Transport and Communications Science Journal
Tạp chí của Trường Đại học Giao thông vận tải
Dynamic stability analysis of functionally graded plate with variable thickness
Understanding the dynamic stability behaviour of functionally graded microstructures is of great importance for the development of advanced micro-electromechanical systems, aerospace components, and high-performance smart structures. Accurate prediction of instability regions and vibration characteristics can significantly improve structural reliability, safety, and lightweight design efficiency in modern engineering applications. This study presents an analytical method for analysing the dynamic stability of FG variable thickness microplate based on Mindlin plate theory, modified couple stress theory and the Bolotin method. The microplate thickness is assumed to vary along the length and width with a non-linear variation. The isogeometric analysis method is used to derive the pulse frequency and dynamic stability region of the plate under different boundary conditions. The accuracy of the model and calculation method is verified through numerical comparison with reliable publications. A set of numerical results is collected to evaluate the influence of input parameters; these results are crucial for the optimal calculation and design of variable thickness structures in practice.
Forced vibration analysis of variable thickness microplate
Nowadays, with the development of material science, nano- or microstructures are increasingly of interest in research because of their applications in micro-electromechanical technology, semiconductor chips, or sensors. In this study, the isogeometric approach (IGA) method combining the Mindlin plate hypothesis and the modified couple stress hypothesis is used to analyse the forced vibration of a micro-plate with variable thickness made of materials with variable mechanical characteristics subjected to the effect of different types of dynamic loads. The thickness change of the microplate is considered in two directions with a nonlinear law, while the material characteristics vary with the plate thickness. The model's and method's accuracy are validated by comparing it with results from published studies. Then, the paper surveys the impact of geometric and material coefficient on the transient responses of FG microplate. The research results provide important bases in the design and optimization calculation of micro-electromechanical systems serving the chip and sensor industries.
Experimental evaluation of early-age thermal behavior of fly ash concrete using semi-adiabatic calorimetry
Early-age temperature development of concrete is governed by the balance between hydration heat generation and heat dissipation to the surrounding environment. Adiabatic calorimetry is commonly regarded as the most accurate experimental method for determining hydration-related thermal parameters of concrete; however, it requires complex equipment and is relatively costly. In contrast, semi-adiabatic calorimetry offers a simpler and more economical alternative. This study experimentally investigates the semi-adiabatic temperature development of concrete mixtures with different fly ash replacement levels. Four mixtures containing 0%, 10%, 20%, and 30% fly ash by mass of cementitious materials were tested using a laboratory calorimeter operated under semi-adiabatic mode. Concrete and chamber air temperatures were continuously monitored, and key thermal indicators were evaluated. The results show that increasing fly ash replacement reduces both the peak temperature and the overall temperature rise under consistent testing conditions. The temperature rise (ΔT) decreased from 23.9 °C to 17.5 °C as fly ash replacement increased from 0% to 30%. The reduction in temperature rise does not follow a strictly linear trend, and no clear monotonic relationship was observed for the time to peak temperature. Semi-adiabatic calorimetry is shown to provide reliable comparative indicators for preliminary assessment of early-age thermal behavior of concrete mixtures.
Urban traffic flow prediction and traffic state identification using catboost with shap based analysis
Traffic flow forecasting is a critical component in intelligent transportation systems, supporting traffic management, reducing congestion, and improving the operational efficiency of urban road networks. However, this is a challenging problem due to the temporal variability of traffic data and the influence of numerous complex factors. In this study, we propose a traffic flow forecasting method based on the CatBoost algorithm to effectively exploit tabular traffic data collected from traffic sensors. The dataset consists of 2,976 records containing temporal information and vehicle counts across four categories (cars, motorcycles, buses, and trucks). In addition to the original features, the study constructs supplementary temporal and time-series features, including Hour, DayOfWeek, IsWeekend, Total_lag1, and Total_roll3, to enhance the model's ability to capture traffic flow variation trends. Based on this, two independent machine learning tasks are established: (i) total traffic flow forecasting as a regression problem, and (ii) traffic condition classification into four levels. Experimental results demonstrate that the proposed model achieves strong predictive performance. Furthermore, feature importance analysis using the SHAP method reveals that vehicle count-related variables, particularly CarCount and BusCount, have a significant impact on prediction outcomes. The study demonstrates that CatBoost is an effective approach for traffic flow forecasting with tabular data and holds strong potential for application in intelligent traffic management systems.
