Mean Texture Depth Research Articles

Analysis of Surface Texture of Asphalt Mixture with Sand Circle Method

A road is usually defined as an infrastructure connecting a zone to other zones. As service and safety are essential for vehicle users, any conditions related to the pavement, including surface texture, are considered. The importance of this matter is that many researchers or practitioners are concerned with the quality of asphalt mixtures, whether structural or functional, which affect the safety and security of road users, mainly related to the texture of the pavement surface. This research aims to determine the texture depth (MTD, Mean Texture Depth). The research method used is the circle sand method, which is tested using the Sand Patch tool. The asphalt mixture type uses AC-WC with a composition of 39% coarse aggregate, 54.5% fine aggregate, and 6.5% filler. Optimum Asphalt Content of 6.28% and Density of 2.28 were obtained, and texture depth of 0.549 mm was more significant than 0.45 mm (Bina Marga 2010 revision 3). The conclusion is based on the design composition of the aggregate mix in the AC-WC asphalt mixture, which has a safe texture.

Image processing and artificial neural network based determination of surface mean texture depth on lab-controlled chip seal pavement samples.

Because surface texture is nearly the sole indicator of pavement functional properties and highly correlated with critical operational characteristics of roadways like traffic noise and safety, the change in pavement surface texture because of traffic loadings and environment has to be evaluated routinely. There are numerous direct or indirect evaluation techniques in the market. However, most of these methods have some limitations like requiring lane closure or being expensive. In this study, a 2D image processing method was established to estimate the surface mean texture depth (MTD) of chip sealed pavements. We produced chip sealed pavement samples in the laboratory with different aggregate type, shape, and size ranging between 2 and 19mm to cover wide range of live conditions. Two well-known conventional test methods, Sand Patch (SP) and Hydrotimer (HT), were used to determine MTDs of chip seal samples. Subsequently numerous photos were taken on surface of the samples with a camera for 2-D image processing that was done based on surface void ratio (SVR) approach. With the image processing, SVR of all samples were determined. At the point of whether there is a relationship or not, correlation analysis was made between the MTDs obtained with SP and HT and the data obtained by SVR approach with the artificial neural network method. The results show that the proposed SVR approach construed on 2D image processing method can be a reliable alternative to evaluate the surface texture of pavements.

Road Surface Texture Evaluation and Relation to Low-Speed Skid Resistance for Different Types of Mixtures

Pavement skid resistance is significant for driving safety. British Pendulum Number (BPN) is commonly used as a low-speed skid resistance indicator, whereas sometimes it is impractical for data collection on roads in service. Since skid resistance is greatly affected by pavement surface texture, this research aims to evaluate pavement surface texture comprehensively and estimate the low-speed friction BPN from road surface texture on macro- and micro- scale. Asphalt Concrete (AC) and Stone Mastic Asphalt (SMA) were included. Road surface texture was evaluated from four aspects, texture depth, amplitude-related Root Means Square (RMS), elevation variances corresponding to different wavebands and texture spectral analysis. Texture depth indicators include Mean Texture Depth (MTD) and Mean Profile Depth (MPD). Elevation variances with three wavebands, from 5 mm to 50 mm, from 0.5 mm to 5 mm and from 0.024 mm to 0.5 mm respectively, were obtained. The results show that MPD is well correlated with MTD. Elevation variances with different wavebands demonstrates that the elevation variance of macro-texture with long wavelengths from 5 mm to 50 mm dominates the total variance. Spectral analysis shows that texture level is larger when the wavelength is beyond 4 mm, which is consistent with elevation variances. A linear regression between BPN and single texture index, as well as multiple linear regression analysis were conducted. The former regression result indicates that it is not feasible to estimate BPN using single index due to low correlation coefficient R2. The latter shows that the BPN can be estimated from texture levels corresponding to 64 mm and 2 mm, and the micro-texture. The R2 can be up to 0.684. This research will contribute to fast acquisition of BPN from pavement surface texture, thus improving skid resistance.

