Pavement Surface 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.

Pavement surface condition assessment: a-state-of-the-art research review and future perspective

Pavement surface condition assessment: a-state-of-the-art research review and future perspective

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.

A Methodological Approach to the Study of Retroreflective Pavements

Climate change, principally driven by human activities, has led to an increase in global temperature, which is predicted to continue rising in the coming years. This temperature increase is even more pronounced in urban areas due to the heat island effect. This phenomenon is highly influenced by the presence of paved streets made with bituminous mixtures, which are characterised by their high solar radiation absorption capacity. Bituminous mixtures retain and re-emit a large amount of heat that intensifies the urban heat island effect. The novelty of this work is to measure retroreflective properties of bituminous mixtures that present a highly textured surface. In this context, the aim of this study is to evaluate the retroreflectance of different bituminous mixtures for use as pavement surfaces, focusing on the influence of colour and different types of aggregates. For this, total and directional reflectance measurements were conducted to determine the retroreflectance of these mixtures, with the purpose of mitigating the heat island effect in urban environments without affecting users through reflected solar radiation. The results show the retroreflective capacity of the designed mixtures within the visible spectrum, especially those manufactured with light-coloured aggregates and synthetic binders pigmented with titanium dioxide. Thus, the retroreflectance of the lighter mixtures range from 37.9% at a 0° entrance angle to 68.9% at 60°, while the black mixtures exhibit values between 5.1% and 8.4%.

Prognosis of reflective pavement cracking development with Bayesian updating and surrogate models

ABSTRACT The main objective of this study is to establish an integrated prognostic method for predicting the development of thermal-induced reflective cracking in asphalt overlay on concrete pavement. The integrated prognosis process for crack growth is developed on the modified Paris’ law and includes three steps: (1) calculation of J-integral; (2) Bayesian updating of crack growth parameters and (3) prediction of remaining life. Finite element modelling (FEM) was conducted to calculate J-integrals at different crack depths and cold weather conditions. A surrogate model was built based on the original Kriging method and it shows high accuracy in predicting J-integrals from maximum air temperature, temperature drop and crack depth. The Bayesian updating process starts from the laboratory-measured distribution of fracture model parameters (A and n) in Paris’ Law and converges to the true values to match the measured crack depths at different loading cycles from field inspection. On the other hand, the consideration of material damage in asphalt overlay avoids over-estimation of remaining loading cycles for reflective cracking to reach the pavement surface. Further study is recommended to accurately assess in-situ pavement modulus and detect crack depth over concrete joints for implementation of the proposed prognostics method.

Novel fast full-wavefield modeling of air-coupled surface waves and its implications for non-contact pavement testing

We present a novel approach for deriving and modeling air-coupled surface waves with applications in non-contact non-destructive testing (NDT). It is based on the fast Fourier-Bessel series system in conjunction with the unconditionally stable dual-variable and position matrix method. Parametric studies, including sensitivity analysis, are conducted to assess the feasibility of using non-contact air-coupled measurements for pavement testing, focusing on Green's functions, time-domain waveforms, and experimental frequency-velocity spectra (FVS, i.e., the estimated Green's functions from acquired truncated wavefield). The predicted experimental FVS presented in this study are synthetic dispersion images, which are distinguished from the measured experimental FVS (i.e., measured dispersion images from multichannel analysis of surface wave (MASW) test). With the derived complete solution of air-coupled dynamic responses, we find that: (1) Striking similarities between the theoretical Green's functions of vertical displacement (on the pavement surface) and pressure (in the air), as well as in their corresponding experimental FVS. (2) The proposed accurate and efficient full-wave modeling of air-coupled surface waves avoids the need for good contact between geophones/accelerometers and pavement surface. This facilitates direct inversion of shear wave velocity profiles by fitting the predicted experimental FVS to the measured one. (3) Sensitivity analysis demonstrates no significant loss of information in the pressure measured in the air, supporting the feasibility of using non-contact measurement for non-destructive testing. These results suggest that non-contact air-coupled measurements hold great promise as a viable alternative to contact measurements in non-destructive testing.

