New innovations in pavement materials and engineering: A review on pavement engineering research 2021

Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods, new technologies and new materials related to pavement engineering are emerging. Deterioration of pavement infrastructure is a typical multi-physics problem. Because of actual coupled behaviors of traffic and environmental conditions, predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis. In order to summarize the current and determine the future research of pavement engineering, Journal of Traffic and Transportation Engineering (English Edition) has launched a review paper on the topic of “New innovations in pavement materials and engineering: A review on pavement engineering research 2021”. Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering, this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world. The content includes asphalt binder performance and modeling, mixture performance and modeling of pavement materials, multi-scale mechanics, green and sustainable pavement, and intelligent pavement. Overall, this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Combined Prediction Method for Thermal Conductivity of Asphalt Concrete Based on Meso-Structure and Renormalization Technology

Pavement temperature field affects pavement service life and the thermal environment the near road surface; thus, is important for sustainable pavement design. This paper developed a combined prediction method for the thermal conductivity of asphalt concrete based on meso-structure and renormalization technology, which is critical for determining the pavement temperature field. The accuracy of the combined prediction method was verified by laboratory experiments. Using the tested and proven model, the effect of coarse aggregate type, shape, content, spatial orientation, air void of asphalt concrete, and steel fiber on the effective thermal conductivity was analyzed. The analysis results show that the orientation angle and aspect ratio of the aggregate have a combined effect on thermal conductivity. In general, when the aggregate orientation is parallel with the heat conduction direction, the effective thermal conductivity of asphalt concrete in that direction tends to be greater. The effective thermal conductivity of asphalt concrete decreases with the decrease of coarse aggregate content or steel fiber content or with the increase of porosity, and it increases with the increase of the effective thermal conductivity of coarse aggregate

Investigation on Three-Dimensional Void Mesostructures and Geometries in Porous Asphalt Mixture Based on Computed Tomography (CT) Images and Avizo

To investigate the void mesostructure in porous asphalt mixtures (PA), computed tomography (CT) and Avizo were utilized to scan and reconstruct the three-dimensional (3D) void model of PA-16 specimens. The void mesostructure of the specimen was quantitatively characterized through the anisotropy evaluation index. The equivalent pore network model (PNM) was extracted using the medial axis method. Based on the PNM model, the topological structure of the specimen and the morphological characteristics of the connected pores were analyzed. The results showed that the void anisotropy evaluation method can reflect the microscopic morphology of voids in porous asphalt mixtures. The cross-sectional porosity of representative elementary volume (REV) is mainly distributed between 20% and 25%, and about 90% of the macropores have a diameter between 0.5 mm and 3 mm. The distribution of cross-sectional porosity is uneven along the REV height direction. As the smallest cross-section of the seepage path, the equivalent radius of the throat is mainly between 0.1 mm and 1.5 mm, which is much smaller than the equivalent radius of the pore. The topological structure of pores is quite different, and their coordination numbers are mainly concentrated within 18. The pores with coordination numbers 1 to 10 constitute the main body of the pores inside REV, accounting for over 98% of the total number of pores. In addition, the permeability calculation results show that there is a significant difference in the permeability of each axis of REV compared to the total permeability of the superpave gyratory compactor (SGC) specimen, which illustrates that the permeability distribution presents an obvious spatial anisotropy. This study effectively reveals the heterogeneity of the 3D void morphology of porous asphalt mixtures, and it provides a reference for a better understanding of the void flow rules in drainage pavements

Optimizing Asphalt Surface Course Compaction: Insights from Aggregate Triaxial Acceleration Responses

