting the eco-based maintenance decision outcome. Consistent methods or metrics for lab and field pavement texture evaluation are lacking. TPIN prediction based on pavement structural and material characteristics is not yet available. This paper used 3D point cloud data scanned from specimens and road pavement to conduct correlation and clustering analysis based on representative 3D texture metrics. We conducted an influence analysis to exclude macroscope pavement detection metrics and macro deformation metrics’ effects (international roughness index, IRI, and mean profile depth, MPD). The cluster analysis results verified the feasibility of texture metrics for evaluating lab and field pavement wear, differentiating the wear states. TPIN prediction accuracy based on texture indicators was high (R2 = 0.9958), implying that it is feasible to predict the TPIN level using 3D texture metrics. The effects of pavement texture changes on TPIN can be simulated by laboratory wear.

Effects of Pavement Texture Characteristics on Tire–Pavement Noise from Dense-Graded Asphalt Concrete Pavement

Effects of Pavement Texture Characteristics on Tire–Pavement Noise from Dense-Graded Asphalt Concrete Pavement

A review on the influencing factors of pavement surface temperature.

Pavement surface temperature is of great significance to pavement performance and pavement design, as well as the development of cool pavements. The variation of a pavement surface temperature is complicated as it is jointly affected by various factors, including air temperature, solar irradiance, wind speed, and pavement texture. This study overviews the internal and external factors that affect the pavement surface temperature in the field. It is found that air temperature is the main external climatic factor affecting the pavement surface temperature during the course of a day. Although solar radiation dictates the thermal partition at the pavement surface, it mainly influences daytime pavement temperature but vanishes at night. Pavements in calm weather can be 3-10°C hotter than those in windy weather, depending on the time of the day and the season. Other external factors such as passing vehicles also influence the pavement surface temperature at a degree 1-3°C. Also, the shading effect of urban trees can affect pavement surface temperature and urban microclimate. Internal factors that vary pavement surface temperature include albedo, thermal conductivity, heat capacity, and emissivity. Among them, albedo controls the pavement surface temperature while other factors play a secondary role. The results of this review provide a scope of research for developing sustainable and advanced solutions for future municipal pavement construction and urban heat island (UHI) mitigation.

Anti-Skid Characteristics of Asphalt Pavement Based on Partial Tire Aquaplane Conditions.

This study presented a finite element model of radial tire–asphalt pavement interaction using ABAQUS 6.14 software to investigate the skid resistance properties of asphalt pavement under partial tire aquaplane conditions. Firstly, the pavement profile datum acquired by laser scanning were imported to Finite Element Analysis (FEA) software to conduct the pavement modeling. Secondly, a steady state rolling analysis of a tire on three types of asphalt pavements under drying conditions was carried out. Variation laws of the friction coefficient of the radial tire on different pavements with different pavement textures, tire pressures, and loads on the tire were examined. Subsequently, calculation results of the steady state rolling analysis were transmitted to dynamic explicit analysis, and an aquaplane model of a radial tire on asphalt pavements was built by inputting the flow Euler grids. The tire–pavement adhesive characteristics under partial aquaplane conditions are discussed regarding the aquaplane model. Influences of the thickness of water film, the texture of asphalt pavement, and the rolling speed of the tire on the vertical pavement-tire contact force are analyzed. It is found that the vertical contact force between open graded friction course (OGFC) pavement and tire is the highest, followed by stone mastic asphalt (SMA) pavement and dense graded asphalt concrete (AC) pavement surface. The vertical contact force between tire and pavement will be greatly reduced, even with increasing speed or water film thickness. As tire speed increases from 70 km/h to 130 km/h, the tire–pavement contact force is reduced by about 25%. Moreover, when the thickness of water film increases from 0 (dry condition) to 4 mm and then to 12 mm, the vertical contact force reduced 50% and 15%, respectively, compared with under the dry contact condition. This study provided a key theoretical reference for safe driving on wet pavements.

Space of basis functions to retrieve road surface reflection tables from luminance maps

Road surface reflection tables ( r-tables) relate scene illuminance to luminance seen by a car driver. They are important for many road lighting tasks accounting for road optical properties, such as new illumination design, new pavement texture or lighting design software, to reduce energy consumption without losses on safety and visibility. This paper aims first at finding a space of basis functions to describe r-tables. From a database of 34 r-tables covering a large variety of pavements, a principal component analysis allows to construct a 33-dimensional space, basis for r-table representations. From that statistical model, a method is exposed to retrieve r-table from a luminance map. The estimated r-table is then used to calculate a reconstructed luminance map. Road lighting quality criteria are also derived and they demonstrate the relevancy of the estimated r-table. Finally the model is tested with noisy input data and it remains stable and reliable, making it applicable with experimental luminance maps.

3D Imaging based pavement texture evaluation

ABSTRACT Pavement texture is an important surface characteristic for safety evaluation. Through a series of laboratory and field testing, this study presents an automatic non-contact method using high resolution 3D images for accurate and efficient pavement texture evaluation. Firstly, a 3D laser device was applied to capture pavement texture. Subsequently, the 3D texture images were denoised using image processing methods. Then, the 3D seed filling algorithm was developed to calculate mean texture depth (MTD) and mean profile depth (MPD). The computed MTD and MPD values from 3D images showed a good correlation with results of sand patch testing and high repeatability. Further, through the follow-up field testing, the proposed method using 3D images showed better performance in texture evaluation than the 2D texture profiler or sand patch testing. The results indicate it is accurate and efficient to evaluate pavement texture using high resolution 3D images in a non-contact and automatic manner.