A dynamic boundary condition finite difference model for predicting pavement profile temperatures: Development and validation

The appearance of ground frost is of vital importance in construction and maintenance of roads in cold climates. Frost often causes ground heave and subsequent road damage, which must be taken into account in designing the road structure. Frost depth, pavement temperature, and freezing/thawing cycles are also important for estimating the frequency of road maintenance and treatment. Various analytical, numerical, and empirical models have been developed to estimate the surface temperature of the pavement and to model the heat flow in the underlying layers. The pavement surface experiences a variety of intricate nonlinear heat transfer mechanisms during winter, making it challenging to accurately model the surface boundary. Dynamic variation of parameters such as cloud cover and traffic density during the modeling period introduces additional complexity. To address this challenge, we have established an experimental setup in Luleå, Sweden, to measure pavement profile temperatures during the winter season. Additionally, we have developed a Finite Difference Model that utilizes local weather data including dynamic cloud cover, and which also takes traffic into account. The experimental and simulation findings demonstrate how the impact of surface temperature fluctuations diminishes and, more or less, vanishes for depths more than 55 [cm] below the pavement surface. The Finite Difference Model presented in this study exhibits the ability to forecast the pavement profile temperatures, including the surface temperature based on weather conditions, with acceptable precision for at least 3 days. As a consequence, a reasonable assessment of pavement layer conditions appears feasible based on local weather conditions, and the model can serve as a useful tool for planning road maintenance and construction in cold regions.

LC3 cementitious binder incorporating microencapsulated phase change materials for self-defrosting traffic surfaces

Exposed surfaces of bridge decks, viaducts and pavements incur ice formation and accumulation of snow in cold seasons, which threatens traffic safety. Mitigating this problem using traditional methods such as deicing salts have caused ecosystem damage and inflicted substantial reinforcement corrosion and surface scaling to bridge decks and pavements, thus compromising their service life and causing colossal economic loss. This paper presents an alternative solution to overcome this problem through the development of a sustainable LC3 cementitious material incorporating micro-encapsulated phase change material (MEPCM) with low phase transition temperature to delay the surface temperature drop and mitigate snow accumulation and ice formation. MEPCM was incorporated in the cementitious matrix at 0 %, 10 % and 20 % by binder mass. Paste and mortar mixtures were prepared to investigate the microstructural, mechanical, physical, and thermal properties. Test results showed that incorporating MEPCM in the LC3 matrix achieved adequate compressive strength. Thermal simulations and visual observations conducted on mortar samples in the laboratory and outdoor exposure showed that MEPCM can effectively regulate the surface temperature of the LC3 matrix and mitigate temperature drops as well as snow accumulation. Analysis of temperature data in the 2023–2024 winter season of Hamilton, Ontario, Canada indicated that the MEPCM incorporated in mortar samples could effectively regulate 40 % of the total days considered and mitigate surface ice formation and snow accumulation, which provides both economic and environmental benefits.

Mechanical Response of Hot-Mixed Epoxy Asphalt Concrete Steel Deck Pavement Under Thermal and Load

In recent years, fatigue cracking in orthotropic steel bridge deck pavements has become a significant concern, so the investigation of the mechanical response of the pavement layer has become a central focus in pavement structure design. This experiment subjected a full-scale specimen to a constant amplitude dynamic load of 60 kN to 300 kN over 2 million cycles. Throughout the testing, a circulating water bath elevated the temperature of the pavement layer from 15 °C to 50 °C. Key locations were monitored for strain and deflection data, facilitating an investigation into the mechanical response of the epoxy asphalt pavement system under the effects of temperature and load. The results indicate that the maximum transverse strain at the bottom of the steel deck occurs at the U-rib weld aligned with the load center, reaching 190% of the initial loading strain. Meanwhile, the maximum transverse strain on the pavement surface is observed at the U-rib weld adjacent to the loaded area, measuring 167% of the initial strain. The maximum longitudinal strain is lower than the maximum transverse strain. In the load zone, the longitudinal strain between the U-ribs exceeds that at the U-rib weld. Both transverse strain and relative deflection increase as the load intensifies. The relationship between transverse strain and applied load is characterized by an exponential function, while deflection exhibits a cubic relationship with the applied load. Elevated temperatures also contribute to increased transverse strains at both the bottom of the steel deck and the pavement surface, following an exponential trend. Relative deflection is primarily influenced by the applied load and remains relatively unaffected by temperature variations. When accounting for the coupling of load and temperature, the maximum transverse strains at both the bottom of the steel deck and the pavement surface can be modeled as an exponential function of the independent variables: load and temperature.

