Search

Pavements skid resistance, especially in wet states, is an essential indicators of traffic safety. However, the existing testers cannot reflect actual road friction situations and oversimplify water's effect on texture and skid resistance. Therefore, this study compared a traditional British pendulum tester (BPT) with actual vehicle braking. A simplified water coverage simulation programme was proposed, and the BPT characterised the influence of different thin water films on skid resistance in the laboratory. Besides, several freshly constructed road sections (AC-13, SMA-13, and PAC-13) were tested with both BPT and vehicle braking during rainfall. The results showed that water film, even thin, is unfavourable for pavement skid resistance. The BPT results were inconsistent with that of the vehicle braking. Because the tribological properties characterised by BPT are provided by micro-texture and partly macro-texture within the rubber-texture interaction depth (0.1 mm). Larger texture depth and more tortuous and connected pores will offer pavements larger water capacity and higher water permeability, which is beneficial for alleviating braking distance reduction during rainfall. In general, an ideal skid resistance tester should consider characteristics within the rubber-texture contact depth and should consider braking conditions such as speed, water film thickness, drainage method, and slip rate. [ABSTRACT FROM AUTHOR]

The patch recognition and localisation from high-resolution pavement images play a key role in evaluating asphalt pavement condition. To achieve the purpose, two line-scan cameras were employed to acquire lots of full-scale asphalt pavement images with 1 mm2 per pixel resolution in the field. The raw images were pre-processed to construct the dataset, including 984 patches of various shapes in 827 pavement images. Subsequently, the YOLO series models innovatively applied in automatically detecting the patches from pavement images were well trained with the pavement training and validation sets. The testing results reveal that the F1-score and mAP@0.5 values of the YOLOv4 model are 0.911 and 92.92% respectively in the test set. The comprehensive recognition accuracy of the YOLOv4 model outperforms the other YOLO models. And the detection speed of the YOLOv4 model for pavement data video is 38.1 frames per second. Additionally, all the features of patches in the partial testing pavement images, no matter whether they are strip, block, intersection, blurry within the shadow, or background noise, can be correctly predictedby the rectangular box. The average localisation errors of the width and height of the predicted patches are 21 and 16 mm respectively, and the average IoU is 0.83. [ABSTRACT FROM AUTHOR]

The thermal cracking behaviour of asphalt pavement is complex and includes thermal contraction, low-temperature relaxation, and low-temperature damage. However, using relaxation characteristics alone instead of low-temperature properties leads to an incomplete evaluation. In this paper, the relationship between viscoelastic properties and relaxation characteristics was analyzed. Based on the time domain characteristics of relaxation, the relaxation rate and cumulative stress at per unit strain rate (CSSR) were proposed as evaluation indexes corresponding to the long-term/short-term characteristics of the relaxation. By combining the relationship among the low-temperature performance obtained by Thermal Stress Restrained Specimens Test (TSRST) and relaxation characteristics, evaluation indexes for the low-temperature performance of asphalt mixture were preferably selected. The results show that asphalt binders dominate the relaxation characteristics of asphalt mixtures, but do not directly replace the low- temperature performance of asphalt mixtures. The relaxation characteristics expressed by the traditional viscoelastic indexes were insufficient to fully evaluate the low- temperature performance of asphalt mixtures. Furthermore, the results of the relaxation characteristic index for evaluating the low-temperature performance were different under different temperature conditions, indicating that performance at a single temperature cannot replace the low-temperature performance at all temperatures. [ABSTRACT FROM AUTHOR]

During the long-term service of asphalt pavement, under the combined action of repeated loads and environmental factors, asphalt pavement will gradually appear small damage. When the damage accumulates to a certain extent, the asphalt pavement will have serious rutting deformation, resulting in poor highway flatness and greatly reducing the service life of the asphalt pavement. The traditional monitoring methods of pavement rutting deformation need high environmental detection conditions. At the same time, there are some problems, such as subjective judgment standard and unable to monitor in real time. The commonly used road surface coring detection methods will destroy the original continuous structure of the pavement and easily cause internal damage of the pavement under the action of rain and snow environment. In this study, based on the intelligent aggregate (IA) equipped with high-precision attitude sensor, a new pavement rutting deformation monitoring method was proposed. Combined with the indoor rutting experiment, the relationship model between intelligent aggregate attitude change and rutting deformation was established, and the relationship model between real pavement rutting deformation and intelligent aggregate attitude change was established by finite element method, so as to realize the rutting deformation monitoring method based on Intelligent aggregate. [ABSTRACT FROM AUTHOR]

