Mean Texture Depth Research Articles

Influence of pavement condition towards accident number on Malaysian highway

Accidents in Malaysia are alarming and have been in the rise for the past decades. Increasing numbers of road users, condition of road surface, expanded road network and weather conditions are being carefully looked into determining the contributors towards fatality. Human factors has been widely known and established as one of the main contributor to road accidents. Therefore, this study had focused more on to pavement conditions, where its relation with accidents needs to be further explored. The purpose of this study is to determine the influence of pavement condition towards accident number on Malaysian Highway. Pavement condition data which consist of Mean Texture Depth (MTD), Rutting Depth (RD) and also International Roughness Index (IRI) were measured along 265 km southern stretch of Malaysia highway, by using Multi Laser Profiler (MLP). Accident data were also collected from the accident report prepared by Southern region of Malaysian Highway Authority. Based on the results, it was found that more than 70% of the accidents happened at a good condition of road which having high MTD, low RD and minimum IRI. MTD was found as the most significance parameters that relates to accident. Other factors such as human negligence and environments (weather and lighting), might be the major cause that contribute to the accidents that happened along Malaysian highway.

Effects of asphalt pavement texture on hydroplaning threshold speed

Hydroplaning speed can be affected by pavement texture depth, thickness of water film, tire pressure and tread depth. In this study, to understand the influence of pavement texture on the hydroplaning speed, a new lab-scale apparatus has been designed and manufactured. The lack of proportion between linear movement of vehicle shaft and the wheel rotation was found to be a good index to determine hydroplaning threshold. A 5% drop in the ratio of wheel-to-axle rotation has been assumed as an index to determine hydroplaning threshold. Based on the measures, a simplified model was developed that is able to predict the hydroplaning speed depending on pavement’s texture characteristics. The results indicated that a 77% increase in mean texture depth cause 9% increase in hydroplaning threshold speed.

Acquisition method of asphalt pavement texture information based on the CPR Technology

In order to obtain the asphalt pavement texture information in real time and accurately monitor the anti-skid performance of the road pavement, an automatic close range photogrammetry system (ACPR system) was proposed and built based on the circle arranged three cameras close range photogrammetry (CPR) technology to obtain the asphalt pavement surface texture. Automatic image acquisition and 3D reconstruction were achieved by the ACPR system. Sand patch method and laser scanning method (ZGScan) were used to collect the on-site comparison test of the asphalt pavement texture. Mean texture depth (MTD) and root mean square roughness (RSMR) were chosen as the statistical indicators of road surface texture. The results show that the texture data obtained by ACPR system has relatively high accuracy and efficiency, and the recognition accuracy is close to 0.02mm. The ACPR system improves the efficiency and accuracy of traditional close range photogrammetry and provides real-time and effective road surface anti-skid information for subsequent safety braking of autonomous vehicle.

Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques

The energy or fuel consumption of the millions of vehicles that daily operate in road pavements has a significant economic and environmental impact on the use phase of road infrastructures regarding their life cycle analysis. Therefore, new solutions should be studied to reduce the vehicles energy consumption, namely due to the tire-pavement interaction, and contribute towards the sustainable development. This study aims at estimating the energy consumption due to the rolling resistance of tires moving over pavements with distinct surface characteristics. Thus, different types of asphalt mixtures were used in the surface course to determine the main parameters influencing the energy consumption. A laboratory scale prototype was developed explicitly for this evaluation. Data mining techniques were used to analyze the experimental results due to the complex correlation between the data collected during the tests, providing meaningful results. In particular, the artificial neural network allowed to obtain models with excellent capacity to estimate energy consumption. A sensitive analysis was carried out with a five input parameter model, which showed that the main parameters controlling the energy consumption are the vehicle speed and the mean texture depth.

Evaluation of pavement surface texture at the network level

ABSTRACTIn this study, variations in surface texture depth within a given roadway project and across different projects were studied. Network-level surface texture data were collected on the highway network in using a 3D laser system (LCMS). Differences in mean texture depth (MTD) were estimated for five surface types (Portland concrete cement, PCC; Superpave mixes, SM-9.5A & SR-12.5A; Chip seal; Ultra-thin bonded asphalt surface, UBAS) considering the random effects of project location, surface age and annual average daily traffic. Variations in surface texture were also investigated at the horizontal curves. In addition, the relationship between the texture depth and the skid number obtained by the ASTM skid trailer were investigated. Results show that there are significant differences in average texture depth within a project and across projects. Highest MTDs were obtained for UBAS and lowest for PCC. At the network level, skid number did not correlate well with the MTDs, irrespective of surface type. However, project-level correlation is quite acceptable.