Coal-derived electrically conductive asphalt pavements for snow/ice melting: From laboratory to field

Timely removal of ice and snow from roads is critical to safe, fast, and uninterrupted transportation networks in cold regions. Constructing electrically conductive asphalt pavements to melt the ice and snow on the roads through resistive heating is an emerging alternative technology to traditional snow/ice removal approaches such as utilizing snowplow machines and deicing chemicals. Carbon-based fibers and fillers including carbon fiber and graphite have been widely reported to make electrically conductive hot mix asphalt mixtures for pavement snow/ice-melting applications. This study aimed to develop and demonstrate a novel type of electrically conductive asphalt pavements for snow/ice-melting, which utilizes electrically conductive cold mix asphalt (CMA) mixtures incorporating coal-derived carbon-based coke aggregate as resistive heating elements. Both laboratory experiments and field tests were conducted to investigate the electrical, mechanical, and thermal properties of such electrically conductive asphalt mixtures and pavements. The laboratory experiment results indicated that the electrically conductive CMA mixtures incorporating coke aggregate had sufficiently high electrical conductivity and satisfactory mechanical performance and the pavement prototype slab utilizing a thin layer of such CMA mixtures could successfully raise the pavement surface temperatures to 8.3–11.7 °C from a low temperature of −5 °C with an input power density of 473 W/m2. The field test results showed that the full-scale coal-derived electrically conductive asphalt pavements were able to increase the pavement surface temperatures when electricity was applied, but the magnitude of temperature increase was highly dependent on the power density. Therefore, it is promising to use coke aggregate to construct coal-derived electrically conductive asphalt pavements for snow/ice melting.

Asphalt pavement raveling recognition based on deep learning algorithms

Abstract Pavement raveling can lead to the development of issues such as potholes. Monitoring and providing real-time early warnings for road surface developments is crucial for timely maintenance planning and effectively controlling the occurrence and progression of surface issues. To achieve intelligent identification of raveling distress on asphalt pavement surfaces, we utilized a portable laser-based pavement surface scanning device to collect raveling images, followed by histogram equalization to eliminate noise caused by environmental factors. Additionally, data enhancement strategies such as rotation and mirroring were used to enrich training data and expand the sample size. A Convolutional Neural Network (CNN) was employed to perform multi-layer convolution and pooling operations on the images to quickly extract features of distress. Finally, five pre-trained transfer learning models—ResNet-18, DenseNet, VGG16, Inception, and EfficientNet—were introduced to identify the asphalt surface disease data and the normal surface data. The results showed that the accuracy and loss of the training sets of several models exhibited a positive trend, with the EfficientNet model demonstrating stable and superior performance under the same training conditions. The precision matrix, confusion matrix, precision, recall, and F1 score were used as evaluation indexes to assess the effectiveness of the models for identifying pavement raveling. The EfficientNet and Inception models had the best accuracy and comprehensive performance, with accuracy rates of 0.96 and 0.95, respectively. The precision, recall, and F1 score are 0.95, 0.96, and 0.96 for the EfficientNet model respectively. A total of 146 images correctly predicted raveling disease and only 4 normal images were incorrectly predicted as diseased. For example, the EfficientNet model’s identification accuracy for pockmark disease on the PC platform was 0.963, verifying the model’s feasibility in practical applications. The principal contribution of this paper lies in the efficient and precise identification of asphalt pavement raveling, which adversely affects vehicular traveling comfortability. This provides theoretical and technical support for the intelligent identification of asphalt pavement defects and enhancing road asset maintenance management in the pavement engineering industry.

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.

Interaction between roadway and sewer/water pipes

Sewage pipes and drinking water pipes as well as all other pipes such as gas, district heating, electricity, internet, telephone, cable TV, etc. in densely populated areas are located in the road space. Over time, the traffic loads as well as the road structure can change. In addition, the materials of the pipelines are subject to ageing processes. The consequences of leaking drinking water and wastewater pipes that lie in the body of the road can have extremely negative effects. Firstly, the wastewater contains harmful substances that must not be allowed to enter the groundwater under any circumstances. Secondly, the leaking drinking water pipes, especially a water pipe burst, cause the soil mechanical changes in the subsoil and the traffic surface pavements and can cause immense damage in the road surface or in the road body itself.