The evolution of tire-road wear particles and road surface texture under rolling friction

Traffic-related non-exhaust emissions are an important part of air pollution, which can adversely affect human health when inhaled. Tire-road wear is a significant source of these emissions and originates from tire-road friction. To gain a deeper understanding of the relationship between tire-road wear particles and road surface texture under rolling friction, this study conducted long-term durability experiments using a Model Mobile Load Simulator at 1/3rd scale to form a closed environmental test system. The experiments were conducted under 30°C, 50 % relative humidity, 0.7 MPa tire-ground pressure, and 5.4 km/h rolling speed. Based on an analysis of asphalt concrete-13 Marshall samples’ British Pendulum Number (BPN) value, Mean Texture Depth (MTD), wear particle size distribution, particulate matter (PM) concentration, and thermogravimetric analysis during 1600,000 rolling times, this study draws the following conclusions: This indoor research has indicated that the weight ratio of tire and asphalt wear to aggregates and mineral fillers is approximately 4.5:5.5 (after stabilization). When the tire contacts the aggregate exposed after asphalt film peeling, it significantly increases PM10 concentration. Under the rolling friction of the tire-road contact, the quality loss of asphalt concrete follows an exponential decay curve with an R2 of 0.99. Road (asphalt concrete pavement) wear per square meter of tire-road contact was 5.32×10^−4 g and 2.13×10^−4 g (before and after asphalt film peeling). It is hoped that this study will contribute to the field of tire-road wear, including non-exhaust emissions, and provide methods and theoretical support for mitigating re-suspension dust.

A Comparative Study on the Texture of Exposed Aggregate Concrete (EAC) Pavements Using Different Measurement Techniques.

This paper presents issues related to the assessment of the texture of aggregate concrete (EAC) surfaces using various methods for its verification. Microtexture was assessed using the British Pendulum Tester (BPT) and Dynamic Friction Tester (DFT). Two laser profilometers were used to assess macrotexture, circular texture meter (CTM) and stationary laser profilograph (SPL), as well as the commonly known volumetric method. Measurements were carried out on left and right tracks and in between them on five test sections of expressways. Based on the analyses performed, it was found that the results obtained by the DFT were less sensitive to changes in microtexture between individual tracks compared to the results obtained by the BPT. The BPN values in the left track were lower than those in the right track. However, the difference between the DFT20 results in these spots was insignificant. Both MPD and MTD values did not show significant differences between the right and left tracks. However, some differences were observed between the MPD parameters obtained using the CTM and SPL. This resulted from the different frequency and length of the scanned surface profile. However, the differences were at an acceptable level. A very high linear correlation was obtained in the case of BPN and DFT20 values (r - 0.719), and in the case of MPD and MTD values, the correlation was almost certain (r above 0.900). Based on a comparative analysis of the models estimating mean texture depth (MTD/ETD), a significant difference was observed between models based on EAC pavement results and those based on asphalt surfaces.

Evaluation for long-term skid resistance of ultra-thin asphalt overlay based on texture characteristics

To evaluate the durability of ultra-thin asphalt overlays in terms of skid resistance accurately, the long-term degradation trends in texture characteristics need to be carried out comprehensively. In this paper, three types of ultra-thin asphalt overlays with different gradations were selected. The long-term abrasion test was conducted, with friction coefficient and texture characteristics tested in each abrasion cycle. Additionally, the stress state changes at the interface of the tire and mixtures were also obtained. Finally, the reliability of texture characteristics was validated based on the correlation between different indicators. The results show that the friction coefficient and texture characteristic show a two-stage decay pattern during the abrasion process, with a significant decrease in stress between 0.75 and 2.5 MPa at the interface of tire and mixtures. The surface mean texture depth (SMTD), root-mean-square deviation of surface (Sq), peak density of surface (Spd), and spatial fluctuations of texture (Δα) were correlated with British pendulum number (BPN) strongly, and the Sq and Δα were recommended as reliable evaluation parameters for the long-term skid resistance of ultra-thin asphalt overlays. This study provides theoretical guidance for the optimal design and field monitoring of ultra-thin asphalt overlays from the perspective of surface texture characteristics.