Study on intention to switch to electric vehicles in the Low-Emission zone in Hanoi
Air pollution is one of the most critical environmental challenges worldwide, encouraging governments to promote electric motorcycles (EMs) and implement Low-Emission Zones (LEZs) as key instruments for achieving sustainable urban mobility. In Vietnam, especially in large cities such as Hanoi, emerging policies restricting gasoline-powered motorcycles are expected to significantly influence individual travel behavior. This study investigates the factors affecting individuals’ intention to switch from conventional gasoline motorcycles to EMs in Hanoi under the context of these policy changes. The analysis is based on primary survey data collected from 409 respondents. Exploratory Factor Analysis (EFA), multiple regression analysis, and ANOVA are employed to examine the impacts of psychological, socio-demographic, and mobility-related factors on EM adoption intention. The findings reveal that psychological factors play a prominent role, besides socio-demographic characteristics, in shaping switching intention. Perceived value is identified as the strongest positive determinant, followed by environmental concern, while perceived risk negatively influences intention. The results provide valuable insights for policymakers in designing effective measures to promote electric motorcycle adoption and support sustainable transport transitions in urban Vietnam.
The impact of debt structure on financial performance: evidence from listed transportation firms
The transportation sector is a key pillar of economic activity, both supporting broader development and remaining highly responsive to shifts in the macroeconomic environment and the pressures associated with deeper international integration. Examining the relationship between debt structure and financial performance in transportation firms is essential in understanding the challenges and guiding financial management in the context of an emerging economy. This study focuses on analyzing the impact of key indicators related to debt structure, including the ratio of short-term debt to total debt (SDR), the ratio of long-term debt to total debt (LDR), and the total debt to total assets ratio (TDR), on financial performance metrics such as earnings per share (EPS), return on equity, and return on assets. The research data was collected from 99 listed transportation firms between 2019 and 2023 and analyzed using fuzzy-set Qualitative Comparative Analysis. The findings indicate that higher levels of SDR are associated with configurations linked to stronger financial performance, suggesting that the use of short-term debt may be conducive to improved performance in the short run. However, the simultaneous presence of LDR and TDR shows varying impacts, indicating that when the ratio of long-term debt or total debt exceeds a reasonable threshold, it can lead to increased financial costs and risks, ultimately degrading financial performance. Therefore, this study offers practical implications for developing appropriate debt management strategies to ensure sustainable growth and financial stability for Vietnamese listed transportation firms.
Induced inner product structures and Cauchy-Schwarz inequalities for linear functionals
Linear functionals on finite-dimensional polynomial spaces generate fundamental algebraic and analytic structures, including moment sequences, moment matrices, and functional inequalities. Associated with a linear functional on polynomials of bounded degree is a moment matrix whose entries are given by the values of the functional on products of monomials and naturally exhibit a Hankel structure. Adopting an intrinsic functional-analytic viewpoint, this paper studies linear functionals on polynomial spaces without invoking any external representation framework. We develop a unified algebraic setting in which linearity, positivity-type conditions, moment matrices, and a functional inequality are examined simultaneously. We distinguish properties arising purely from linearity from those requiring additional structural assumptions. In particular, we establish a Cauchy-type inequality for linear functionals under mild algebraic conditions, independent of any a priori inner product structure. Under stronger positivity assumptions, the linear functional induces an inner product on polynomial spaces, with the associated moment matrix reflecting this structure precisely. Moreover, the Hankel structure of moment matrices is clarified as an intrinsic consequence of polynomial multiplication.
Enhancing crack segmentation in fused RGB-IR images with CSWin transformer and semantic feature pyramid network
Surface crack segmentation is a critical task in structural health monitoring (SHM), serving as an early indicator of structural deterioration and safety risks. Recently, deep learning-based computer vision has emerged as a dominant approach for automating defect detection, gradually replacing manual inspections. However, traditional convolutional neural networks (CNNs) often struggle to capture long-range dependencies. To address this limitation, this paper introduces the CSWin-Semantic FPN, a model integrating the transformer architecture with cross-shaped window (CSWin) attention and a semantic feature pyramid network (Semantic FPN) to optimize feature extraction. Notably, this study utilizes a multi-modal fusion dataset-combining optical and thermal infrared images collected in real-world environments. This fusion approach significantly enhances crack signals against complex backgrounds, facilitating more effective model training. Experimental results demonstrate that the CSWin-Semantic FPN achieves an impressive intersection over union (IoU) of 70.53%, significantly outperforming ResUNet (59.44%), SwinUNet (57.91%), and UNet (51.79%). These findings confirm the potential of hybrid Transformer architectures combined with multi-modal data in providing reliable and automated SHM solutions.