The service performances of asphalt pavement, especially rutting, will be inevitably affected by climate change. However, existing studies have generally focused on the rutting depth and rutting life, and thus became insufficient for comprehensively evaluating the influence of climate change on rutting over the service life. A resilience assessment method for asphalt pavement rutting is developed to solve the above problem. First, the original resilience method is extended to fit the system whose performance level continues to decline. Then, the calculation formulas of rutting resilience are derived by combining the rutting prediction model and the level assessment model. Subsequently, the influence degrees of climate change in representative cities on rutting resilience are studied. The results suggest that neglecting climate change in rutting design of asphalt pavement will lead to insufficient resilience, especially in northern China. Furthermore, the predicted temperature under RCP8.5 should be employed for asphalt pavement design. [ABSTRACT FROM AUTHOR]

• Capture appropriate models in the discussion of TNn, TN5d and CR. • TNn and TN5d extreme events influence on pavement temperature with depth. • Identify the low-temperature performance based on winter temperature extremes. Winter temperature extremes are of major interest due to their potential damaging impacts related to pavement temperature and thermal cracking. In the present paper, three climate indices are adopted to describe the feature of winter temperature extremes. Statistical models for indices are analyzed based on the observed data in Harbin. The threshold values with their 99.9% cumulative frequency are proposed. Experiments were carried out in order to investigate the impacts of winter temperature extremes on pavement temperature and low-temperature performance of asphalt mixture. The results show that Weibull distribution is an appropriate model in the discussion of air temperature and its duration, and Frechet is the best-fitted model for the rate of air temperature change. Comparisons of experimental results indicate the importance of winter temperature extremes to asphalt pavement temperature. The influence of low-temperature properties by different temperature conditions is also analyzed. The low-temperature performance in Harbin can be accurately quantified by linear interpolation. [ABSTRACT FROM AUTHOR]

In this paper, three-dimensional structure feature points of the asphalt mixture were obtained based on the industrial CT. The surface morphology of the asphalt mixture was then extracted through image morphology processing and evolutionary matching algorithms. A series of accelerated polishing tests were utilized to simulate the long-term polishing effect of the tire load on the asphalt pavement. Combining with digital image analysis, the full-life morphology evolution trend of the asphalt pavement was analyzed, and the full-life surface morphology datasets of the asphalt pavement were constructed. By comparing the power spectrum density of the asphalt pavement morphology at different wear stages, a segmented evaluation method based on the spectral fractal method was proposed. Combining the spectral fractal parameters and statistical geometric parameters, a multi-scale asphalt pavement surface morphology feature evaluation index was proposed. This provides a more intricate and detailed analysis of the surface structure of asphalt mixtures. • The full-life morphology evolution trend of the asphalt pavement was analyzed. • A segmented evaluation method based on the spectral fractal method was proposed. • A multi-scale asphalt pavement surface morphology feature evaluation index was proposed. [ABSTRACT FROM AUTHOR]