The compaction quality of asphalt surface courses has a significant impact on the overall performance of asphalt pavements, but the dynamic response and compaction degree variations of different asphalt surface courses (top, middle, and bottom surface courses) during vibrational compaction have still received limited research. SmartRock sensors can be utilized to monitor aggregate acceleration in real-time. This study aims to address this gap using SmartRock sensor technology to further understand the compaction mechanisms of different asphalt surface courses from a particle perspective, as well as the relationship between aggregate acceleration and compaction degree. The results indicate that the rolling of steel drums induces a significant alteration of the aggregate acceleration along the roller’s rolling direction, primarily resulting in horizontal shearing in that direction. As distance increases, vibration waves gradually attenuate on both sides of vibrating drums, and surface course thickness and gradation significantly affect acceleration amplitude. There is a linear correlation between triaxial aggregate acceleration and compaction degree, with the vertical correlation being the strongest. Finally, an empirical relationship between triaxial acceleration and pavement compaction degree was established, providing a basis for predicting the asphalt surface course density. These findings enhance our understanding of pavement compaction mechanisms and promote innovation in asphalt pavement compaction and quality control methods

Combined Prediction Method for Thermal Conductivity of Asphalt Concrete Based on Meso-Structure and Renormalization Technology

Pavement temperature field affects pavement service life and the thermal environment the near road surface; thus, is important for sustainable pavement design. This paper developed a combined prediction method for the thermal conductivity of asphalt concrete based on meso-structure and renormalization technology, which is critical for determining the pavement temperature field. The accuracy of the combined prediction method was verified by laboratory experiments. Using the tested and proven model, the effect of coarse aggregate type, shape, content, spatial orientation, air void of asphalt concrete, and steel fiber on the effective thermal conductivity was analyzed. The analysis results show that the orientation angle and aspect ratio of the aggregate have a combined effect on thermal conductivity. In general, when the aggregate orientation is parallel with the heat conduction direction, the effective thermal conductivity of asphalt concrete in that direction tends to be greater. The effective thermal conductivity of asphalt concrete decreases with the decrease of coarse aggregate content or steel fiber content or with the increase of porosity, and it increases with the increase of the effective thermal conductivity of coarse aggregate

Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNN

To meet the practical needs of detecting various defects on the pointer surface and solve the difficulty of detecting some defects on the pointer surface, this paper proposes a transfer learning and improved Cascade-RCNN deep neural network (TICNET) algorithm for detecting pointer defects. Firstly, the convolutional layers of ResNet-50 are reconstructed by deformable convolution, which enhances the learning of pointer surface defects by feature extraction network. Furthermore, the problems of missing detection caused by internal differences and weak features are effectively solved. Secondly, the idea of online hard example mining (OHEM) is used to improve the Cascade-RCNN detection network, which achieve accurate classification of defects. Finally, based on the fact that common pointer defect dataset and pointer defect dataset established in this paper have the same low-level visual characteristics. The network is pre-trained on the common defect dataset, and weights are transferred to the defect dataset established in this paper, which reduces the training difficulty caused by too few data. The experimental results show that the proposed method achieves a 0.933 detection rate and a 0.873 mean average precision when the threshold of intersection over union is 0.5, and it realizes high precision detection of pointer surface defects

The Stiffness Behavior of Asphalt Mixtures with Different Compactness under Variable Confinement

The dynamic modulus is a key property determining the short- and long-term performance of asphalt pavement, and its strong dependence on confining pressure and material density (mixture compactness) has been clearly indicated in the literature. It is always challenging to reproduce three-dimensional in situ stress conditions in the laboratory. To alleviate this difficulty, in this study, a convenient experimental setup was developed, in which the lateral confinement was made present and variable as a concomitant reaction of the surrounding materials to the vertical loading. Three dense-graded mixtures were prepared to a set of four different densities and then subjected to the confined dynamic modulus test. The results indicated a significant dependence of the confined modulus on the three factors of temperature, frequency, and compactness and that the mixture with coarser gradation demonstrated a less sensitivity to these parameters. A mathematical model was developed for the dynamic modulus master curve unifying these factors by means of horizontal shifting due to the time–temperature superposition principle (validated against the variable confinement at different compactness) and the vertical shift factor as a function of reduced frequency and compactness. The adequacy of the model was demonstrated using the experimental data, and its potential application in field pavement compaction was discussed