HIT4DAR: Holistic interaction transformer for driver action recognition
Traffic accidents remain a critical issue worldwide, especially in the context of the rapidly growing number of vehicles on the road. This leads to the need for an automatic system to recognize distracted driver actions and provide early warnings to enhance road safety. In fact, driver action recognition (DAR) can be considered as a subfield of human action recognition (HAR). In addition to the common challenges of HAR, DAR must address additional difficulties, including fine-grained and subtle hand movements, self-occlusion, and complex interactions with multiple objects. To overcome these challenges, we leverage Holistic Interaction Transformer (HIT) network, originally designed for HAR, to recognize driver activities from video sequence. The proposed method named HIT4DAR (Holistic Interaction Transformer for Driver Action Recognition). Some experiments are conducted on UTCDriverAct to show the effectiveness of HIT network in DAR task. The overall performance across the six actions of interest achieves a Video-mAP of 47.8%. In particular, the recognition accuracy for Texting activity reaches 78.9%. Furthermore, an ablation study is performed to investigate the influence of pose estimation models on recognition accuracy. The experimental results indicate the trade-off between recognition accuracy and computational efficiency in different pose estimation models. This comprehensive analysis provides useful recommendations for the research community when deploying the proposed framework in real-world DAR systems.
Vibration-based damage detection in cable-stayed bridges using a novel 1D-ConvNeXt-LSTM network
Structural health monitoring (SHM) plays a crucial role in maintaining the safety and serviceability of civil infrastructure, such as cable-stayed bridges. However, simultaneously extracting detailed local signal features and long-range temporal dependencies from vibration data remains a significant challenge for standalone deep learning architectures like LSTM or ResNet1D. To address this limitation, this study proposes an advanced hybrid architecture, 1D-ConvNeXt-LSTM, for structural damage detection. This framework integrates the multi-scale feature extraction capabilities of 1D-ConvNeXt with the sequential modeling proficiency of Long Short-Term Memory (LSTM) networks. The proposed method was evaluated using vibration time-series data acquired from a scaled cable-stayed bridge model under five distinct simulated damage scenarios. Experimental results demonstrate that the 1D-ConvNeXt-LSTM model achieves superior classification performance, yielding a macro–Area Under the Curve (AUC) of 0.971 and a macro F1-score of 0.814, significantly outperforming baseline architectures including FCN, ResNet1D, and LSTM. Ultimately, the proposed architecture provides a robust, stable, and highly accurate solution for structural condition assessment, thereby enhancing the effectiveness of damage identification in practical SHM applications.
Enhancing the operational efficiency of public transport through automated fare collection systems
Public transport systems in major urban areas of Vietnam have experienced significant growth in recent years, however, the application of information technology in their management and operation remains limited. In cities such as Hanoi and Ho Chi Minh City, fare collection on bus networks is still predominantly conducted manually, with ticket counting and data recording also performed using traditional methods. This approach not only leads to high labor costs but also reduces efficiency in data management and service planning. These limitations hinder the implementation of advanced operational and policy mechanisms, including integrated fare systems, direct passenger-based subsidy schemes, and data-driven service planning that can adapt to fluctuations in travel demand. This study examines the technical, technological, and socio-economic prerequisites for implementing automated fare collection (AFC) systems in Vietnam. Based on this analysis, it proposes a set of solutions aimed at enhancing the operational efficiency and overall performance of urban public transport through the automation and digitalization of fare collection processes.
Geometrically nonlinear response of axially functionally graded beams subjected to a moving load
Understanding the dynamic response of the beam under the actions of a moving load is crucial for practical applications. In this study, nonlinear dynamic characteristics of an axially functionally graded (AFG) beam subjected to a moving load has been performed by using the trigonometric shear deformation beam theory and the von-Kármán geometric nonlinearity. The Voigt model is used to calculate the material properties of the beam. The system of nonlinear differential equation of motion for the beam is derived by using Hamilton’s principle. The finite element formula based on the Lagrange and Hermite interpolation functions is employed to discretize the model and obtain a numerical approximation of the system of differential equation of motion in nonlinear analysis. The Newmark method together with the Newton-Raphson iteration method is adopted to solved these equations. To validate the present work, the results in this paper are compared with those of the existing literature and good agreement is achieved. The results show that the power-law index, velocity and moving load magnitude and aspect ratio play a very important role on the beam’s nonlinear response.