Analytical tool to shorten polishing time based on mean texture depth (MTD) of flexible pavements

The reproduction of asphalt pavement surface texture deterioration in the laboratory is usually lengthy. The objective of this work is to find an analytical tool to predict the steady-state mean texture depth (MTD) early in the experiment in order to save time. Data were obtained from laboratory testing carried out on eight job mix formulas of bituminous mixtures. Measurements were collected using the volumetric sand patch method for each hour of polishing. The basic parameter used for this study is the MTD, which is a measure of surface macrotexture. Based on nonlinear regression, an equation was developed to calculate the steady-state macrotexture. The main characteristic of the developed equation allows for calculating the steady-state macrotexture and the onset of steady state. This enables the experimenter to determine when steady state is reached at the desired tolerance during the test, thus reducing the testing time. It was concluded that testing time can be reduced significantly (around 50%-time savings), which may be received positively by highway materials’ agencies worldwide.

Convolutional Neural Network for Asphalt Pavement Surface Texture Analysis

AbstractSeveral data processing techniques (DPTs) have been implemented for evaluating pavement surface texture to partially replace onsite inspections by humans. However, the extensively varying real‐world situations have resulted in challenges in the widespread adoption of DPTs. To overcome these challenges, we propose the use of a convolutional neural network (CNN) to calculate the mean texture depth (MTD) without computing the surface texture feature statistics. Because a CNN is capable of automatically learning data features, the proposed method does not require the conjugation of DPTs for extracting features. The proposed CNN was trained and tested using 8,000 and 1,000 3D scan data samples, respectively, and achieved an average error of 0.0024 cm. The stability of the CNN was analyzed based on various test results. Comparative studies were conducted to verify the superiority of the CNN over conventional MTD algorithms. The results demonstrated that the CNN‐based method is significantly more stable in various real‐world situations. Additionally, the CNN‐based method achieved higher accuracy of automatic feature extraction than traditional MTD methods. Finally, the CNN‐based method was applied to evaluate the surface texture statistics of four highways in Shanxi, China, which were different from the training and testing samples; the results establish the transferability of this method to different highways.

Evaluating asphalt pavement surface texture using 3D digital imaging

ABSTRACTThe traditional asphalt mixture surface texture measurement methods cannot provide the distributions of the texture, which is necessary for studying the influence mechanism of texture on tyre/pavement interaction performance. A non-destructive 3-dimensional image-based texture analysis method (3D-ITAM) is developed based on the 3D surface reshaping and spectral analysis techniques. Mixture 3D surface is reshaped based on the photometric stereo method using the acquired tricolour digital images. Spectral indicators representing mixture surface texture distribution properties are calculated from 3D-ITAM software. Additionally, the traditional texture indicator (i.e. mean texture depth) is also calculated. The 3D-ITAM results are confirmed with both macro and micro-texture indicator values from the sand patch method (SPM) and hand-push friction tester (HFT), which are two widely used in practice though they cannot capture the distribution property of surf ace texture. It was demonstrated that the 3D-ITAM results correlate well with SPM and HFT and can be considered as an effective method to characterise the mixture surface texture.

Optimization of the photometric stereo method for measuring pavement texture properties

The texture of the pavement surface, in addition to the traffic safety, plays a major role in the driving comfort and performance of the vehicle. A dozen of methods have been introduced for determining the pavement texture and each of them has advantages and disadvantages. The usage of the image processing as one of the newest methods is expanding because of its significant advantages. In this study, by constructing a device for taking a picture with different light angles from the pavement surface and using a four lights photometric stereo method, the pavements with different texture have been investigated. Finally, for pavements with different texture type, the optimal angles of different light have been introduced for use in photometric stereo technique. The results show that the adverse relationship between the optimum zenith angle and the depth of texture of the pavement surface, such that by increasing the mean texture depth, using the less zenith angle will give better results.

