Journal of Traffic and Transportation Engineering-English Edition最新文献

Currently, few studies explore the internal phases of styrene-butadiene-styrene (SBS) modified bitumen at the nanoscale, though the surface phases of SBS modified bitumen have been deeply understood through a lot of research. The present study uses the atomic force microscopy (AFM) peak force quantitative nanomechanical mode (PF-QNM) to investigate the nanomechanical properties of the interior of linear SBS modified bitumen and corresponding mastics at the nanoscale. Firstly, the suitable experimental methods for the interior of bitumen and mastics are explored. Then, the phase relationship between surfaces and interiors of linear SBS modified bitumen is determined by analyzing nanomechanical properties. On this basis, in comparison with the internal phases of base bitumen, the effect of modifiers on phases is deeply investigated. Finally, the internal phases of linear SBS modified bituminous mastics are further investigated. The results reveal that the interior of linear SBS modified bitumen only has two phases, which form in a manner like periphase and paraphase on the surface. In contrast to base bitumen, linear SBS modified bitumen does not create new phases and change the properties of original phases in the interior, but affects the proportion of A-phase and B-phase and presents the homogenization. Furthermore, due to the influence of preparation methods, only the bitumen area away from the fillers can be imaged by AFM PF-QNM. It can be found that the addition of mineral fillers also slightly changes the internal phase distribution of linear SBS modified bitumen, while the effect is less than that of fillers on base bitumen.

Numerous studies showed that synthetic fibers are effective for reinforcing the mechanical performance of the asphalt mixture due to their high strength properties, ductility, and durability characteristics. In this paper, the objective is to present a review of the reinforcement effect of synthetic fiber on the mechanical performance of the asphalt mixture. This paper reviews the relevant literature on the characterizations and applications of synthetic fibers to improve different mechanical properties of asphalt mixes, which can provide a reference for the applications and development of synthetic fibers in asphalt pavement. The characteristics of common synthetic fibers are introduced and the utilization of synthetic fibers in asphalt mixture is discussed. Different surface treatment methods for fiber are reviewed and it is found that surface treatment can improve the performance of the synthetic fibers in asphalt mixtures, especially the chemical surface treatment method. The influence of synthetic fiber addition on the mechanical properties of the asphalt concrete such as rutting resistance, tensile strength, water stability performance, and cracking resistance are then discussed. The research results show that aramid, glass, and polyester fibers improve the fatigue cracking resistance of asphalt mixture. Polyester fibers, polyamide fibers, and carbon fibers are used to improve resistance to the permanent deformation of asphalt pavement.

Salt freezing damage has severe impacts on durability of cement-based materials (CBMs). Calcined layered double hydroxide (CLDH), as an efficient environmental-friendly adsorption material, can impart excellent salt freezing resistance to CBMs. In this work, salt freezing resistance improvement of CBMs incorporated with CLDH was experimentally evaluated by chloride binding capacity, mass loss rate, relative dynamic elastic modulus, setting time, compressive strength, and micro structure tests. Beside these, the salt freezing damage model was established to effectively express the quantitative relationship between influencing factors and evaluation indexes of the salt freezing resistance of CBMs. Results show that CLDH can reconstruct its original layered structure to form reconstructed layered double hydroxide (RLDH). RLDH combines with chloride ions to form RLDH-Cl recrystallization, which can improve chloride binding capacity and pore structures of CBMs to relieve the salt freezing damage. The salt freezing damage model indicates that the suitable CLDH content can evidently alleviate the salt freezing damage, which facilitates the quantitative analysis of the effect of CLDH on the salt freezing resistance of CBMs.

In this study, different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed, and the panel data models (PDMs) are highlighted for longitudinal data sets. The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation. The models could simultaneously capture the heterogeneity and update forecast through inspections. PDMs are applied to tackle the cross-section heterogeneity of longitudinal data, and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data. To illustrate the methodology, three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China. Estimation results obtained by ordinary least square (OLS) estimator and system generalized method of moments (SGMM) are compared for two dynamic instances. The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time. There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time. In addition, SGMM does not obtain higher prediction accuracy than OLS in this case. Finally, it is recommended to specify the inspection intervals as several constants with integer multiples.

In this paper, the images of tunnel surface are obtained by tunnel lining rapid inspection system, and tunnel crack forest dataset (TCFD) is established. The disaster characteristics of tunnel cracks are analyzed and summarized. Solutions of tunnel crack segmentation (TCS) method are developed for the detection and recognition of cracks on tunnel lining. According to the image features of the tunnel lining and the optical principal of detection equipment, effective image pre-processing steps are carried out before crack extraction. The tunnel image of TCFD is divided into appropriate number of blocks to magnify the local features of tunnel cracks. Local threshold segmentation method is used to traverse the blocks successively, and the first target block with crack is obtained. The seed in the target block were obtained by adaptive localization method and mapped to the whole image. Region growing is performed through crack seed until complete tunnel crack is extracted. The results show that the precision, recall rate and F-measure of tunnel cracks under the TCS method can reach 92.58%, 93.07% and 92.82% without strong interference. According to the binary images processed by TCS method, the projection images of different types of tunnel cracks and their respective laws are obtained. Furthermore, the TCS method is implemented and deployed as a GUI software application.

