Journal of Road Engineering最新文献

Recently, researchers in the road field are focusing on the development of green asphalt materials with lower emission of volatile organic compounds (VOCs). The characterization methodology of asphalt VOCs and the influencing factors on VOCs release have always been the basic issue of asphalt VOCs emission reduction research. Researchers have proposed a variety of asphalt VOCs characterization methodologies, which also have mutually irreplaceable characteristics. Asphalt VOCs volatilization is affected by many factors. In this study, asphalt VOCs characterization methodologies were summarized, including their advantages, disadvantages, characteristics and applicable requirements. Subsequently, the influencing factors of VOCs release, such as asphalt types and environment conditions, are summarized to provide theoretical support for the emission reduction research. The classification and mechanism of newly-development asphalt VOCs emission reduction materials are reviewed. The reduction efficiencies are also compared to select better materials and put forward the improvement objective of new materials and new processes. In addition, the prospects about development of VOCs release mechanism of asphalt materials during the full life cycle and feasibility research of high-efficiency composite emission reduction materials in the future were put forward.

The goals of this study are to assess the viability of waste tire-derived char (WTDC) as a sustainable, low-cost fine aggregate surrogate material for asphalt mixtures and to develop the statistically coupled neural network (SCNN) model for predicting volumetric and Marshall properties of asphalt mixtures modified with WTDC. The study is based on experimental data acquired from laboratory volumetric and Marshall properties testing on WTDC-modified asphalt mixtures (WTDC-MAM). The input variables comprised waste tire char content and asphalt binder content. The output variables comprised mixture unit weight, total voids, voids filled with asphalt, Marshall stability, and flow. Statistical coupled neural networks were utilized to predict the volumetric and Marshall properties of asphalt mixtures. For predictive modeling, the SCNN model is employed, incorporating a three-layer neural network and preprocessing techniques to enhance accuracy and reliability. The optimal network architecture, using the collected dataset, was a 2:6:5 structure, and the neural network was trained with 60% of the data, whereas the other 20% was used for cross-validation and testing respectively. The network employed a hyperbolic tangent (tanh) activation function and a feed-forward backpropagation. According to the results, the network model could accurately predict the volumetric and Marshall properties. The predicted accuracy of SCNN was found to be as high value ​>98% and low prediction errors for both volumetric and Marshall properties. This study demonstrates WTDC's potential as a low-cost, sustainable aggregate replacement. The SCNN-based predictive model proves its efficiency and versatility and promotes sustainable practices.

Pavement infrastructure is vital in providing services and links between various sectors of society. Therefore, the preservation and maintenance of these roads are critical to attaining a pavement network in good condition throughout its service life. Various performance indicators like the international roughness index (IRI), pavement condition index (PCI), and present serviceability rating (PSR) have been used by the state department of transportation (DOT) and highway agencies for evaluating pavement surface conditions and planning future maintenance strategies. Limited data availability, multiple distresses depending on region, lack of correlation of these condition indices to maintenance strategies, and data collection limitations pose a challenge for applying these indices to local conditions. This paper compares condition indices of different states for rigid pavements. Further, using a specific condition index for local conditions is also highlighted. For this purpose, five states and their corresponding condition indices were evaluated and compared to the Michigan DOT distress index (DI). These states include Virginia, Minnesota, North Dakota, Louisiana, and Oregon. The corresponding distresses of each condition index were converted to make them compatible with the MDOT DI. This study used the MDOT's pavement management system (PMS) database to evaluate each condition index for 433 rigid pavement sections. Each distress index was plotted against MDOT DI and compared using a paired t-test. Results show that the condition indices of Virginia and Minnesota are comparable to DI in terms of the Spearman correlation value. The t-test results show that except for Virgina, condition indices from other states statistically differ from DI. Therefore, one can't use those directly for local conditions in Michigan. This paper presents the evaluation and data requirements for each condition index and its impact on selecting a maintenance treatment.

To comprehensively assess the current state-of-art in asphalt foaming technology, the following four key aspects have been reviewed systematically: foaming principles, test methods, evaluation indicators, and influencing factors. Key findings reveal that asphalt foaming was primarily driven by the vaporization of water, with deterioration processes including bubble collapse and liquid film drainage. However, the current understanding of asphalt foaming principles remains limited, primarily due to difficulties in capturing and precisely measuring its microscopic behaviors during asphalt foaming process. Volume changes provided an intuitive means to evaluate the expansion capacity of asphalt and its foaming stability. Bubble evolution characteristics of foamed asphalt offered promising insights into its foaming performance. Traditional ruler and stopwatch-based assessments were being superseded by automated techniques like laser and ultrasonic ranging. Nevertheless, the current measuring equipment still lacks the capability to comprehensively evaluate the foaming effect of asphalt across various dimensions. Asphalt temperature and foaming water consumption significantly affected asphalt foaming performance, and the inclusion of foaming agents typically led to a notable increase in the half life of foamed asphalt. However, the interaction between foaming agents and asphalt, as well as the underlying mechanisms affecting the foaming effect, are still unclear and require further exploration. Future research should primarily focus on the correlation between asphalt foaming effect and mixture performance, aiming to guide the practical engineering application of foamed asphalt mixtures and enlarge the advantages of such low-emission and sustainable mixtures.