Cosmogenic 10Be‐ and 21Ne‐based model exposure ages of desert pavements in the Thar Desert, India

AbstractThe Thar Desert, India has desert pavements comprising angular‐subangular to well‐rounded gravels at marginally higher elevations than the surrounding terrain. Sedimentological and geomorphic analyses suggest that the pavements are lags of weathered Mesozoic and older bedrock. The presence of Palaeolithic artefacts on the pavement surfaces and occasionally within their matrix was used to infer their antiquity and landscape stability.This study presents the first surface exposure ages based on cosmic‐ray‐produced 10Be and 21Ne for pavements at four sites in the Thar Desert, viz. Bhojka, Hamira, Solanki and Jayal. The computation of model exposure ages assumed that (a) the gravels were derived from cemented conglomerates, uplifted by tectonics and thereafter disintegrated by climate, and (b) cosmogenic nuclide production in the gravels began when the conglomerates approached the surface and, continued during their disintegration, gravity sliding of individual gravels and storage, until the present. Assuming an average burial depth of 50 cm, 21Ne and 10Be data provide ages ranging from 1.30 to 2.92 Ma and 1.11 to 5.4 Ma, respectively, for the two nuclides.Published electron spin resonance ages of Thar calcretes suggest the presence of water and extreme seasonality since 1.54 Ma. Such conditions facilitated the mobilization and precipitation of carbonates. The pavement ages and the minimum age of the conglomerate at 2.51 Ma extend the time of such desertic conditions to > 2.51 Ma and suggest that the initiation of desertic conditions in the Thar was possibly linked to global aridity beginning around 3.6 Ma.Depending on assumptions, cosmic ray surface exposure (10Be) ages at Jayal range between 0.76 and 2.43 Ma. In the context of the Indian Palaeolithic, the presence of tools on the gravel surfaces and within dunes, suggests frequent occupation of this region from at least 0.76 Ma and, parallels Early to Middle Pleistocene Acheulian assemblages from Southern India.

Innovative Pavement Solutions: A Comprehensive Review from Conventional Asphalt to Sustainable Colored Alternatives

Climate change significantly impacts transportation infrastructure, particularly asphalt pavements. Similarly, the heat absorption of paved surfaces, especially conventional black pavements, significantly intensifies the urban microclimate. Paved surfaces, including asphalt pavements, account for over 30% of the covered surfaces and are vulnerable to rising temperatures, which cause not only pavement distress, such as rutting and cracking, but also urban heat islands (UHI). Sustainable pavement solutions, specifically colored pavements, have been investigated for their potential to mitigate these effects. This review presents an extensive overview of current pavement technologies, emphasizing conventional asphalt’s economic, environmental, and functional characteristics. A discussion of the benefits and challenges of colored pavements is also provided, including their ability to reduce UHI, enhance safety, and contribute to sustainable urban growth. This paper discusses advancements in pavement material science, the use of recycled materials, and the application of reflective coatings, providing insights into sustainable infrastructure development. Transitioning from conventional black pavements to sustainable colored alternatives is not merely a matter of material choice but a strategic transition toward resilient urban planning. Increasing demand for environmentally friendly infrastructure could prompt the construction industry to adopt colored pavements as a tool to promote environmental stewardship.

Design of Two-Layer Roller Compacted Concrete with Joints by KENSLAB

Roller compacted concrete (RCC) is a type of rigid pavement that is designed in a similar way to conventional concrete pavements reflecting its mode of failure. The thickness design of RCC pavements is based on keeping the flexural stresses and fatigue damage in the pavement caused by wheel loads within allowable limits. As in all jointed concrete pavements, there is a greater effect from loads placed along edges and less at interior locations in the pavement. Joints in RCC pavement are clearly critical areas and form weak points. RCC is typically constructed with saw-cut joints to prevent random cracking, improve the appearance of the pavement surface, and maintain the highest possible load transfer across the joints. This paper presents an approach to designing the thickness of a two-layer RCC with induced cracks (i.e., joints) based on load transfer stiffness and fatigue damage, using the KENSLAB program to obtain pavement life, predict joint deterioration, and pavement damage.