Application of fuzzy logic in collision avoidance control of multiple swarm robots
In nature, many swarms of organisms may encounter one another while moving in search of food, which can lead to conflicts between groups. In the field of transportation, there are flows of unmanned vehicles traveling from different directions on the roads; when they reach intersections, the challenge is how these vehicles can pass one another without collisions and then continue toward their destinations as originally planned. Groups of drones fly in the sky, controlled to form various shapes. So how do these drone swarms move past each other without colliding while forming the desired shape? This paper proposes a solution based on the use of Takagi–Sugeno and Mamdani SISO fuzzy structures to compute internal forces within a swarm, target-directed forces, and forces for avoiding other swarms. The proposals above will be validated through MATLAB simulations under different scenarios involving varying numbers of swarms and different numbers of individuals in each swarm.
Short-term mechanical properties of geopolymer concrete incorporating sea sand
The depletion of natural river sand and the environmental effect of Portland cement production have motivated the search for green building materials. This study investigates the short-term mechanical properties of geopolymer concrete (GPC) incorporating sea sand as fine aggregate. Class F fly ash and ground granulated blast furnace slag were employed as aluminosilicate precursors, activated by a dry sodium silicate-based powder. Nine mixtures were designed by varying activator content (8%, 11.5%, and 15% by binder mass) and water-to-binder (W/B) ratios (0.30, 0.35, and 0.40). A total of 108 cylindrical specimens were tested for compressive strength at the age of 3, 7, 14, and 28 days. Flexural tensile strength, splitting tensile strength, and elastic modulus were evaluated at 14 days for the optimal mixture. Results indicate rapid early-age strength development, with 3-day strength reaching 50–60% of 28-day strength. Increasing activator content significantly improved compressive strength, while increasing W/B ratio reduced strength. The 28-day compressive strength reached 44.8 MPa under ambient curing. Flexural and splitting tensile strengths exceeded predictions from conventional concrete design models. The findings preliminary demonstrate that sea sand can be effectively utilized in geopolymer concrete providing a sustainable option for construction in coastal regions.
Analysis of slope stability under dynamic vehicle loads induced by rough flexible pavements
Uneven pavement surfaces increase the dynamic loads generated by moving vehicles. However, stability analyses of road embankments often neglect the influence of vehicle vibrations on slope stability. Therefore, considering the interaction between vehicles, pavement conditions, and slope response provides a more realistic evaluation of embankment stability. In this study, vehicle dynamics are simulated using a quarter-car model (QCM), pavement roughness is represented by the International Roughness Index (IRI), and slope stability is evaluated using the Fellenius method. Numerical simulations were conducted for an 8-m-high, 1:1 cut slope in homogeneous saturated cohesive soil under vehicle speeds of 20, 40, and 60 km/h. Four pavement roughness levels with IRI values of 2, 3, 5, and 7 were considered. Dynamic wheel loads for car and truck models were simulated in MATLAB-Simulink over 150 s using the Runge-Kutta integration method. The results show that the static factor of safety (FoS) is 2.05 for the car and 2.03 for the truck. Under dynamic loading with IRI=7, the FoS decreases to 1.96-2.00 for cars and 1.92-1.96 for trucks, depending on vehicle speed. The findings indicate that increasing pavement roughness and vehicle loads reduce slope stability and should be considered in road embankment design and assessment.
Evaluation of creep–recovery behavior of TPS-modified asphalt binders
Due to increasing traffic loads and high pavement temperatures, improving the rutting resistance and durability of asphalt pavements has become a critical issue in road engineering. Conventional asphalt binders often show inadequate resistance to permanent deformation under severe loading and temperature conditions. This study investigates the effect of TAFPACK-Super (TPS) modification on the high-temperature performance of asphalt binders using Multiple Stress Creep Recovery test. Tests were conducted at stress levels of 0.1 and 3.2 kPa over temperatures ranging from 52°C to 88°C. Asphalt binders containing different TPS contents (6%, 13.6%, and 18%) were compared with base asphalt and polymer-modified asphalt. Non-recoverable creep compliance (Jnr) and percent recovery were analyzed to evaluate rutting resistance and elastic response. The results show that TPS significantly enhances deformation resistance by reducing Jnr values and increasing recovery rates. Temperature mainly affects Jnr, while TPS dosage strongly influences elastic recovery behavior. Overall, TPS modification improves the rheological properties of asphalt binders by forming a stable polymer network, resulting in lower temperature sensitivity and better long-term pavement performance under heavy traffic conditions.
