Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) was originally founded in 1959. The publisher of the journal is Wuhan University of Technology. JWUT first got the scopus license in the year 2001. The journal generally publishes all aspect of engineering sciences like: physics, chemistry, mathematics, and all sorts of general engineering.
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) (ISSN:2095-3844) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to: :
Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.
Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case.
In recent years, the frequent occurrence of rainstorms has seriously affected urban–public transport systems. In this study, we examined the impact of rainstorms on the vulnerability of urban–public transport systems consisting of both ground bus and metro systems, which was abstracted into an undirected weighted Bus–Metro complex bilayer network (Bus–Metro CBN) and the passenger volume was regarded as its weight. Through the changes in the node scale, network efficiency, and passenger volume in the maximal connected component of the Bus–Metro CBN, we constructed a vulnerability operator to quantitatively calculate the vulnerability of the Bus–Metro CBN. Then, the flow-based couple map lattices (CMLs) model was proposed to simulate cascading failure scenarios of the Bus–Metro CBN under rainstorm conditions, in which the rainstorm is introduced through a perturbation variable. The simulation results show that under the condition of passenger flow overload, the network may have a two-stage cascading failure process. The impact analysis shows that there is a rainstorm intensity threshold that causes the Bus–Metro CBN to collapse. Meanwhile, we obtained the optimal node and edge capacity through capacity analysis. In addition, our analysis implies that the vulnerability of the Bus–Metro CBN network in most scenarios is mainly caused by the degradation of network structure rather than the loss of passenger flow. The network coupling strength analysis results show that the node coupling strength has greater potential to reduce the vulnerability than edge coupling strength. This indicates that traffic managers should prioritize controlling the mutual influence between bus stops (or metro stations) to reduce the vulnerability of the Bus–Metro CBN more effectively.
Due to the strong interface effect of continuous steel–concrete composite beams with conventional shear connectors, the efficiency of applying pre-stress in the negative moment zone is greatly reduced, which leads to a difficulty of anti-cracking design in the negative moment zone of pre-stressed steel–concrete composite box girder. In order to study the feasibility and the working mechanism of improving the crack resistance of continuous steel–concrete composite bridges by releasing the interfacial slip effect within the negative bending moment region, two groups of model tests were carried out in the paper. Two steel–concrete composite beams were used for model test, one of them using the conventional stud shear connectors, another one using the new shear connectors, named uplift-restricted and slip-permitted shear connectors. The results show that, compared with the composite beam with conventional shear studs, the composite beams with uplift-restricted and slip-permitted shear connectors have a higher pre-stress application efficiency, and the new connector could release the interface slip, which can make the tensile stress distribution in concrete slab more uniform within the negative moment zone, thus increasing the cracking load of concrete slab and reducing the subsequent crack width effectively. This study is helpful to understand the relationship between the interface slip and the anti-crack characteristics in negative moment zones, and a new anti-crack design method is proposed for the design of continuous composite girder.
This review aims to explore the state of the knowledge and the state-of-the-art regarding bitumen rejuvenation. In particular, attention was paid to clear things up about the rejuvenator mechanism of action. Frequently, the terms rejuvenator and flux oil, or oil (i.e., softening agent) are used as if they were synonymous. According to our knowledge, these two terms refer to substances producing different modifications to the aged bitumen: they can decrease the viscosity (softening agents), or, in addition to this, restore the original microstructure (real rejuvenators). In order to deal with the argument in its entirety, the bitumen is investigated in terms of chemical structure and microstructural features. Proper investigating tools are, therefore, needed to distinguish the different mechanisms of action of the various types of bitumen, so attention is focused on recent research and the use of different investigation techniques to distinguish between various additives. Methods based on organic synthesis can also be used to prepare ad-hoc rejuvenating molecules with higher performances. The interplay of chemical interaction, structural changes and overall effect of the additive is then presented in terms of the modern concepts of complex systems, which furnishes valid arguments to suggest X-ray scattering and Nuclear Magnetic Resonance relaxometry experiments as vanguard and forefront tools to study bitumen. Far from being a standard review, this work represents a critical analysis of the state-of-the-art taking into account for the molecular basis at the origin of the observed behavior. Furnishing a novel viewpoint for the study of bitumen based on the concepts of the complex systems in physics, it constitutes a novel approach for the study of these systems.
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