https://tcsj.utc.edu.vn/index.php/tcgtvt/issue/feedTransport and Communications Science Journal2025-12-15T11:37:51+07:00Tạp chí Khoa học Giao thông vận tảitcsj@utc.edu.vnOpen Journal Systemshttps://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2770Table of contents2025-12-15T11:37:51+07:00Trần Văn Giápvpdt_1279@utc.edu.vn2025-12-15T11:37:51+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2423Evaluation on sustainable logistics efficiency in asean by data envelopment analysis (DEA)2025-12-15T11:29:24+07:00Hưng Lê Mạnhhunglm.kt@vimaru.edu.vnHà Hoàng Thị MinhSustainable logistics efficiency is the practically global issue due to the importance of logistics activities in forming national economic linkages with the sustainable tendency. ASEAN is currently experiencing impressive economic growth but challenging the uneven development of sustainable logistics performance among countries. This study focuses on comparing sustainable logistics efficiency among the 10 ASEAN countries using Sustainable Development Goal (SDG) and Logistics Performance Index (LPI). The comparison is conducted through Data Envelopment Analysis (DEA) method, utilizing secondary data from 64 observations collected from United Nations (UN) and World Bank (WB). The results indicate a significant difference in SDG scores among countries with high and low LPI scores. Notably, Singapore and Malaysia have the most potential to improve sustainable logistics efficiency, whereas Laos and Myanmar are making the most of their available national resources to achieve this. This study can serve as a foundation for national policymakers to develop logistics system planning aimed at social sustainability, particularly in ASEAN2025-12-15T00:00:00+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2309Developing software for assessing the reliability of sliding bearing joint systems based on hydrodynamic lubrication theory2025-12-15T11:29:24+07:00Hiếu Trần Vănddtuan@utc.edu.vnTuấn Đỗ ĐứcToàn Nguyễn ĐứcThe reliability of sliding bearings and their associated systems depends on multiple factors, including structural parameters, clearance gaps, load capacity, lubricant viscosity, operating temperature, and the critical thickness of the lubricant film. According to reliability theory, a bearing support system is considered a series-connected system of interdependent components. To assess the overall system reliability, it is necessary to evaluate the individual reliability of each component (joint) while considering the influence of all relevant factors. Subsequently, a comprehensive reliability assessment of the entire system can be conducted. To achieve this, it is essential to develop software that evaluates the reliability of bearing support systems based on hydrodynamic lubrication theory. Such software facilitates the diversification of computational methods, accelerates calculation speed, and enhances the accuracy of results. This tool enables the assessment of reliability for various types of sliding bearing joints, with a primary focus on the bearing support system of crankshafts in diesel locomotive engines used in the Vietnamese railway industry2025-12-15T00:00:00+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2700Finite element–informed neural network (feinn) approach to inverse problems in 1D linear elasticity2025-12-15T11:29:23+07:00Khiết Nguyễn Thanhnguyendinhdu@lhu.edu.vnPhúc Phạm MinhDư Nguyễn ĐìnhInverse analysis of material parameters plays a crucial role in solid mechanics problems, which remain challenging and computationally expensive with conventional numerical approaches such as the finite element method (FEM). This paper proposes a highly efficient inverse analysis framework for 1D problems based on the Finite Element Informed Neural Network (FEINN). Unlike Physics-Informed Neural Networks (PINNs), which solve the strong form of governing equations, FEINN addresses the weak form through finite discretization and incorporates Gaussian integration to compute the strain–displacement matrix, thereby significantly accelerating training and convergence. In the inverse problem, the unknown material parameters are inferred from the neural network’s output layer, while the nodal coordinates serve as input. Nodal displacements and forces are employed as constraints within the loss function. FEINN determines the material parameters by iteratively optimizing the neural network using the fmincon function in MATLAB. The proposed method demonstrates high efficiency through several benchmark cases involving both constant and spatially varying material parameters2025-12-15T00:00:00+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2615Optimization of double wishbone suspension geometry through kinematic simulation and finite element