Exploring the relationship between pavement mean texture depth and mean profile depth: from theoretical derivation to field results

Pavement mean texture depth (MTD) and mean profile depth (MPD) are common evaluation indicators for characterizing the pavement surface. Clarifying the relationship between the two contributes to coordinate various measurement methods and evaluation criterions. The calculation methods of MTD and MPD were first introduced to trace the similarity between the indicators. Subsequently, the tortuosity area of surface texture was observed using x-ray Computer Tomography (CT). To further determine the model parameter, over 3000 sets of field data in Research Institute of Highway Ministry of Transport (RIOH) track were measured. Meanwhile, the field results of another track and existing recommended model were also used to validate the model. The results show that the shift relationship between MTD and MPD is theoretically unified into a linear model with a slope of 1 and an intercept related to the surface tortuosity. These tortuous areas, the main difference source between the two indicators, would lead to a greater equivalent texture depth under the coarser gradation or lower the compactness of the asphalt mixture. Even for the different track roads, the determined model (MTD = MPD + 0.03) has a lower mean relative estimation error, indicating good applicability. This study provides theoretical explanation and empirical references for the automated laser-based texture detection.

Mean Texture Depth Estimation of Exposed Aggregate Concrete Pavement Surface Texture Based on Photogrammetry Technique

Mean Texture Depth Estimation of Exposed Aggregate Concrete Pavement Surface Texture Based on Photogrammetry Technique

Reducing the effects of clogging and surface wear on the radiative properties of concrete blocks produced with recycled glass by incorporating titanium dioxide nanoparticles

ABSTRACT This study aimed to demonstrate how incorporating titanium dioxide nanoparticles into concrete blocks made with recycled glass mitigates the effects of clogging and surface wear on pavement radiative properties. Using a reference concrete, four variants were formulated: with colourless, green, and amber glass, and a photocatalytic concrete with colourless glass. Three accelerated processes were applied to the concrete blocks in field: clogging, wear, and clogging on a worn surface. After each cycle of clogging and/or wear, solar reflectance and thermal emittance were measured to calculate the solar reflectance index (SRI). Surface clogging decreased the SRI for all concrete types, albeit less for photocatalytic concrete with colourless glass. SRI decreased with wear until a mean texture depth (MTD) near 0.80 mm, where a stabilisation trend was noted across all concretes. Photocatalytic concrete with colourless glass consistently showed the highest SRI values relative to the MTD. Clogging on worn surfaces indicated superior radiative properties for photocatalytic concrete with colourless glass at surface contamination degree (SCD) values less than 40%. In conclusion, conventional concretes with glass had inferior radiative properties compared to reference concrete. However, photocatalytic concrete with colourless glass showed superior radiative properties, indicating that nano-TiO2 addition reduced clogging and wear effects.

Numerical determination of pavement mean texture depths at different degrees of traffic polishing-induced wear

All existing digital image-based numerical mean texture depth (MTD) calculation methods are unable to achieve a consistent level of acceptable accuracy for MTD values calculated at different degrees of pavement polishing. To solve this problem, a novel procedure is proposed to determine MTD as a function of degree of polishing represented by a texture statistic ‘directional wear index’ DWI. First, a relationship is established between DWI and the elevation H eq that defines the equivalent top surface of MTD. H eq is derived from 3-D digital image based on the concept of planation surface. Through studying laboratory specimens and field pavements polished to different degrees, a well-defined relationship between DWI and H eq was established. Knowing H eq from the H eq–DWI relationship, MTD is calculated as the mean depth of the voids between H eq and the test surface. The digital image-based procedure is able to concurrently determine MTD and the surface's degree of polishing.