This study delves into the complexities of frost formation on road surfaces, a phenomenon that presents significant safety hazards in transportation due to its sudden emergence and unpredictable nature. Despite advanced meteorological warning systems for snowfall and freezing rain, black ice and frost remain difficult to predict and counteract. To address this, a controlled indoor simulation experiment was designed to investigate the characteristics of road surface frosting at low temperatures. Results from the experiment indicated that growth of the frost layer thickness follows a parabolic trend with extended freezing time yet the mass of the frost layer increases at a roughly linear rate. The data also revealed that lower temperatures expedite the phase transition of water vapor to ice, leading to faster increases in frost layer height. Additionally, the effects of air temperature and velocity on frost properties were examined. Interestingly, higher air temperatures facilitated rapid frost formation initially, but later stages displayed a plateau phase in the rate of accumulation. Furthermore, increased air velocity up to 1 m/s resulted in greater frost mass, but higher velocities diminished frost formation due to enhanced heat transfer. In conclusion, this study offers a detailed analysis of frost layer development under controlled conditions, providing valuable insights into the environmental factors influencing this hazardous phenomenon. The findings contribute to the understanding of frost dynamics on road surfaces, with implications for improving predictive models and developing effective countermeasures for road safety during icy conditions. • An indoor simulation experiment reveals the parabolic growth of road surface frost layer thickness and a roughly linear increase in mass over time. • Lower temperatures significantly expedite the phase transition from water vapor to ice, hastening the vertical growth of frost layers on road surfaces. • Air temperature and velocity are found to have a nuanced effect on frost formation, with higher temperatures initially promoting rapid accumulation. [ABSTRACT FROM AUTHOR]

In regions impacted by snow and ice, icy road surfaces can significantly reduce road capacity and endanger vehicle occupants. While traditional de-icing methods, such as spreading de-icing salt and mechanical de-icing, have drawbacks such as environmental pollution and damage to road facilities, superhydrophobic coating is gaining attention as a new, environmentally friendly, and efficient anti-icing method. Superhydrophobic coating can reduce the adhesion between the ice layer and the road surface, so as to achieve the purpose of road anti-icing in a low environmental impact way. To improve the anti-icing performance of asphalt pavement under low-temperature and humid conditions and reduce the impact of snow and ice freezing disaster, we prepared nano-SiO 2 superhydrophobic coatings(SSC) and nano-SiO 2 /graphene composite superhydrophobic coatings(SGSC) using aqueous epoxy resins, hydrophobic nano-SiO 2 , and sparsely layered graphene as raw materials. The coatings' wettability, ice resistance, and road performance were analyzed. The study found that the contact angles of the SSC and SGSC were 151.2° and 153.0°, respectively. The BPN value of the SGSC decreased by 12.6% after application to the asphalt pavement, but it still meets safety requirements. The coating also maintains a certain degree of ice-resistant performance within five freeze-thaw cycles. The study results indicate that superhydrophobic coatings have the potential to improve pavement ice resistance. • Superhydrophobic coating for anti-icing on asphalt pavements. • Preparation methods and performance evaluation of superhydrophobic coatings. • Nano-SiO2 superhydrophobic coating extends freezing time by 322.5%. • Nano-SiO2/graphene composite superhydrophobic coating reduces melting time by 34.5%. • Anti-icing Property remains within 5 freeze-thaw cycles. [ABSTRACT FROM AUTHOR]