analysis2025-12-15T11:29:23+07:00Phước Nguyễn Hữuluphuoc76@gmail.comPhước Lư HuệĐạt Võ ThànhThe double wishbone suspension system plays an important role in ensuring vehicle dynamic stability and structural safety; therefore, optimizing the design parameters of the suspension system is essential. This paper proposes an optimization approach for the double wishbone suspension system by integrating kinematic simulation, multi-objective optimization, and finite element analysis. The primary objectives are to reduce camber angle variation, minimize the lower arm mass, and improve the structural safety factor. First, a kinematic model of the suspension system is developed in NX Motion to accurately evaluate its performance characteristics. Then, the HEEDS MDO platform with the adaptive SHERPA algorithm is employed to automatically combine various optimization strategies, enabling the identification of the most effective design solutions for the system. Finally, structural strength is analyzed in NX Simcenter 3D to validate load-bearing capacity and reliability. The results demonstrate that the camber angle variation is significantly reduced from 3.50° to 0.86°, the lower arm mass decreases by 22.67%, and the safety factor increases from 1.22 to 1.42. These findings confirm that the proposed method is highly effective and practical for automotive suspension design, especially for applications requiring enhanced stability and durability under demanding operating conditions2025-12-15T00:00:00+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2625Detection of damage in steel truss bridges using a hybrid 1DCNN–BIGRU model and time-series data augmentation techniques2025-12-15T11:29:25+07:00Đăng Nguyễn Lê Minhhxnam@utc.edu.vnNam Hồ XuânTrung Vũ MạnhBảo Nguyễn Kiều NgọcStructural health monitoring (SHM) systems based on time-series data are pivotal tools for assessing the safety and serviceability of bridges. However, the lack of large-scale and diverse datasets often leads deep learning (DL) models to overfitting and poor generalization. This study proposes a comprehensive damage detection framework that simultaneously addresses both data and model aspects. On the data side, we employ data augmentation (AUG) techniques—including noise injection, sequence reversal, flipping, and window slicing—to expand and diversify the training set. Regarding the proposed methodology, we established a hybrid deep learning framework that integrates One-dimensional Convolutional Neural Networks (1DCNN) with Bidirectional Gated Recurrent Units (BiGRU). In this architecture, the 1DCNN layers are responsible for mining local features from raw time-series inputs, while the BiGRU layers specialize in capturing long-term temporal dependencies in both directions. The efficacy of this approach was verified using the Cua Rao steel truss bridge dataset. The experimental outcomes reveal that the data-augmented hybrid model significantly outperforms standalone baselines, achieving an accuracy of 93.9%. This is a substantial improvement over the individual 1DCNN (84.2%) and BiGRU (83.8%) models, demonstrating faster convergence and high stability for SHM applications2025-12-15T00:00:00+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2624A closed-form solution to predict the buckling load of steel bars strengthened with laminates2025-12-15T11:29:24+07:00Thi Đoàn Tấnphe.phamvan@utc.edu.vnHương Cao Thị MaiPhê Phạm VănHuy Nguyễn XuânBình Nguyễn ĐứcThe investigation of elastic buckling in steel trusses strengthened with FRP sheets is of great importance for enhancing structural performance and durability, especially in the context of increasing demand for lightweight and efficient materials. This study proposes a simple closed-form solution, derived from the variational principle of structural strain energy, to evaluate the critical load and buckling mode of FRP-strengthened steel truss members. The proposed formula was verified against numerical solutions obtained from structural analysis software, demonstrating good agreement and confirming the accuracy and applicability of the analytical model. A parametric study further revealed that (i) FRP sheets with fibers oriented at 0° provided the highest buckling resistance, whereas sheets with angles equal to or greater than ± 45° had negligible influence, (ii) the buckling load increased linearly with the thickness of the FRP sheets, and (iii) FRP strengthening proved to be highly effective in improving the stability of steel trusses. These findings highlight the feasibility of the proposed approach and provide a useful scientific basis for the practical design and strengthening of steel truss structures with FRP materials2025-12-15T00:00:00+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2627Analytical