Experimental investigation on wear behavior of asphalt pavement based on three-dimensional surface texture scan technology

Functional-based failure assessment and design for the service life of pavements have gained significant attention, particularly concerning wear behavior. The long-term wear characteristics of asphalt pavements are influenced by various factors and can be simulated as surface texture wear. This study employs two different indoor accelerated loading abrasion devices to investigate the effects of temperature, load, mixture type, and number of abrasion cycles on asphalt pavement wear. Surface textures of asphalt pavements were captured using a structured laser light camera, and gray & depth data were collected at the same time. Texture indicators including estimated mean texture depth (EMTD), fractal dimension (FD), and optimized structure similarity index measure (SSIM) are utilized to assess changes in surface texture. Key observations based on the small abrasion simulation equipment include the need for specific acceleration conditions in low-temperature abrasion tests, the elimination of deformation effects for high-temperature wear analysis, and a three-stage wear trend, which is more pronounced under the heavy load condition (100 kg) with no unusual fluctuations. EMTD can be used to effectively distinguish textural differences between mixtures, while SSIM shows a good trend of wear for specific mix types. Long-term lead test with MLS-11 and higher data collection frequency has verified the regularity of EMTD and SSIM, and the three stages of the asphalt mixture wear. The BPN test results are used to cross-check the failure of the skid resistance function corresponding to the wear behavior.

Measuring the Macrotexture of Pavement Surface Using an Image Processing Technique

The objective of this study was to propose an image processing-based index for measuring pavement macrotexture at the network level. This index enables macrotexture to be measured easily and inexpensively using images collected at traffic speed. The study involved collecting pavement surface images at a constant traffic speed on a test section specifically designed and constructed for this purpose, with three surface course mixes that are commonly used in India, namely, bituminous concrete, stone matrix asphalt, and gap-graded rubberized bituminous mix. Additionally, macrotexture data with regard to mean texture depth (MTD) from the sand patch test and mean profile depth (MPD) from laser sensor-based measurements were obtained at the locations where the images were captured. The surface macrotexture index (SMI), which was derived from wavelet transform-based image texture analysis, was compared with the MTD and MPD data. The results showed that the SMI is an accurate indicator of pavement surface macrotexture. In addition, the study showcased the application of an unsupervised machine learning algorithm to identify and replace outliers in the SMI data that resulted from isolated spots with dirt, pavement markings, and wet surfaces. The research also established relationships between the proposed SMI and MTD/MPD. These relationships are reliable and can be used to predict the commonly used pavement surface construction quality measure MTD and the network-level skid resistance indicator MPD.

Accident Severity Analysis On the North-South Expressway Using Binomial Logistic Regression

Accidents on Malaysian expressways must be monitored on a regular basis since severe accidents occur on expressways due to higher posted speeds than on other roadways. This study aims to analyse accident severities based on pavement conditions using binomial logistic regression. Data on accident severities and pavement conditions were gathered from the Malaysian Highway Authority (MHA) and the The Royal Malaysian Police (PDRM) respectively. Three binomial logistic regression models were constructed based on four accident severity categories of death, serious injury, minor injury and damage. The accident severity was grouped into different classifications in which Model 1, Model 2 and Model 3 were developed. To assess the model's predictive capabilities, predicted accident severity levels were distinguished with actual accident severity levels. Based on the results, Model 1 and Model 2 except Model 3 have significant pavement conditions and are viable for accident severity predictions whereby both models exhibited high prediction accuracy, has good fit and good in differentiating between two classifications. The model's classifier is better at classifying accident severity classes with more samples than categorizing accident severity classes with fewer data. The odds ratio of both Model 1 and Model 2 revealed that International Roughness Index (IRI) has greater influence in predicting accident severity compared to rut depth (RD) and mean texture depth (MTD) particularly on predicting death. This study suggested that a comparable study be conducted on other road classifications.