In order to effectively remedy shallow grooves on asphalt pavements, this study presents an innovative solution, termed Polyamide-based Asphalt Pavement Shallow Groove Rapid Repair Material (PA-SGRRM). This material employs polyamide 6 (PA6) as the matrix resin, in conjunction with polypropylene (PP), ethylene-vinyl acetate copolymer (EVA), and maleic anhydride-grafted polyethylene-octene copolymer (POE-g-MAH). It is manufactured through an extrusion granulation process. At the outset, a molecular dynamics approach was employed to assess the compatibility of the primary constituents and the adhesion between the matrix resin and asphalt, confirming the rationale behind the material selection. Subsequently, the orthogonal experimental design method was utilized to investigate the influence of design parameters (POE-g-MAH content, PP content, and EVA content) on the flexural strength, tensile strength, low-temperature toughness, and melt flow rate of PA-SGRRM. Following this, we prepared samples with the optimal proportions of EVA and POE-g-MAH and conducted a separate analysis to evaluate the impact of PP on the saturated moisture content of PA-SGRRM. Finally, we employed Scanning electron microscopy (SEM) to observe the microstructures of the low-temperature fracture surface and ambient temperature tensile fracture surface of the PA-SGRRM and employed Fourier transform infrared spectroscopy (FTIR) to deduce the primary chemical reactions occurring within the PA-SGRRM. The results demonstrated that POE-g-MAH played a crucial role in enhancing the compatibility of the simulated hybrid system consisting of PP and PA6. Additionally, it was observed that adhesion between PA6 and asphalt existed, with a gradual decrease as temperature rose. The content of POE-g-MAH and PP were the two primary parameters influencing the performance of PA-SGRRM. POE-g-MAH exerted a significant adverse impact on the flexural strength, tensile strength, and melt flow rate of PA-SGRRM, while substantially improving its low-temperature toughness. The PP positively reduced the water absorption characteristics of PA-SGRRM. Based on the experimental results, the optimal combination of design parameters for PA-SGRRM is recommended. [Display omitted] • POE-g-MAH can improve the compatibility between the components of PA-SGRRM. • There is a strong adhesion between PA6 and asphalt. • POE-g-MAH can effectively improve the low temperature toughness of PA-SGRRM. • Significant improvement of moisture absorption of PA-SGRRM by PP. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) has been prevailingly applied in nondestructive testing of internal distresses within asphalt pavement. However, the interpretation of abnormal echo signals in massive pavement GPR images is time-consuming and labor-intensive, and prone to misjudgment. To address this issue, a three-step method was proposed for the automatic detection and location of the internal distresses (eg: crack and debonding) echo features from GPR images. The on-site and numerical simulated GPR images of the asphalt pavement together formed the dataset required for the subsequent deep learning models. The first step is that You Only Look Once version 3 (YOLOv3) model predicted rectangular boxes for enclosing the internal distress echo features from GPR images. The second step involves developing the U-net models to segment the internal distress echo feature pixels in the cropped rectangular boxes. The last step is that the median points of the segmentation of the internal distress echo feature were fitted with a theoretical curve equation, to estimate the location of the internal distress. The proposed method has shown that the comprehensive detection accuracy of the internal distress echo features can reach 96.99%, the semantic segmentation accuracies of the internal distress echo feature pixels are not less than 0.856, and the average deviation of the estimated depths of the internal distresses is 3.25 cm. The research method makes further advances in accurately and automatically detecting and locating the internal distresses of asphalt pavement. • Clarify the electromagnetic wave features of internal distresses (crack and debonding) in 3D-GPR images of asphalt pavement. • Augment 3D-GPR longitudinal section image dataset by using signal filtering techniques and adding simulation images. • Propose a three-step method for detecting and locating internal distresses from GPR images automatically in whole process. [ABSTRACT FROM AUTHOR]

[Display omitted] • A real-time assessment method of asphalt pavement moduli and traffic loads using monitoring data from built-in sensors was proposed. • The optimal sensor placement and stepwise decoupling algorithm were complemented and beneficial to decouple the identification of pavement moduli and traffic loads. • The accuracy of the proposed method was comprehensively validated from the theoretical and practical perspectives. • The proposed method has application prospects for understanding the damage behavior of pavement, improving pavement design methods, and guiding maintenance decisions. Asphalt pavement performance is gradually attenuated subjected to repeated traffic loads. Grasping the relationship between the repeated traffic loads and pavement modulus attenuation is crucial for understanding pavement mechanical behavior, improving pavement design methods, and guiding maintenance decisions. However, the current bottleneck is that neither the existing non-destructive testing nor monitoring methods can simultaneously obtain the information of traffic loads and pavement moduli. To fill this gap, an innovative real-time assessment method of asphalt pavement moduli and traffic loads from the aspect of structural health monitoring was proposed. On the one hand, the optimal sensor placement was determined by the theoretical relationships between moduli, loads, and mechanical responses based on elastic multi-layered theory. On the other hand, the corresponding algorithm for stepwise decoupling identification of asphalt pavement moduli and traffic loads to match the optimal sensor placement was developed. Therefrom, the validity of the proposed assessment method was theoretically verified, and the effects of the viscoelastic property of asphalt materials, measurement errors, and layer thickness on the identification accuracy were comprehensively discussed. The proposed assessment method was applied to the realistic pavement to further prove its applicability. Results show that the proposed assessment method can both evaluate pavement moduli and traffic loads accurately. The proposed assessment method has application prospects for simultaneous real-time monitoring of the pavement modulus attenuation and traffic loads, which helps understand the pavement damage behavior, improve pavement design methods, and guide maintenance decisions. [ABSTRACT FROM AUTHOR]