Effect of RAP content on the performance of warm mix stone matrix asphalt
The incorporation of reclaimed asphalt pavement (RAP) into warm mix stone matrix asphalt (WSMA) has attracted increasing attention due to its potential to reduce natural resource consumption, energy demand, and environmental impacts associated with asphalt pavement construction. However, the use of RAP in SMA mixtures may significantly influence the balance between rutting resistance and cracking performance because of the stiffening effect of aged binder. Therefore, this study investigates the influence of RAP content on the mechanical performance of WSMA mixtures. Four mixtures containing 0%, 10%, 20%, and 30% RAP were designed using a warm mix additive, a bio-based rejuvenator, and cellulose fiber. The performance of the mixtures was evaluated through IDEAL-RT and IDEAL-CT tests to characterize rutting and cracking resistance, respectively. The results indicated that increasing RAP content significantly improved rutting resistance, as reflected by higher shear strength and RTIndex values. However, the cracking resistance gradually decreased with increasing RAP content due to the increased stiffness and brittleness of the aged binder system. Among the investigated mixtures, the WSMA containing 20% RAP exhibited the most balanced mechanical performance, providing substantial rutting improvement while maintaining acceptable cracking resistance.
Gaussian-based data augmentation for improved prediction of axial capacity of UHPC-jacketed rectangular RC columns
Accurate prediction of the axial load-carrying capacity of reinforced concrete (RC) columns strengthened with ultra-high-performance concrete (UHPC) jackets is essential for reliable structural design. However, experimental data for UHPC-jacketed RC columns are scarce due to high costs and complex testing procedures, limiting the generalization of data-driven models. This study proposes a Gaussian-based data augmentation framework to enhance the predictive performance of machine learning models for estimating the axial capacity of UHPC-jacketed rectangular RC columns. An experimental database compiled from the literature is statistically analyzed, and Gaussian distribution-based techniques are employed to generate synthetic samples while preserving the statistical characteristics of the original data. Several machine learning models are developed and evaluated, with testing performed exclusively on the original experimental dataset to ensure unbiased generalization assessment. The results show that the best-performing model, CatBoost, exhibits poor generalization when trained solely on experimental data, achieving a test R² of 0.526 with MAE = 328.1 kN, MAPE = 33.5%, and RMSE = 540.9 kN. After Gaussian-based data augmentation, CatBoost performance improves substantially, reaching a test R² of 0.943, with MAE = 145.51 kN, MAPE = 10.5%, and RMSE = 222.1 kN. These results confirm that Gaussian-based data augmentation significantly enhances prediction accuracy, robustness, and generalization. The proposed framework offers a practical solution for mitigating data scarcity and supports reliable design and assessment of UHPC-strengthened RC columns.
Reliability-based multi-objective optimization of laminated plates using GDE3 and isogeometric analysis
Laminated composite plates are widely used in lightweight structures owing to their high stiffness-to-weight ratio and flexible design capability. However, uncertainties in loads, material properties, and design parameters may significantly affect their mechanical performance and are often not fully addressed in deterministic optimization. This study presents a reliability-based multi-objective optimization framework for the bending design of laminated composite plates under uncertainty. The structural response is evaluated using Isogeometric Analysis combined with the First-order Shear Deformation Theory, allowing geometry and transverse shear deformation to be represented in a unified numerical model. Ply thicknesses are design variables, while maximum transverse deflection and structural mass are minimized as conflicting objectives. The First-order Reliability Method is used to evaluate the reliability index, and the Generalized Differential Evolution 3 algorithm is used to obtain Pareto-optimal solutions. Numerical results show that uncertainties affect the feasible design region and the trade-off between mass, deflection, and reliability. The framework supports reliability-informed design of lightweight composite plate structures.
