VNUHCM Journal of Engineering and Technology

DIRECT METHOD FOR ANALYSIS OF PRESSTRESSED CONCRETESTRUCTURE

Pham Thanh Cong — 2026-05-13

One of significant progress of construction industry is prestressed concrete. The range of applications is very wide in the high-rise buildings, large-span buildings and the bridges. Prestressed concrete has been successfully used for small as well as large projects over the last sixty years. The efficiency stems from being able to use high strength materials, to structurally utilize the entire cross section, to vary the force and location of the reinforcing to best resist applied loads, and to control the timing of when the prestressing force is applied to the structure. In prestressed concrete structures, load balancing is an indirect and familiar method for analyzing prestressed members through equivalent load. The frame being subjected to equivalent load produces balanced moment or prestressing moment (it is taken into account in serviceability limit state) and reactions; then, secondary moment (it is taken into account in ultimate limit state) is produced by reactions. The paper presents a direct method for analyzing prestressed members by solving the differential equations of the elastic line. These differential equations consider the equilibrium of the reactions and eccentricity of the prestressing force. After solving these equations, the engineers can directly obtain the reactions; and subsequently, determine the prestressing moment, secondary moment and shear force. The direct method also finds out the precise effect resulting from large tendon sag in transfer or deep beam. Effect of large tendon sag is the same as small tendon sag, the differences are very small. Besides, in this paper, the equivalent load models associated with any complicated tendon profiles which are formularized deal with and expand the unconventional tendon profile in the future of construction industry.

ANALYSIS OF FACTORS AFFECTING THE PROGRESS OF CONSTRUCTION PROJECT IN VIET NAM

Tran Hoc Duc — 2026-05-21

In construction projects, three core objectives, including quality, cost, and schedule, are considered key criteria for project success. However, in Vietnam, project delays remain prevalent, significantly affecting investment efficiency and construction quality. This study aims to analyze the factors affecting construction project schedules and to evaluate the impact of delays on project cost and quality. The research methodology involves a synthesis of domestic and international studies, combined with surveys of experts and experienced professionals in the construction field. Data were collected from 173 valid responses and analyzed using statistical techniques, including Cronbach’s Alpha, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The results indicate that the measurement scales are reliable and valid, with statistically significant observed variables and a well-fitted research model. The findings reveal that contractor-related factors have the greatest impact on project schedules, followed by factors related to equipment and materials; project owners and project characteristics; design units; external factors; and consulting units. Notably, the “decision-making speed of the project owner” is identified as the most influential factor. The SEM results demonstrate that project delays have direct effects on cost (coefficient = 0.41) and quality (coefficient = 0.38). This study provides empirical evidence on the relationships among schedule, cost, and quality, while supporting stakeholders in identifying and managing risk factors. Accordingly, the findings contribute to a scientific basis for improving schedule management efficiency and enhancing the success rate of construction projects in Vietnam.

Research on Establishing Form Error Predictive Model for Al6061 Spherical Surfaces in Ultra-Precision Turning Using Artificial Neural Network

Duong Xuan Bien — 2026-06-02

Currently, driven by the increasing demand for precision in optical and mechanical components, complex surfaces are now required to maintain profile errors within micrometric tolerances. The development of profile error prediction models is, therefore, not merely a technical solution but a vital instrument for optimizing product quality, enhancing cost-effectiveness, minimizing scrap rates, and streamlining machining lead times. This study presents a predictive model for the form error of spherical surface (SS) on Al6061 aluminum alloy during ultra-precision turning (UPT) using a single-crystal diamond tool. The prediction model was developed based on a back-propagation (BP) neural network (NN) structure. The dataset for establishing the prediction model was collected from 30 precision machining experiments on spherical surfaces. To ensure the objectivity and reliability of the data, the fixturing and alignment procedures as well as the measurement method for profile error were standardized and thoroughly modeled. The cutting parameters, including spindle speed (n – rev/min), feed rate (F - mm/min), and depth of cut (ap - µm), were identified as independent variables to establish the relationship with the profile error. The determination of the appropriate neuron ratio between layers was investigated through six specific network structures. The criteria for evaluating the prediction quality of the neural network included the coefficient of determination (R²), mean squared error (MSE), and mean absolute percentage error (MAPE). Accordingly, the artificial neural network ANN structure 3:5:20:1 delivered the best prediction performance, achieving an R² value of 1, MSE of 26.9, RMSE of 5.19, and MAPE of 0.2%. The results not only confirm the effectiveness of the proposed method but also provide a reliable scientific foundation for the development of profile error prediction models. At the same time, this study demonstrates the potential for extending the application to other complex surfaces such as aspheric surfaces, diffractive surfaces, or on different substrate materials.