Development and Validation of a Model to Predict Interface Bonding Between Pavement Layers

During the planning of overlay construction activities, a contractor and/or state agency may be interested in selecting a tack coat material and application rate that would provide adequate interface bonding at the site. The objective of this study was to develop an interface shear strength (ISS) predictive model to estimate interface bonding between the hot-mix asphalt (HMA) overlay and the underlying pavement layer. To achieve this objective, 10 field rehabilitation projects were selected in six states that included 33 in-service test sections representing different traffic and climatic conditions. The overlay construction used different types of tack coat at various residual application rates. Field cores were obtained to measure ISS using a direct shear test device, referred to as the Louisiana Interlayer Shear Strength Tester (LISST). Influencing factors to be included in the model were pavement surface type and mean texture depth, residual application rate, and characteristics of tack coat residues. The experimental data were complemented with test results from a recently completed study for model development and validation. Two types of statistical regression models (linear and nonlinear) were evaluated. Results of the analysis showed that the predicted ISS obtained from the proposed models were in good agreement with the measurements. Further, the developed nonlinear model showed better prediction accuracy than the linear model. The nonlinear model can be used effectively for optimum selections of tack coats given the project conditions. The results presented herein were part of NCHRP Project 9-40A, Field Implementation of the Louisiana Interface Shear Strength Test.

Influence of temperature on polishing behaviour of asphalt road surfaces

The skid resistance of road surfaces is crucial for traffic safety and varies due to the polishing effect of the traffic loads. Numerous prediction models for the skid resistance of asphalt pavements have been developed based on three standard polishing methods in accordance to regulations; Polished Stone Value (PSV), Micro-Deval (MD) and Wehner/Schulze machine (W/S). However, the aforementioned tests can only be carried out for given conditions such as under constant temperature. This does not fully represent real conditions on road surfaces, because asphalt exhibits a strongly temperature-dependent performance due to the viscosity of bitumen. In this paper, the polishing effect due to traffic and weathering was simulated with a self-developed device, the Harbin Accelerated Polishing Machine (HAPM). It is equipped with real tires and allows for the precise control of the testing temperature. The British pendulum number (BPN) and mean texture depth (MTD) were determined to describe skid resistance development during polishing process. Prediction models of skid resistance are created and the model parameters are calculated by means of a regression analysis. The influences of the temperature during polishing on the long-term evolution of skid resistance are studied.

Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

The emerging 1mm resolution 3D data collection technology is capable of covering the entire pavement surface, and provides more data sets than traditional line-of-sight data collection systems. As a result, quantifying the impact of sample size including sample width and sample length on the calculation of pavement texture indicators is becoming possible. In this study, 1mm 3D texture data are collected and processed at seven test sites using the PaveVision3D Ultra system. Analysis of Variance (ANOVA) test and linear regression models are developed to investigate various sample length and width on the calculation of three widely used texture indicators: Mean Profile Depth (MPD), Mean Texture Depth (MTD) and Power Spectra Density (PSD). Since the current ASTM standards and other procedures cannot be directly applied to 3D surface for production due to a lack of definitions, the results from this research are beneficial in the process to standardize texture indicators’ computations with 1mm 3D surface data of pavements.

Modelling and simulation of pavement drainage

ABSTRACTThe development of a numerical model to describe pavement surface runoff by a system of non-linear hyperbolic partial differential equations enables the analysis and prediction of safety-relevant road design and construction. Aquaplaning risks could be minimised as well as damages to the road surface geometry prevented. The hydromechanical approach presented in this article by a two-dimensional Pavement Surface Runoff Model (PSRM) based on the depth-averaged Shallow Water Equations – in contrast to empirical models – represents a general simulation. PSRM includes features like the treatment of an irregular topography or the possibility to treat different surface textures. The model enables the calculation of water depths. Discontinuous solutions to the underlying equations are numerically gained by applying Riemann solvers with Godunov methods to compute approximate intercell fluxes over the discretised problem using Finite Volume Methods. The Harten–Lax–Van Leer (HLL) Riemann solver was chosen to calculate the intercell fluxes. Simulation examples show the wide range of possibilities to describe real pavement surface runoff problems, for example junctions and superelevation transitions. Flow resistance is modelled with the Darcy–Weisbach equation by considering the pavements mean texture depth (MTD) as a variable parameter. In order to take into account the pavement surface drainage capabilities due to infiltration processes in porous surface layers, a coupled three-dimensional numerical formulation enlarges the macroscopic PSR Model. The saturation of the porous surface geometry and the infiltration velocities by Darcy velocities under consideration of interface conditions on the interface between free flow on the road surface and drainage into the porous structure are implemented within an emerging hydromechanical numerical model.