Study of Texture Indicators Applied to Pavement Wear Analysis Based on 3D Image Technology.

Pavement texture characteristics can reflect early performance decay, skid resistance, and other information. However, most statistical texture indicators cannot express this difference. This study adopts 3D image camera equipment to collect texture data from laboratory asphalt mixture specimens and actual pavement. A pre-processing method was carried out, including data standardisation, slope correction, missing value and outlier processing, and envelope processing. Then the texture data were calculated based on texture separation, texture power spectrum, grey level co-occurrence matrix, and fractal theory to acquire six leading texture indicators and eight extended indicators. The Pearson correlation coefficient was used to analyse the correlation of different texture indicators. The distinction vector based on the information entropy is calculated to analyse the distinction of the indicators. High correlations between ENE (energy) and ENT (entropy), ENT and D (Minkowski dimension) were found. The CON (contrast) has low correlations with HT (macro-texture power spectrum area), ENT and D. However, the differentiation of ENE and HT is more prominent, and the differentiation of the CON is smaller. ENE, ENT, CON and D indicators based on macro-texture and the corresponding original texture have strong linear correlations. However, the microtexture indicators are not linearly correlated with the corresponding original texture indicators. D, WT (micro-texture power spectrum area) and ENT exhibit high degrees of numerical concentration for the same road sections and may be more statistically helpful in distinguishing the characteristics of the pavement performance decay of the road sections.

Asphalt pavement macrotexture reconstruction from monocular image based on deep convolutional neural network

AbstractPavement macrotexture is one of the major factors affecting pavement functions, and it is meaningful to reconstruct the pavement macrotexture rapidly and accurately for pavement life cycle performance and quality evaluation. To reconstruct pavement macrotexture from monocular image, a novel method was developed based on a deep convolutional neural network (CNN). First, the red‐green‐blue (RGB) images and depth maps (RGB‐D) of pavement texture were acquired by smartphone and laser texture scanner, respectively, from various asphalt mixture slab specimens fabricated in the laboratory, and the pavement texture RGB‐D dataset was established from scratch. Then, an encoder–decoder CNN architecture was proposed based on residual network‐101, and different training strategies were discussed for model optimization. Finally, the precision of the CNN and the three‐dimensional characteristics of the reconstructed macrotexture were analyzed. The results show that the established RGB‐D dataset can be used for training directly, and the established CNN architecture is plausible and effective. The mean texture depth and f8mac of the reconstructed macrotexture both correlate with the benchmarks significantly, and the correlation coefficients are 0.88 and 0.96, respectively. It could be concluded that the proposed CNN can reconstruct the macrotexture from monocular RGB images precisely, and the reconstructed macrotexture could be further used for pavement macrotexture evaluation.

Automatic pavement texture measurement using a new 3D image-based profiling system

Pavement surface texture affects safety and comfort of road users. Pavement skid resistance, surface drainage, noise generation and vehicle depreciation are all affected by the pavement surface texture. 3D evaluation is one of the most accurate methods of measuring pavement texture characteristics. In this paper, a 3D automatic device based on a cumulative imaging technique is presented for pavement texture measurement. The presented device has an innovative surface friction reduction system. This system evaluates the effect of different level of humidity on pavement surface texture characteristics. 3D Point cloud models of pavement texture are generated using Structure from Motion (SfM) technique. Then, evaluation indices are extracted based on morphological characteristics of texture. Finally, a new method is presented to evaluate the pavement texture in case of rainfall. A new method for 3D calculation of MDE and RMSR has been proposed. Examination of the relationship between these indices and MTD shows that evaluation of pavement texture and its effect on other related characteristics needs to consider different indices. Experimental tests showed a high correlation between the results of presented system and sand patch test.

A new predictive skid resistance model (PSRM) for pavement evolution due to texture polishing by traffic

We propose a new model to predict the evolution of pavement skid-resistance due to traffic polishing. The model accounts for polishing (by coupling a contact model with a wear law equation) and friction mechanisms. The contact model considers the tire operating conditions (including load, speed, and slip ratio), the tire geometry and mechanical behavior of the rubber tread, the pavement texture, and the physical properties of contaminants (i.e. the thickness and viscosity of fluids). The wear law considers the types of aggregates in the pavements (particularly their mineralogical composition) and the pressure distribution within the tire/pavement contact area. The friction component considers the mechanical behavior of a rubber measuring pad, the operating conditions during pad/pavement contact, and the pavement texture. The new predictive skid resistance evolution model is a numerical computational code that resolves pavement texture topographies at different polishing stages and the corresponding skid resistance levels. The model outcomes have been validated against repeated profilometer measurements on three mosaic pavements made of different types of aggregates subjected to the “Wehner-Schulz” procedure (that polishes and measures friction). The modeled predictions performed well against the captured changes in pavement textures during the polishing process, with slight differences in their absolute values potentially due to probable limitations of the wear law. The ability to predict the evolution of pavement skid resistance has transformative potential for tailored forecasting and proactive interventions to be undertaken across road networks.