Study on Landslide Mechanism on Basaltic Soils in Lam Dong Province, Vietnam

Ly Thi Khau — 2026-06-02

This study investigated the key physical and chemical properties of basaltic soils in Lam Dong Province, Vietnam, and assessed their influence on slope stability under the region's distinct tropical monsoon climate. The integrated research methodology combined comprehensive field surveys, sampling across representative slopes with detailed laboratory analyses and numerical modeling. A total of 90 soil samples were collected and subjected to a series of tests to determine critical physical and mechanical parameters, including natural moisture content, Atterberg limits (liquid limit, plastic limit, and plasticity index), specific gravity, and grain size distribution. The stability of representative slopes was then simulated under varying rainfall infiltration scenarios using the finite element method in Plaxis 2D software. The experimental results revealed that the soils are characterized by a significant clay fraction (≥ 30%), with the presence of expansive montmorillonite group minerals. This mineralogical composition promotes pronounced shrink-swell cycles in response to seasonal moisture variations, generating cracks and fissures that facilitate deeper water infiltration. The modeling and analysis further demonstrated that prolonged and intense monsoon rainfall critically reduces soil shear strength by markedly increasing pore water pressure and decreasing effective stress within the soil matrix. Concurrently, the chemical weathering process, particularly the dissolution and alteration of iron oxide (Fe₂O₃) cementing agents under the influence of naturally acidic rainwater, contributes to the long-term weakening of soil structure and further slope destabilization. Moreover, the slope stability modeling conclusively quantified the detrimental impact of rainfall, showing a significant reduction in the factor of safety for slopes during simulated events. This research elucidates the coupled hydro-mechanical and chemical mechanisms driving landslides in this setting, where intense weathering, high clay content, and monsoon hydrology interact. The findings substantially deepen the understanding of failure mechanisms in tropical red basaltic soils. Consequently, they provide a scientific basis for selecting and designing appropriate slope stabilization, drainage, and land-use planning interventions. This work is pivotal for developing integrated geotechnical and hydrological management strategies to mitigate landslide risks not only in Lam Dong Province but also across similar terrains in the Central Highlands of Vietnam, thereby contributing to enhanced resilience and natural disaster prevention.

Garment Fit Assessment: A Comprehensive Literature Review Integrating Fabric Mechanics, 3D Simulation, and Machine Learning Techniques

Phan Ha Nhu Ngoc — 2026-06-03

Garment fit is a key factor influencing consumer comfort, confidence, and purchasing decisions, particularly in the context of e-commerce, where the inability to try on products physically results in poor fit being a leading cause of costly product returns, material waste, and customer dissatisfaction. These challenges negatively impact not only consumer experience but also operational efficiency and environmental sustainability. However, traditional methods for evaluating garment fit are subjective, time-consuming, and inconsistent, highlighting the urgent need for more accurate, objective, data-driven, and scalable assessment solutions. This study presents a systematic literature review of research on garment fit assessments published between 2017 and 2025. The review emphasizes integrated approaches that combine fabric mechanical analysis, three-dimensional (3D) virtual simulation, and machine learning techniques to enhance the precision and efficiency of fit evaluation processes. A range of methodologies is examined and categorized into four major research areas: (1) 3D simulation and virtual try-on technologies; (2) data-driven machine learning and analytics; (3) material fabric–body interaction analysis; and (4) hybrid multi-technology integration frameworks. Results indicate that advanced tools such as 3D body scanning, simulation-based pattern adjustment, and predictive machine learning models have emerged as dominant technologies in recent studies. The review also identifies research gaps, such as a lack of standardized data formats, limited realism in fabric and body modeling, and poor interpretability of AI-driven systems. To address these limitations, the study proposes future research directions, including the development of open-access datasets, improvements in simulation fidelity, and enhancements in model explainability. Ultimately, these insights aim to inform the development of next-generation garment fit assessment frameworks that are accurate, transparent, personalized, and adaptable for practical use in the evolving digital fashion landscape.