Three-dimensional survey method of pavement texture using photographic equipment

In this paper, an image-based technique for the assessment of a 3D model of pavement texture is presented. Five common cameras were used to collect pictures of the pavement surface. An innovative procedure was developed, based on volumetric calculation, to calculate the digital Mean Texture Depth (MTD) starting from the Digital Surface Model (DSM) generated by the photos.To validate the procedure, 20 different pavement surfaces were acquired for a total of 100 DSMs. For each model the digital MTD was calculated and compared with the measured MTD performed with the sand patch method, in the same pavement. The coefficients of determination were found for each camera. The results highlighted the high accuracy level of the analysis, with a coefficient of determination from 0.99 to 0.94 in relation to the features of each camera used for the acquisition.In addition, others texture parameters extracted by profiles were calculated and analyzed. At last, a volumetric study was conducted to investigate the pavement behavior in case of rainfall.

RETRACTED: Effect of segregation on rutting resistance of asphalt pavement

Mix segregation is generally described as localized non-uniform zones of mix that do not conform to the original Job Mix Formula in gradation or asphalt content. Segregation may cause field performance issue such as rutting but no good trend between segregation and field/laboratory rut depth is observed. In this paper, a field road with segregation was monitored and those reported parameters used to detect segregation were collected. The segregated areas and the control non-segregated areas were identified by visual assessment. Rut depth were measured within one year after the road was opened to traffic. Rut depth from segregated areas and non-segregated areas are compared to evaluate the effect of segregation on rutting performance. The possibility of using different parameters such as pavement texture and construction temperature to detect segregation is also analyzed. Acknowledging that the rut depth may be affected by multiple parameters, a statistical Partial Least Squares (PLS) regression method is applied to select predictor variables that have the most significant effect on rut depth, and to develop rut depth prediction model. Results indicate that rut depth from segregated areas (visual) are statistically higher than rut depth from the non-segregated areas (visual). It is also found that pavement texture and construction temperature are the potential factors to detect segregation since they are correlated well with both visual assessment and rut depth. The developed rut depth prediction model shows good predictability and is well validated by considering segregation (mean texture depth), climatic effect (aging days), material property (HMA layer moduli), and pavement structure (entire HMA thickness).

Effective parameters on interface failure in a geocomposite reinforced multilayered asphalt system

Application of geosynthetics is known as an effective way to reduce and delay reflective cracks. Although these materials can improve pavement resistance against reflective cracking, they can reduce shear bonding between the overlay and the old pavement. Lack of this resistance can cause some defects, especially in zones in which cars accelerate, decelerate or turn. There are many parameters affecting the quality of shear bonding between layers. In this research, a double shear bonding test denoted as Amirkabir University of Technology Shear Lab Tester was developed and was used for conducting dynamic shear loading experiments on geocomposite reinforced specimens. In order to simulate the weight of passing vehicles, a normal loading system was designed and added to the double shear test device. This normal loading system applies constant stiffness on the specimen, thus simulating real conditions with high fidelity. Seven factors, namely temperature, tack-coat application rate, tack-coat penetration value, geocomposite mesh size, mean texture depth of old pavement surface, loading amount and loading frequency were selected, based on the literature review. The experimental design was Taguchi L16 (2*3 4*4) orthogonal. Graphs of values of the shear stiffness modulus versus number of loading cycles have shown that increasing the number of loading cycles applied on the specimen results in a decline in the interface shear stiffness that occurs in three distinct stages. Furthermore, ANOVA analysis was performed, focusing on interlayer shear stiffness, interlayer failure levels and number of loading cycles required to reach these failure levels. In addition, within the studied range of factors, among the examined parameters, the temperature and tack-coat application rate were found to be the most influential on the initial interlayer shear stiffness. Moreover, the application of normal load with normal stiffness was shown to noticeably augment the geocomposite mesh size effectiveness. Temperature, followed by geocomposite grid size, emerged as the most important parameter for determining the number of loading cycles required to reach 10%, 30%, 50% and 70% of the initial shear stiffness. On the other hand, the number of loading cycles required for shear stiffness reduction was primarily affected by the tack-coat application rate.