Texture Depth Measurement using a Micro-volume Measurement Technique for Gyratory Compactor Specimens

Introduction: Road safety plays a pivotal role in transportation infrastructure, as the quality of road surfaces is a crucial factor in ensuring safe driving conditions. One of the main indicators of road quality is the Mean texture depth (MTD), which greatly affects a vehicle's traction and skid resistance. Multiple methods are available for analyzing fabric depth, with the volumetric method, known as the sand patch test and standardized under ASTM E 965 – 96, being the traditional approach. Aim: This research aims to adapt a Sand Patch test procedure to evaluate surface texture depth on laboratory specimens with a 100 mm diameter and determine the required amount of sand, both in terms of weight and volume, to conduct this test effectively. Methods: To achieve this, the relationship between the amount of sand required and the sample size was determined. Two different types of asphalt mixtures were studied Stone Mastic Asphalt (SMA) and Asphalt Concrete (AC), with five gradation limits for each. Results: In the case of the AC10 mixture, the MTD ranged from 0.60 to 1.139 mm, while in SMA 20, the MTD ranged from 1.91 to 2.574 mm. These results are consistent with previous studies, confirming the reliability of the testing methodology, even when working with limited sample areas. Conclusion: In conclusion, the study highlights the potential simplicity and cost-effectiveness of using the Sand Patch micro-volume measurement method. When employed at the initial design stage, this method can provide valuable insights into surface texture, enhancing the design process and potentially further improving road safety.

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.

Microsurfacing: a predictive macrotexture model from mix design parameters

The main functional characteristics of pavement (friction, noise, drainability, and rolling resistance) are strictly related to the surface texture. Considering its importance, it would be appropriate to consider surface texture as a key parameter for road engineering already during the design stages. The objective of this study is confined to the setting up of a macrotexture prediction model (in terms of mean texture depth, MTD) for Microsurfacing, a preventive solution for pavement maintenance that provides a regular and even surface texture with remarkable improvement in skid resistance. Four models were developed – by using multiple linear regression (MLR) – considering a volumetric approach based on mix design-related factors (aggregate gradation, bitumen content, and mineral filler rates). In an attempt to confirm the good generalization of the models and their basic assumptions, a K-fold cross-validation method was used. Several statistical metrics aim at evaluating the goodness of fit and the errors in prediction. The results obtained reveal a strong correlation and low errors between the observed and estimated values, confirming the adequacy of the models (R2 of 0.76–0.90). A more comprehensive dataset and experimental data can further improve the performance of the model. Results can benefit both researchers and practitioners.

Reconstruction and evolution of 3D model on asphalt pavement surface texture using digital image processing technology and accelerated pavement testing

Asphalt pavement surface texture is the main factor affecting pavement function. Reconstruction of the asphalt pavement surface texture is needed to accurately reveal its evolutionary characteristics for pavement performance and quality evaluation. To reconstruct an optimised 3D model of the asphalt pavement surface texture and to study its evolutionary properties, digital image processing technique and accelerated pavement testing system were used. First, the asphalt pavement surface texture 3D model optimised by three camera parameters and their thresholds. Next, the accelerated pavement testing system simulated traffic loadings on dense-gradation asphalt mixtures to investigate the pavement surface texture evolution properties. Finally, predictive models are developed for the asphalt pavement surface texture evolution. Results show that the optimised pavement texture 3D model resembles actual pavement structure. The surface texture evolutionary characteristics of asphalt pavement can be divided into three periods and six stages. The evolution model can accurately characterise the evolution of the surface texture of asphalt pavement. Abbreviations: MLS11: Accelerated pavement testing system; HP : Mean pixel difference; Df : Fractal Dimension; MTD: Mean Texture Depth; BPN: British Pendulum Number

Analysis of Aggregate Types with Micro-texture and Macro-texture Characteristics of Asphalt Mixture in Indonesia

