Journal of Road Engineering最新文献

This study normalized the mixture's fatigue behavior at various temperatures, and the strength and fatigue tests of the mixture were conducted. The stress state of the asphalt mixture includes direct tensile, uniaxial compression, and indirect tensile. The Desai yield surface and fatigue path were proposed. And a normalized fatigue characteristics model of the mixture was established. The following conclusions were obtained. With the increases in the loading rate, the strength of the asphalt mixture increased. As the temperature increases, the strength of the mixture is reduced. At various temperatures and rates, the strength forms a closed curved surface. The Desai strength yield surface was established, which forms a closed curved surface. When the loading rate and temperature are below a certain critical line, the asphalt mixture will not undergo strength damage. At a fixed stress state, the fatigue damage path of the mixture was determined. The stress ratio was determined considering the influence of the loading rate. In this way, a normalized model can be described to express the asphalt mixture fatigue properties at various temperatures and stress levels. For the asphalt mixture in an indirect tensile state, the normalized fatigue equation parameter is 4.09. This model is more suitable for reflecting the viscous-elastic behavior of the mixtures than the fatigue equation determined by the notional stress ratio.

In the induction heating of airport pavement to remove snow and ice, soft magnetic geopolymer composite (SMGC) can be used to gather the dissipated electromagnetic energy, thus enhancing the energy utilization efficiency. The aim of this work is to analyze the influence mechanism of iron powder content on the electromagnetic and mechanical performance of SMGC, so as to provide theoretical guidance for the design of soft magnetic layer within airport pavement structure. The results show that the increase of iron powder content reduces the resistance and magnetoresistance of SMGC by decreasing the content of non-magnetic phases between iron powder. However, the reduction of iron powder spacing also provides a shorter transmission path for the inter-particle eddy currents in the SMGC specimen, which enhances the exchange coupling between iron powder, thus increasing the electromagnetic loss. Therefore, the compatibility between magnetic permeability and electromagnetic loss should be considered comprehensively in the mix design of SMGC. In addition, although iron powder can enhance the mechanical properties of SMGC by improving the density of geopolymer matrix, the excessive amount of iron powder can lead to a weak interfacial transition zone between geopolymer matrix and iron powder. According to the induction heating results, optimized SMGC can improve the energy transfer efficiency of induction heating by 24.03%.

Cohesive failure is one of the primary reasons for low-temperature cracking in asphalt pavements. Understanding the micro-level mechanism is crucial for comprehending cohesive failure behavior. However, previous literature has not fully reported on this aspect. Moreover, there has been insufficient attention given to the correlation between macroscopic and microscopic failures. To address these issues, this study employed molecular dynamics simulation to investigate the low-temperature tensile behavior of asphalt binder. By applying virtual strain, the separation work during asphalt binder tensile failure was calculated. Additionally, a correlation between macroscopic and microscopic tensile behaviors was established. Specifically, a quadrilateral asphalt binder model was generated based on SARA fractions. By applying various combinations of virtual strain loading, the separation work at tensile failure was determined. Furthermore, the impact of strain loading combinations on separation work was analyzed. Normalization was employed to establish the correlation between macroscopic and microscopic tensile behaviors. The results indicated that thermodynamic and classical mechanical indicators validated the reliability of the tetragonal asphalt binder model. The strain loading combination consists of strain rate and loading number. All strain loading combinations exhibited the similar tensile failure characteristic. The critical separation strain was hardly influenced by strain loading combination. However, increasing strain rate significantly enhanced both the maximum traction stress and separation work of the asphalt binder. An increment in the loading number led to a decrease in separation work. The virtual strain combination of 0.5%-80 provided a more accurate representation of the actual asphalt's tensile behavior trend.

Pavement management systems (PMS) are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost. To accomplish this objective, the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time. The pavement condition index (PCI) is a commonly used metric to evaluate the pavement's performance. This research aims to create and evaluate prediction models for PCI values using multiple linear regression (MLR), artificial neural networks (ANN), and fuzzy logic inference (FIS) models for flexible pavement sections. The authors collected field data spans for 2018 and 2021. Eight pavement distress factors were considered inputs for predicting PCI values, such as rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, patching, potholes, and delamination. This study evaluates the performance of the three techniques based on the coefficient of determination, root mean squared error (RMSE), and mean absolute error (MAE). The results show that the $R^2$ values of the ANN models increased by 51.32%, 2.02%, 36.55%, and 3.02% compared to MLR and FIS (2018 and 2021). The error in the PCI values predicted by the ANN model was significantly lower than the errors in the prediction by the FIS and MLR models.

The anti-skid performance of snowy and icy pavements is a popular research topic among road workers. Snow and ice are pollutants on a road surface. They significantly reduce the skid resistance of pavements, and thus, cause traffic accidents. Pertinent research progress on the skid resistance of snowy and icy pavements was reviewed and summarized in this work. The formation and classification of snowy and icy pavements were described on the basis of the state of snow and ice. The friction mechanisms between tires and snowy and icy pavements were revealed. Measurement methods and their applicability to the skid resistance of snowy and icy pavements were summarized. Factors that affect the skid resistance of pavements were discussed from the perspectives of pavement, environment, and vehicle. In addition, models of snowy and icy pavement resistance were classified into experience, mechanical, and numerical models. The advantages and disadvantages of these models were then compared and analyzed. Some suggestions regarding snowy and icy pavements were presented in accordance with the aforementioned information, including the development of efficient testing tools, the quantification of skid resistance under the coupling effects of multiple factors, the establishment of unified evaluation standards, and the development of more effective skid resistance models.

Concrete pavement is accompanied by two major functional properties, namely noise emission and friction, which are closely related to pavement surface texture. While several technologies have been developed to mitigate tire-pavement noise and improve driving friction by surface texturization, limited information is available to compare the advantages and disadvantages of different surface textures. In this study, a state-of-the-art and state-of-the-practice review is conducted to investigate the noise reduction and friction improvement technologies for concrete pavement surfaces. The commonly used tests for characterizing the surface texture, skid resistance, and noise emission of concrete pavement were first summarized. Then, the texturing methods for both fresh and hardened concrete pavement surfaces were discussed, and the friction, noise emission and durability performances of various surface textures were compared. It is found that the next generation concrete surface (NGCS) texture generally provides the best noise emission performance and excellent friction properties. The exposed aggregate concrete (EAC) and optimized diamond grinding textures are also promising alternatives. Lastly, the technical parameters for the application of both diamond grinding and diamond grinding & grooving textures were recommended based on the authors' research and practical experience in Germany and the US. This study offers a convenient reference to the pavement researchers and engineers who seek to quickly understand relevant knowledge and choose the most appropriate surface textures for concrete pavements.

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users. This paper presents a state-of-the-art review of multi-objective optimization (MOO) problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities. First, the paper presents a taxonomy and hierarchy for these activities, the role of funding sources, and levels of jurisdiction. The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling (historical practices, expert opinion, and explicit mathematical optimization) and identifies the pros and cons of each mechanism. The paper then focuses on the optimization mechanism and presents the types of optimization problems, formulations, and objectives that have been used in the literature. Next, the paper examines various solution algorithms and discusses issues related to their implementation. Finally, the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities, and makes recommendations to overcome some of these barriers.