approximation and numerical simulation of transport in two-dimensional Primitive surface architected materials2025-12-15T11:29:24+07:00Hải Hoàng Thị Minhviettb@utc.edu.vnTuấn Tống AnhTuấn Trần AnhAnh Trương Đình ThảoViệt Trần BảoWith the advancement of additive manufacturing technologies, architected materials are playing an increasingly important role in various practical applications. This study therefore focuses on the relationship between the geometry of two-dimensional Primitive triply periodic minimal surface (TPMS) structures and the effective transport properties of materials. The influence of the geometric parameter is investigated through its correlation with porosity (density), which serves as a key structural descriptor. A comprehensive set of numerical simulations is performed using the finite element method, in which the volume fraction of the inclusion phase varies from 0 to 1 and the conductivity contrast is extended to large values in order to capture different transport regimes. From these simulations, the effective conductivity is determined and compared with classical analytical models derived from micromechanics. The results indicate that classical formulations remain predictive, where the Hashin–Shtrikman lower bound (for cases with low reinforcement phase volume fraction) and the classical self-consistent approximation (for cases with high reinforcement phase volume fraction) exhibit good accuracy compared to other models in describing the conductivity behavior of the Primitive structure. This study contributes to a deeper understanding of the structure–property relationship in TPMS-based materials and proposes a reliable, simple, and practical analytical–numerical framework for the design of architected materials2025-12-15T00:00:00+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2664Buckling analysis of multilayer beams resting on elastic foundations2025-12-15T11:29:23+07:00Hùng Nguyễn Vũvuong.mta@gmail.comVượng Đào VănMultilayer nanoscale structures are widely employed in the fabrication of miniature sensors, which serve as essential components in measurement systems used in civil engineering, transportation, and defense applications. This paper presents an analytical solution for the stability problem of multilayer nanobeams resting on an elastic foundation, incorporating the flexoelectric effect under simultaneous thermal and moisture conditions. The governing equations are formulated based on a refined shear deformation theory combined with the von-Kármán nonlinear strain model. Moreover, small-scale effects are considered through the nonlocal elasticity theory. The reliability of the proposed analytical approach is verified by comparison with previously published results. The study also investigates the influence of several parameters-including foundation stiffness, geometric dimensions, and material properties-on the stability response of multilayer nanobeams. The results indicate that both the flexoelectric effect and the elastic foundation enhance the overall stiffness of the structure, thereby improving its load-carrying capacity under compressive loading2025-12-15T00:00:00+07:00##submission.copyrightStatement##https://tcsj.utc.edu.vn/index.php/tcgtvt/article/view/2684Assessment of rutting resistance in hot recycled asphalt mixtures using the rutting tolerance index2025-12-15T11:29:25+07:00Trang Lê Thuquyet.tv@utc.edu.vnLân Nguyễn NgọcĐông Đào VănQuyết Trương VănThủy Phạm Thị ThanhRutting and cracking resistance are two key indicators for ensuring balanced performance in asphalt mixtures. This paper introduces a new testing method, the IDEAL Shear Rutting Test (IDEAL-RT), developed to evaluate the rutting resistance of asphalt mixtures. This method has been adopted by several U.S. states because of its simplicity, short testing duration, and suitability for quality control applications. In this study, the IDEAL-RT was performed on hot recycled asphalt mixtures containing 30%RAP and 50%RAP (reclaimed asphalt pavement - RAP). The results showed that the mixture with 50%RAP exhibited higher rutting resistance than that with 30%RAP. Specifically, the 30% and 50% RAP mixtures exhibited RTIndex values that were 15.7% and 41.5% higher, respectively, than those of the control mixture (0%RAP). In addition, the use of rejuvenators was found to reduce the rutting resistance of the mixtures. The overall trends observed from the IDEAL-RT were consistent with those obtained from the conventional Hamburg Wheel Tracking Test (HWTT). Based on these findings, this study recommends the IDEAL-RT as a practical and efficient method for determining the RTIndex values to evaluate the rutting resistance under Vietnamese conditions2025-12-15T00:00:00+07:00##submission.copyrightStatement##