Phase-field based free vibration analysis of cracked porous composite plates with parabolically varying thickness on elastic foundations
Composite plates with cracks, variable thickness, and porous structures are increasingly used in modern engineering applications due to their superior performance under complex operating conditions. However, the combined effects of porosity, variable thickness, cracks, and elastic foundation on the free vibration behavior of such plates are still not fully understood. This study investigates the free vibration characteristics of a porous functionally graded plate with parabolically varying thickness along the x-axis. A crack is assumed to be located either at the center or at the edge of the plate. The material porosity is considered to be uniformly distributed through the plate thickness. The governing equations are established based on the first-order shear deformation theory in combination with the phase-field approach to simulate the crack behavior. The finite element method is employed to solve the resulting eigenvalue problem and obtain the natural frequencies of the plate. Parametric studies are carried out to examine the influences of crack length, crack position, porosity coefficient, thickness variation, and elastic foundation stiffness on the vibration response. The numerical results demonstrate that the crack length and crack location have a significant effect on the natural frequencies, while the presence of a stiff elastic foundation remarkably reduces the sensitivity of the vibration characteristics to cracking effects.
Full-scale experimental assessment of concrete-filled steel pipe piles subjected to special lateral loads in MSPPP structures
Concrete-filled steel pipe piles have been increasingly used in Mono Steel Pipe Pile Pier (MSPPP) structures because of their high strength, ductility, and construction efficiency. Nevertheless, experimental data regarding their resistance to special lateral loads, particularly those induced by vehicular collision, are still limited. This paper presents a full-scale experimental study on the lateral load-carrying capacity of a concrete-filled steel pipe pile representative of an MSPPP configuration. A full-scale static lateral loading test was performed to investigate the pile response under a monotonically increasing horizontal load applied at the pier level. The loading scheme was intended to simulate an equivalent static condition corresponding to vehicular collision actions specified in TCVN 11823:2017. The experimental results indicate that the tested pile sustained a maximum lateral load of 1,275 kN, which is significantly higher than the equivalent static impact load of 720 kN. The pile showed stable flexural behaviour with substantial lateral deformation capacity, without any evidence of local buckling, material fracture, or abrupt loss of load-carrying capacity. These results demonstrate that full-scale static lateral loading tests constitute a practical and reliable method for verifying the resistance of MSPPP piles subjected to special lateral loads and provide useful experimental evidence for structural design and verification
Self-supervised deep learning for gnss time series imputation: a comparative study of neural network architectures
Global Navigation Satellite System (GNSS) time series are widely used for structural health monitoring (SHM) and deformation analysis, but real-world recordings frequently contain short and long contiguous gaps that degrade downstream interpretation. This study addresses the challenge of accurate imputation for GNSS displacement series by proposing a self-supervised learning framework that trains models directly on real, unlabelled data using contiguous-span masking. We evaluate four neural architectures (ANN, CNN, GRU, LSTM) under a unified pipeline comprising signal denoising (moving-average, Kalman smoothing, Haar wavelet), sliding-window segmentation, Z-score normalization, and middle-region masking. Experiments use a year-long 10-minute-sampled dataset from the Can Tho cable-stayed bridge (sensor can519501, x/y/z components) and assess reconstruction quality via R², MAE, and MSE on withheld masked segments. Results indicate that recurrent architectures, particularly LSTM, produce the most faithful reconstructions: LSTM attains the highest validation R² (≈0.948) and the lowest MAE (≈0.137) and MSE (≈0.052) among tested models, while GRU offers competitive performance and CNN/ANN show substantially weaker recovery. These findings demonstrate that masking-based self-supervision is an effective strategy for GNSS gap recovery and that LSTM-like sequence models are well suited to capture the long-range temporal dependencies in bridge displacement data. The proposed approach enhances the reliability and continuity of GNSS-derived time series for structural monitoring and can inform future multi-sensor fusion and uncertainty quantification work
Elevation-based bias correction of chirps and GSMAP rainfall in northwest vietnam (2000–2024): application to climate-resilient transport infrastructure management