Potholes Detection in Doppler Radar Signal’s Power Spectral Density using Decision Tree

Potholes are defects on the surface of roads, streets, or pavements brought on by depressions or holes, which are hazardous for vehicles and pedestrians, whether small divots or huge craters. Various methods have been explored to improve the accuracy of potholes detection. Existing approaches have advantages and disadvantages. This paper presents the proposed method of pothole detection utilising Doppler Radar signal's Power Spectrum Density (PSD) together with the Decision Tree classification algorithm. While continuous waveform (CW) radar is able to identify moving targets, it cannot localise the exact depth of the reflector, which is the prominent characteristic of potholes. In addition, the target's reflected signal is likely to be masked by nearby harmonics. Since the radar is moving despite the target of interest, mounting it on a moving vehicle offers a different perspective. This paper explores the potential of Doppler radar's signal for pothole detection while comparing two Machine Learning (ML) techniques. A commercially over-the-shelf (COTS) K-LC2 Doppler radar was employed to acquire pothole and non-pothole raw datasets. Doppler signal was hardly distinguished between pothole and non-pothole, either in the time or frequency domain. Hence, Doppler signals were converted to power spectral density (PSD), and PSD's features were extracted. Extracted features were applied with the coarse Decision Tree (DT) and K-Nearest Neighbours (KNN) classification algorithms. The result exhibits a better accuracy of 91.2% for 80:20 distribution by using the Decision Tree.

An Experimental Validation-Based Study of Airport Pavement Icing Mechanisms in Saline Environments and the Development of a Simplified Prediction Model

Runway icing presents a significant challenge to aviation safety, especially in saline environments, where comprehending the icing mechanisms and predicting the icing onset are crucial for efficient airport operations. This study developed a specialized experimental apparatus to examine the mechanisms of airport pavement icing under controlled conditions. The apparatus, comprising an environmental chamber, a data acquisition system, and a scaled pavement structure, allowed for detailed simulations of various environmental factors. The experiments specifically examined the effects of the air temperature (−3 °C to −20 °C), wind speed (2 m/s to 6 m/s), and deicing salt concentration (0% to 80%) on the icing process. The results demonstrated that higher wind speeds and lower temperatures significantly accelerated the pavement surface cooling, leading to earlier icing onset. Under the most extreme conditions, the pavement reached critical icing temperatures within 15 min. In contrast, higher deicing salt concentrations delayed the icing onset by up to 67 min and 33 s at an 80% concentration, effectively lowering the pavement surface temperature. A comparison of the experimental data with the theoretical predictions showed initial consistency, although the discrepancies increased over time. This study culminated in the development of a simplified prediction model, which was validated against the experimental results, offering a practical tool for airport operators to manage runway safety during winter conditions.

Quality Assurance of Steel Slag Asphalt Mixtures for Sustainable Pavement Surface Courses

The present study investigates the use of electric arc furnace (EAF) steel slag, a by-product of the steel industry, in asphalt pavement surface courses instead of virgin aggregates (VAs). Therefore, a general performance evaluation of such mixtures compared to conventional mixtures is carried out through laboratory and in situ tests, while both mixtures are environmentally assessed using the life cycle assessment (LCA) tool. The results of the laboratory and in situ tests show that asphalt mixtures containing granulated EAF slag aggregates perform as well as mixtures containing only VA. In addition, the LCA results show that the use of EAF slag aggregates in the asphalt surface course has a lower environmental impact than the exclusive use of VA when it comes to the impact categories of acidification, climate change, marine and terrestrial eutrophication, energy consumption and photochemical pollution. In summary, these results show that replacing virgin aggregates with a proportion of EAF slag aggregate is a viable and sustainable method for road pavement construction.

Performance of the polyurethane foam injection technique for road maintenance applications

Road infrastructure plays an important role in strengthening transportation and driving the economic advancement of countries. However, the increasing traffic volume has accelerated road deterioration, particularly at critical points like bridge-road junctions. Traditional repair methods involving demolition and reconstruction lead to extended closures and high costs. This study explores the polyurethane (PU) foam injection technique as an alternative solution, which can reduce both repair time and costs. The research evaluates the application of PU foam in various road projects across Thailand, highlighting its ability to repair pavement surfaces and structures, even in severely damaged areas. Despite its advantages, the use of PU foam faces challenges due to a lack of standardized quality control. This paper proposes a set of working guidelines for PU foam injection, aimed at key stakeholders such as the Department of Highways, the Department of Rural Roads, and the Department of Local Administration. The findings underline the importance of establishing standardized methods to ensure the long-term effectiveness of PU foam in road maintenance. Future research should focus on refining these guidelines for diverse road conditions to support the sustainable development of national transportation infrastructure