Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
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.
Scopus Indexed(2026)
Submission Deadline
Volume
50
, Issue
03
12 Jun 2026
Day
Hour
Min
Sec
Publish On
Volume
50
, Issue
03
30 Jun 2026
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Aim and Scopes
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: :
Electrical Engineering and Telecommunication Section:
Computer Science Section :
Civil and architectural engineering :
Mechanical and Materials Engineering :
kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.Chemical Engineering :
Food Engineering :
Physics Section:
Mathematics Section:
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Numerical Simulation Research on Influencing Factors of Underwater Multi-orifice Plate Flow Reduction Characteristics Based on Single-phase Flow Model
The paper presents the results of the experimental and numerical analysis of a six-hole orifice flow meter. The experiments were performed on humid air in a 100 mm diameter duct. The aim of this research was to investigate the mass flow and pressure drop dependency in an orifice of a predetermined shape and to compare the results obtained with computational formulas recommended in the ISO 5167-2 standard for a single-hole orifice flow meter. The experiments and calculations were performed on several multi-hole orifice geometries with different contraction coefficient in a wide range of Reynolds numbers. The pressure was probed immediately upstream and downstream of the orifice. The flow coefficient determined for the six-hole orifice flow meter investigated was compared with the flow coefficient of conventional single-hole orifice with the same contraction coefficient. The results from computational formulas for single-hole orifice from ISO 5167 are also included in the paper. During some experiments, an obstacle has been introduced in the duct at variable distance upstream from the orifice. The effect of the thus generated velocity field disturbance on the measured pressure drop was then investigated. Numerical simulation of the flow with the presence of the obstacle was also performed and compared with experimental data.
A Ship Intelligent Collision Avoidance Method Based on Deep Reinforcement Learning
Many achievements toward unmanned surface vehicles have been made using artificial intelligence theory to assist the decisions of the navigator. In particular, there has been rapid development in autonomous collision avoidance techniques that employ the intelligent algorithm of deep reinforcement learning. A novel USV collision avoidance algorithm based on deep reinforcement learning theory for real-time maneuvering is proposed. Many improvements toward the autonomous learning framework are carried out to improve the performance of USV collision avoidance, including prioritized experience replay, noisy network, double learning, and dueling architecture, which can significantly enhance the training effect. Additionally, considering the characteristics of the USV collision avoidance problem, two effective methods to enhance training efficiency are proposed. For better training, considering the international regulations for preventing collisions at sea and USV maneuverability, a complete and reliable USV collision avoidance training system is established, demonstrating an efficient learning process in complex encounter situations. A reward signal system in line with the USV characteristics is designed. Based on the Unity maritime virtual simulation platform, an abundant simulation environment for training and testing is designed. Through detailed analysis, verification, and comparison, the improved algorithm outperforms the pre-improved algorithm in terms of stability, average reward, rules learning, and collision avoidance effect, reducing 26.60% more accumulated course deviation and saving 1.13% more time.
Association and Fusion of Ship AIS and Radar Track Data
A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.
Study on Local Failure Mode of Open Pit Slope Based on DEM Flat Joint Model
With the development of society, the demand for mineral resources is gradually increasing, and the current situation of decreasing total resources dictates the inevitable interaction between open-pit combing underground extraction (OPUG) in time and space. In this research, we took the Anjialing coal mine in Shanxi Province of China as a case study, and tested the physical and mechanical properties of coal rocks in the laboratory. The similarity criterion was used to build a similar experimental model for the deformation evolution of the slope of the open-pit mine section; the digital scattering method was used to test the influence of the underground mining process parameters on the deformation evolution of the open-pit slope. The results showed that there was an obvious distribution of “three zones” above the mining goaf, namely, a collapse zone, fracture zone, and slow subsidence zone. When the mining face was continuously advanced towards the bottom of the open pit, the supporting stress of the mining face transferred to the side of the open-pit slope. Additionally, large displacement and stress concentration were observed on the slope near the stoping line, which caused the slope body to move along the uppermost part of the slope first, and thereafter along the lower part. Various techniques for slope stability control are discussed, including the optimization of spatial and temporal relationships between open-pit and underground mining, the optimization of mining plans, and the use of monitoring and early warning systems. The results can provide a guide for slope stability control of similar open-pit mines in the process of mining coal resources.
Fatigue Vulnerability Analysis of Crossbeam and Chord Joint in Steel Truss Bridge
The K-type joint, which consists of the web members and the chord members with varied angles welded together, has been widely adopted in long-span steel truss bridges. However, its fatigue performance has been rarely considered, despite its critical role in bridge structural safety and durability. Accordingly, the FE model of the K-type joint was established in Abaqus and the fatigue performance analysis was conducted, in which the effect of web/chord thickness ratio (τ), chord/web angle (θ), and chord with rib stiffener were investigated. Take the Mingzhu Bay steel truss arch bridge as an engineering background, the hot spot stress method was employed to calculate the fatigue performance of three K-type joints in unfavorable locations. Furthermore, a 3D full-scall bridge model was built to evaluate the fatigue performance of the K-type joints under standard and overloaded moving vehicle load scenarios. The results show that the max hot spot stress factor (SCFmax) of the web and chord member is influenced by τ and θ. The chord members added stiffener is founded to be an effective way to enhance fatigue performance. The fatigue stress intensities of the three unfavorable locations meet the Eurocode 3 specification requirements, but the one in the mid-truss arch is not satisfied under an overloaded vehicle loading rate of 25%.
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/Journal of Wuhan University of Technology (Transportation Science and Engineering)