Reliable rainfall data are essential for hydrological modelling and infrastructure planning, particularly in mountainous regions where gauge networks are sparse and climate risks are growing. Satellite rainfall products such as Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and Global Satellite Mapping of Precipitation (GSMaP) have been increasingly applied worldwide, yet their performance in complex terrains remains uncertain. This study evaluates CHIRPS and GSMaP against daily observations from 11 meteorological stations in Northwest Vietnam during 2000–2024 and applies an elevation-based bias correction. Statistical analyses were used to compare accuracy, including RMSE and correlation, and performance maps were generated to reveal spatial error patterns. The results show that CHIRPS achieved lower RMSE but tended to underestimate rainfall at high-elevation stations, while GSMaP yielded slightly higher correlations (R² ≈ 0.74–0.81) but often overestimated rainfall in mid-elevation valleys. Elevation-based correction reduced RMSE, narrowed bias spread across the network, and produced notable improvements at upland sites. These corrected datasets enhance reliability for hydrological simulations and flood risk assessment, especially along National Highway 6, where landslides and flash floods frequently disrupt traffic. Overall, the findings demonstrate the practical value of integrating satellite rainfall with ground observations to supplement precipitation information in ungauged regions, supporting safer and climate-resilient transport infrastructure in mountainous areas
Numerical analysis and comparison of dowel load transfer models in jointed plain concrete pavements using the finite element method
Jointed Plain Concrete Pavements (JPCPs) are divided by transverse and longitudinal joints, with dowel bars commonly embedded across transverse joints to ensure adequate load transfer efficiency (LTE) between adjacent slabs. However, joints are often considered the primary source of long-term structural deterioration. Thus, accurate modeling of joint behavior is essential for comprehensive analysis of the structural response of JPCPs systems. This study investigates LTE performance using three-dimensional finite element models developed in ABAQUS, employing three dowel–concrete interaction approaches: (1) surface contact with Coulomb friction; (2) embedded constraint method; and (3) spring connection. All three models produced consistent LTE values and closely matched field measurements. However, notable differences were identified in terms of modeling complexity, mesh density, numerical stability, and the ability to capture local stress around the dowel bars. These distinctions underscore the importance of aligning the modeling approach with the specific objectives of the analysis. The frictional contact model allows for detailed stress evaluation at the dowel–concrete interface but requires greater computational resources and modeling effort. In contrast, the embedded constraint and spring connection methods are better suited for large-scale simulations that focus on overall slab deformation, where local stress analysis at the dowel interface is not required
Study on dynamic load factor for narrow gauge railways. application on Vietnam’s north – south line
In high-speed railways, dynamic load factor has been received many studies recently. This work aims to determine the dynamic load factors of the traditional railway (1000 mm gauge) of North-South railway line in Vietnam. The objective of this research is to determine the dynamic load factors of the track based on the D19E locomotive with an axle load of 13.5 tons, rail P43, and ballast track. Simulation and experimental methods are implemented in the research. The simulation method was performed by SIMPACK software. The experimental method was carried out on a section of the North - South railway line in Vietnam. Based on the obtained simulation results, the dynamic load factors are 1.046, 1.110, and 1.361 corresponding to the speeds V=15km/h, V=30km/h, and V=70km/h. According to the experiment results, the dynamic load factors are 1.113, 1.134, and 1.181 corresponding to the speeds V=15km/h, V=30km/h, and V=70km/h. The results will be a tool for the track design engineer to implement correct design activities, and the design process will be safe and economical. Additionally, the SIMPACK software can be used to determine the dynamic coefficient for railways instead of experimental methods
Nonlinear buckling and postbuckling analysis of FG-GRMMC cylindrical panels with oblique and orthogonal stiffeners in thermal environment
Advanced composite materials, with their superior strength-to-weight ratio, durability, and thermal resistance, are increasingly applied in civil engineering for withstanding harsh mechanical and environmental loads. This paper presents a nonlinear stability analysis of cylindrical panels made from a new type of composite material, namely, functionally graded graphene-reinforced metal matrix composite (FG-GRMMC). The panels are stiffened by oblique or orthogonal FG-GRMMC stiffeners. The fundamental formulation is developed using higher-order shear deformation theory (HSDT), incorporating von Kármán geometric nonlinearity. The effects of stiffeners are modeled using an enhanced smeared stiffener technique for both mechanical and thermal effects through coordinate transformation. The governing equations are derived and solved using the Ritz energy method. Different graphene distribution patterns and stiffener orientations are systematically investigated to evaluate their effects on critical buckling and postbuckling responses. The results highlight the significant improvements in structural stability achieved through the use of oblique stiffeners and optimized material, particularly under thermal loading conditions
Determinants to blockchain technology adoption: a survey in Vietnam construction enterprises