Cracking failure of cement-treated base (CTB) has always been the concern of highway constructors. Mesoscale cracking analysis is an important means to study the damage degradation mechanism, which is difficult to be characterized by experimental techniques alone. The objective of this paper is to develop a random aggregate modelling method to simulate the mesoscopic cracking of CTB material. A minimum rectangle area method was proposed to calculate the polygon aggregate size, which is closer to the sieving analysis than the average radius method. A buffer zone method was proposed to determine the distance between randomly generated polygon aggregates. Based on the proposed random algorithm, finite element method (FEM) was adopted to build the mesoscopic model of CTB including aggregate, mortar, interfacial transition zone (ITZ) and air voids. Laboratory tests were conducted to validate the numerical model. Then the sensitivity analyses were conducted to study the influencing factors on cracking behavior. The simulation results indicate that the higher aggregate content and the finer gradation lead to the increase of ITZ, thus reducing the cracking resistance of the CTB material. Low porosity content is able to significantly reduce the stress concentration and thus improves the cracking resistance. The research results of this paper could be used to guide the crack resistant design of CTB material.

The construction of rigid pavements using conventionally vibrated concrete consumes a significant amount of energy as it requires rigorous vibrations. This also requires a high number of laborers and creates noise during construction. Thus, a new kind of concrete called semi-flowable self-consolidating concrete (SFSCC) for pavement construction using slip-form paving technology is reviewed in this article. The SFSCC requires no energy for compaction as it gets compacted under its self weight. It also renders shape stability in the fresh state which is critical to expedite the construction in slip form concreting. The review focuses on the need, evolution, and requirement of the ingredient materials, mix design, and methods for testing the properties of SFSCC. Further, the utilization of industrial wastes in the construction industry and the production of self-consolidating concrete are discussed. The literature on the effect of different materials on the properties of such concrete and field studies in this context are discussed. Lastly, its suitability as pavement construction material either in normal rural roads or in low-volume village roads is discussed in the Indian context. The review reveals that relatively less amount of study on SFSCC in general and as a pavement material in particular is available in the literature and pursuant to this, creates a wide scope of research.

Phase change materials (PCMs) can regulate the temperature in asphalt pavement and minimize temperature-related problems, such as rutting and thermal cracking, because of their ability to store and release latent heat. Suitable PCMs can also enable additional road surface functions, such as snow melting ability, freeze-thaw cycle resistance, and heat island reduction. These functions are helpful in achieving intelligent, green, and sustainable transportation systems. Although the research on PCMs for asphalt pavement has been carried out for more than 10 years, a systematic material system and mature application technology have not yet been formed. The main reasons for restricting the development of this technology include the lack of suitability between the PCMs and asphalt pavement, the quantitative characterization of phase change temperature regulation property, and the evaluation of the effect of phase change energy storage on improving pavement performance. Although the published review has made a comprehensive summary of the existing research, it has yet to identify the key restricting the development of this technology and carry out a review and discussion based on it. To grasp the development status of the application of PCMs in asphalt pavement, sort out the development needs and break through the technical barriers, this study systematically summarizes the preparation and performance of PCMs for asphalt pavement, compares the performance and evaluation methods of asphalt mixtures with different PCMs, and summarizes the numerical simulation methods of phase change asphalt mixtures. Finally, this study presents potential approaches to address critical technical issues and discusses possible future research.

Several previous studies have documented the progress in polymer modified asphalt binder with respect to materials’ types and properties. However, limited or no effort was made to document findings on the laboratory preparation practices of polymer modified asphalt binder. Full and clear disclosure of asphalt blend preparation method is necessary for research continuity, reproducibility, and accurate adaptation by future studies for analogy and reliable conclusions. The laboratory preparation processes of various modified asphalt binders have been reviewed in this study. Factors affecting the optimal mixing of asphalt-polymer blends were summarized. The optimal mixing conditions associated with different asphalt modifiers were documented. Gap in the literature on the current practice for the preparation and reporting of various modified asphalt binder was discussed. Modifiers include styrene butadiene styrene (SBS), polyethylene (PE), waste tire rubber or crumb rubber (CR), ethylene vinyl acetate (EVA), sulfur, polyphosphoric acid (PPA), epoxy, polyurethane, nano-materials, etc. Currently, there is lack of modern innovative approached in the preparation of modified asphalt towards better performance. There is no clear standardized definition of term associated with asphalt binder preparation process. Given the limited and common types of polymers utilized for the modification of asphalt binder, it is possible to standardize the mixing procedure for several polymers. Doing so could ease research continuity and facilitates accurate comparison of new studies with earlier ones.

Deep learning has received a growing interest in recent years for detecting different types of pavement distresses and automating pavement condition assessment. A proper choice of deep learning models is key for successful pavement assessment applications. In this study, we first present a comprehensive experimental comparison of state-of-the-art image classification models to evaluate their performances on 11 pavement objects classification. Our experiments are conducted in different dimensions of comparison, including deep classifier architecture, effects of network depth, and computational costs. Five convolutional neural network (CNN) classifiers widely used in transportation applications, including VGG16, VGG19, ResNet50, DenseNet121, and a generic CNN (as the control model), are tested with a comprehensive pixel-level annotated dataset for 11 different distress and non-distress classes (UCF-PAVE 2017). In addition, we investigate a simple yet effective approach of encoding contextual information with multi-scale input tiles to classify highly random pavement objects in size, shape, intensity, texture, and direction. Our comparison results show that the multi-scale approach significantly improves the classification accuracy for all compared deep classifiers at a negligible extra computational cost. Finally, we provide recommendations of how to improve the classification performance of deep CNNs for automated pavement condition assessment based on the comparison results.