Development of a Mean Profile Depth to Mean Texture Depth Shift Factor for Asphalt Pavements

The initial condition of a newly constructed pavement structure, also known as the zero point, provides valuable information and can be included in pavement management systems. The zero point data for the initial macrotexture level can be gathered both quickly and efficiently by measuring the mean profile depth (MPD) of the pavement surface with a laser profiler immediately after the end of the construction phase and before the road pavement is opened to traffic. However, in many countries the specifications for macrotexture levels during this phase are gathered through the mean texture depth (MTD) index derived from the sand patch method, which is both location specific and time-consuming. Thus, the study presented in this paper investigated the applicability of the ASTM E1845 equation to the field data collected from a newly constructed highway and then developed a methodology to form a shift factor that could allow the direct estimation of MTD data with MPD data. The investigation findings showed that the applicability of the ASTM equation was not validated for the particular highway, a conclusion that was drawn in other relevant investigations. On the contrary, the developed factor to shift MPD data to MTD appears to be effective for use by pavement engineers with validation results supporting the conclusion. This paper presents details highlighting the need to consider the individual characteristics of each pavement surface, as improper use of the ASTM equation could lead to erroneous results.

Macrotexture characterisation of laboratory-compacted hot-mix asphalt specimens using a new asphalt polishing machine

Highway agencies continuously strive to maintain adequate surface friction (or skid resistance) at the interface between vehicle tyres and pavement surfaces to enhance the available friction during all phases of a pavement life time. Conventionally this is attained by monitoring and ultimately overlaying the pavement surface deficiencies. However, this practice is an inactive approach in solving the problem. A more proactive approach would be to carry out the necessary hot-mix asphalt testing in a laboratory setting before construction or during its initial mix design stage. Towards this objective the polishing behaviour of laboratory-prepared specimens has been investigated in terms of friction and macrotexture values. The potential relationship between friction and texture measurements has also been considered. The new asphalt polisher showed a good degree of repeatability. The macrotexture was found to be a significant factor in controlling the frictional properties of asphalt surfaces which was shown by the relatively high correlation between the mean texture depth (MTD) and the British pendulum number (BPN) values. Good correlation was also found when correlating ΔBPN and ΔMTD for each job mix formula and each group based on their polishing susceptibility. Test results of this study that are based on bituminous surfaces made from aggregates produced in Ohio could enrich the Ohio Department of Transportation material specification database for future usage.

Evaluating the effect of different asphalt mixtures on hydroplaning using a new lab-scale apparatus

ABSTRACTSkid resistance of asphalt pavements is one of the most important road criteria for users' safety. In a rainy condition the skid resistance is largely compromised and when the road surface is flooded, vehicles can experience hydroplaning at a certain critical speed. Tires under hydroplaning conditions become separated from the pavement surface and the ability of tires to increase braking force or cornering traction for stopping or controlling vehicle motion is almost entirely lost. In this study, a new lab-scale apparatus was designed and manufactured, which can stimulate hydroplaning and evaluate the impact of different asphalt mixtures on this phenomenon. To evaluate various mixes, three types of asphalt mixtures with different grading are made and mean texture depth and British pendulum number are measured by six repetitions. By using this apparatus in dry and wet conditions of pavement surface, five different frequencies are tested in terms of hydroplaning. The results show that there is 33% increase in hydroplaning occurrence speed by increasing mean texture depth by 0.5 mm.

Field investigation of skid resistance degradation of asphalt pavement during early service

This paper documents a field investigation into the skid resistance degradation of asphalt pavement during early service. Field tests were conducted 7 times during more than 2 years. There are 2 highway sections included in the field tests, which cover 4 asphalt surface types, i.e., dense asphalt concrete (DAC), rubber asphalt concrete (RAC), stone matrix asphalt (SMA), and ultra-thin wearing course (UTWC). Macrotexture and friction data were collected using the sand patch method and the dynamic friction tester respectively. The degradation of the mean texture depth (MTD) and the friction coefficient at slip speed of 60km/h (DFT60) were analyzed. The international friction index (IFI) was also calculated using the friction coefficient at slip speed of 20km/h (DFT20) with MTD to evaluate the skid resistance degradation. The UTWC has relatively good skid resistance even after 7.4×106 standard vehicle passes. The SMA has very stable friction performance which maintains almost the same friction level after 4.61×106 standard vehicle passes. The DAC and RAC have relatively poor friction performance while the RAC has better macrotexture. The changing trends of skid resistance with traffic wear can be fitted by a logarithmic model for all surface types. The SMA and UTWC have relatively clear relationship between DFT20 and MTD, while the RAC and the DAC show more complex.