In recent years, blockchain technology has demonstrated significant potential benefits for the construction industry, yet its adoption remains limited. This study investigates the determinants influencing blockchain adoption within construction organizations in Vietnam. The research combined Technology Acceptance Model (TAM) and Technology–Organization–Environment (TOE) framework to identify and organize determinants into latent constructs including technology, organization and environment dimensions. A questionnaire was developed to collect data from 122 construction practitioners, the model was then empirically tested and validated through using structural equation modeling (SEM). The results indicate that technological factors significantly affect users' perceived usefulness and perceived ease of use regarding blockchain technology. In contrast, organizational and environmental factors do exhibit no significant impact on perceived ease of use. Moreover, perceived usefulness emerges as a critical determinant of user acceptance of blockchain technology. Collectively, these insights contribute to valuable data to develop a roadmap for promoting blockchain implementation in the Vietnamese construction industry
Evaluation of dowel bar diameter and spacing effects on the stress and deflection of airfield rigid pavements using the finite element method
Mitigating the effects of thermal stress and shrinkage in airfield concrete pavements typically involves dividing the pavement into individual slabs of appropriate dimensions. Under aircraft wheel loading, the slab edges and corners are subjected to the most critical and unfavorable stress states. To reduce these disadvantages, dowel bars must be placed along both the longitudinal and transverse joints to transfer part of the applied load from the loaded slab to the adjacent one. Improper selection of dowel diameter or spacing can significantly diminish the pavement’s load-bearing capacity and shorten its service life. In this study, the finite element software ABAQUS was used to examine the influence of dowel bar diameter and spacing on the stress and deflection responses of airfield concrete slabs subjected to B737-500 aircraft loading. The numerical model considered two commonly used slab thicknesses: 0.30 m and 0.40 m. The results indicate that, for a constant dowel diameter, the deflection and stress increase sharply as dowel spacing increases from 0.1 m to 0.4 m, while changes become negligible beyond 0.40 m. Conversely, when spacing is kept constant, increasing the dowel diameter from 20 mm to 40 mm substantially reduces stress and deflection, with further increases yielding minimal additional benefit. These findings provide useful guidance for selecting appropriate dowel dimensions in the design of airfield concrete pavements
Low-power cordic multiplier design using approximate arithmetic for energy-efficient computing
In digital IC design, low-power CORDIC-based multipliers have attracted significant attention due to their potential to integrate approximate adders for reducing energy and area costs. While CORDIC is hardware-efficient, its precise design still has room for improvement, particularly in terms of power consumption and area overhead. To address this, we present an approach to enhance the CORDIC multiplier using Approx. Adders from the EvoApproxLib library. The proposed design offers multiple variants with different optimization targets: up to 19.3% power reduction in CORDIC + Approx. Adder, 11.5% area savings in CORDIC + Approx. Adder, and 14.7% frequency improvement in CORDIC + Approx. Adder compared to the conventional exact CORDIC multiplier. When applied to Gaussian filtering and Sobel edge detection, optimal variants such as CORDIC + Approx. Adder and CORDIC + Approx. Adder yield PSNR values of 60 and 48 dB respectively, with SSIM values exceeding 0.990, indicating minimal quality loss. The evaluation shows that 8–10 iterations provide the best efficiency-accuracy trade-off, enabling designers to select appropriate variants based on specific application requirements. These results demonstrate the effectiveness of the proposed method for energy-constrained, error-tolerant systems in IoT devices, edge computing, and image processing applications
Capturing Micro-Amplitude Structural Vibrations with Low-Cost Cameras: A Non-Contact Measurement Approach
Vibrations of existing structures induced by wind, traffic, and human activities are often ambient in nature. These vibrations typically have low amplitudes, making it challenging to capture their motion with a camera. This paper presents a framework for measuring the displacement of structural ambient vibrations without physical contact using a low-cost camera. The micro-amplitude vibrations of the structure are captured by the camera and then amplified using the Phase-Based Video Motion Magnification (PVMM). The centroid of structural target segments in each image frame is tracked using the proposed Shape-Box Tracking (SBT). Unwanted background objects are removed using the proposed Line-Based Color Selection (LCS). Vibration characteristics are determined using frequency-based analysis and Random Decrement Technique (RDT). Two experiments based on the proposed framework were conducted to demonstrate its effectiveness. The first involved free-decay vibration testing with large amplitudes, while the second focused on ambient vibration with small amplitudes, emphasizing motion magnification. The success of this method paves the way for applying low-cost cameras in the operational measurement of existing structures