Background: Micro-texture and macro-texture are two essential parameters that functionally evaluate friction on asphalt pavement surface. While micro-texture indicates the irregularity of aggregate using British Pendulum Tester (BPN), macro-texture shows the larger irregularity of asphalt mix surface using Mean Texture Depth (MTD). Both micro-texture and macro-texture contribute to increased skid resistance value, which is needed for road to meet the safety qualification. Aim: This study aims to investigate the effect of local aggregate types and aggregate proportion (%) on asphalt mix on pavement textures (micro and macro-texture). Methods: Laboratory experiments were conducted on four types of local aggregates (A, B, C, and D), which were carried from West Java, Indonesia, and aggregate has a different characteristic to each other. In addition, three asphalt mix types (SMA-fine, AC-WC, and HRS-WC), each containing varying proportion (%) of aggregates, were investigated to analyze change in texture on asphalt mix surface. Statistical analysis with two-way ANOVA was carried out to investigate the significance of aggregate type and asphalt mix type on pavement surface. Results: The results showed that each aggregate exhibited different characteristics chemically and mechanically. Four types of local aggregate possessed distinctive characteristics for the asphalt mix performance (i.e., OAC and density) and pavement textures. Statistically, it was clearly found that both aggregate type and asphalt mix type were strongly related to pavement texture. Conclusion: Therefore, the aggregate type and asphalt mix type with varying aggregate proportions (%), such as coarse aggregate (CA), aggregate-fine (FA), and filler (FF), affect the micro-texture and macro-texture.

Identifying Texture and Friction of Asphalt Pavement Surface with Optimized Close-Range Photogrammetry Method

Abstract An adequate pavement texture that provides sufficient brake friction is critical for drive safety. Thus, it is essential to quantify the pavement texture and friction efficiently and accurately. So far, the close-range photogrammetry (CRP) method has been manifested as a promising one. Nonetheless, related measurement parameters have not been well optimized toward its extensive promotion. For this reason, varying measurement parameters of CRP method were thoroughly discussed and optimized in this study. Additionally, the optimized CRP method was further validated using the sand patch test and X-ray computed tomography scan method. Based on this, the estimation of friction coefficients for asphalt concrete (AC-13), stone mastic asphalt (SMA-13), and open graded friction course (OGFC-13) were carried out. The results indicate the following: (1) the optimized CRP method was able to adequately feature the pavement macrotexture; (2) the mean texture depth derived from the CRP method was equivalent to the texture depth measured from sand patch method; in addition, (3) the OGFC asphalt mixture was superior to the SMA and AC asphalt mixtures in terms of the friction coefficients. The optimized CRP method could bring bright prospects for future measurements of pavement texture.

Mechanistic evaluation of segregation in HMA mixtures

Segregation in hot mix asphalt (HMA) mixtures is defined as the separation of the coarse aggregate particles in the mixtures from the rest of the mass. Segregation can be a result of aggregate stockpiling and handling, production, storage, truck loading practices, construction practices, and equipment adjustments. Segregation is usually evaluated visually, which is considered as a subjective method with no definite limits and depends on the evaluator’s opinion.This study uses two mechanistic surface texture indicators, i.e., mean texture depth (MTD) that is measured using sand patch method and mean profile depth (MPD) using laser texture profilometer to evaluate if a road section is segregated or not.The sand patch method is standardized in ASTM E965 -15 (2019) “for Measuring Pavement Macrotexture Depth Using Volumetric Technique”. MPD is covered by the international standards ASTM E1845-15 “Standard Practice for Calculating Pavement Macrotexture Mean Profile Depth”.Using both measured MTD values at grid point crossings, and average MPD values at 25 m intervals in the wheel paths, in addition to the use of statistical analysis of the obtained data, assuming that the obtained data are normally distributed and finding the 95% probability limits of the MTD and MPD values, it is possible to prove the closeness of the obtained texture depth indicator data, homogeneity of the road section, and that the segregation is only present at very limited localized locations.