Molecular docking, DFT study of some modified curcumins as potential anticancer agents on CXCR2 receptor

Thu Hanh Tran Thi — 2026-05-07

Natural compounds with potential applications in treating complex diseases such as cancer are increasingly garnering attention in medical research. The utilization of computational modeling methods is becoming more prevalent in studying these compounds, facilitating the selection of promising molecular frameworks for therapeutic purposes. Curcumin, a molecule with numerous modified and analogous structures, is used to anticipate potential pharmaceutical compounds using computational calculation methods. This study aims to employ several structures, including curcumin, demethoxycurcumin (DMC), bisdemethoxycurcumin (BDMC), dihydrocurcumin (DHC), and notably tetrahydrocurcumin (THC), to forecast the potential inhibition of the CXCR2 receptor through the DFT and molecular docking methodologies. Molecular docking and DFT calculations play crucial roles in predicting activity stability and electron properties, aiding in better understanding the compounds' structures. In this article, the Density Functional Theory (DFT) method will be employed to optimize the structure and calculate various quantum parameters. Subsequently, the optimized structure will undergo 1H NMR spectroscopy computation and comparison with experimental data to evaluate the proximity to experimental reality. The Ligands will then be subjected to docking with the CXCR2 protein to assess their impact on this protein. The research delineates the noteworthy inhibitory efficacy of THC on CXCR2, facilitated by the formation of pi-sigma bonds within the receptor's binding pocket. These findings are expected to guide forthcoming investigations aimed at advancing THC as a prospective pharmaceutical candidate in the future. This article comprises the following sections: an introduction section providing an overview of the natural molecules which are called the ligands, and target protein; the computational methods section outlining the computational techniques to be utilized in the study that include DFT and molecular docking with Autodock Vina software; and a results section presenting the findings obtained during the research process.

[BKYST] Application of endophytes to prevent Fusarium oxysporum f. sp. cubense causing Panama wilt disease on Cavendish banana

Thu Huynh — 2026-05-25

The endophytic community, living within host plants, is essential for promoting growth and enhancing disease resistance in plants. These endophytes establish a symbiotic relationship with their host plants, providing various benefits such as nutrient uptake, stress tolerance, and protection against pathogens. Their presence highlights the intricate and fascinating interactions that occur within the plant kingdom, ultimately contributing to the overall health and vitality of the ecosystem. However, modern agricultural practices such as breeding and the use of chemical fertilizers have led to a weakening and loss of these important microorganisms. So, there is a need to reconsider our approach to farming in order to preserve and harness the benefits of endogenous microflora for sustainable and healthy plant growth.

Banana is one of the important crops in Vietnam and globally due to its high economic value. The banana growing industry is profitable, but it also encounters numerous disease risk factors. One of the most severe threats is the fungus Fusarium oxysporum f. sp. cubense, which causes wilted Panama disease and has devastated banana cultivation areas worldwide. It is fact that naturally grown banana plants with a healthy endophytic interaction are less susceptible to Panama disease fungi. This highlights the importance of maintaining a balanced and diverse plant-endophytic interaction in the soil to protect banana crops from diseases. Panama disease strain F. oxysporum f. sp. cubense TR4 is known to specifically target Cavendish banana varieties produced by tissue culture. Various studies worldwide have shown that endophytic community associated within plant can inhibit the growth of F. oxysporum f. sp. cubense. Introducing these natural indigenous endophytes into large-scale cultivated banana plants aligns with the plant's natural physiology and can enhance the plant's resistance to diseases. This method promises a sustainable and environmental approach to mitigating the impact of Panama disease on banana crops.

[BKYST] Application of Image Processing for Quality Control in V-bending Processing

Nguyen Quoc Banh — 2026-05-25

The V-bending process, widely used across various everyday products, has experienced a growing demand for tighter quality control measures. Traditional methods often rely on visual inspection or manual angle gauges, which can be time-consuming and prone to human error. This journal explores the application of image processing techniques for automated angle measurement of V-bending workpieces. The proposed method places a camera to capture an image of the workpiece. Subsequently, the Canny edge detection algorithm is employed to identify the edges within the image. These edges are then utilized by the Hough transform, a technique capable of extracting lines from the detected edges. Through analysis of the extracted lines, the angle of the V-bending is calculated. This approach offers a promising alternative to traditional quality control methods, potentially leading to increased accuracy and efficiency. While the result shows high reliability with a minimal error, further development can focus on refining the system's factors like camera placement and specimen variations. Ultimately, this method paves the way for a more automated and reliable approach to V-bending quality control besides existing high-precision techniques like Coordinate Measuring Machines (CMM).

[BKYST] Using Deep Learning Models in Prediction of Surface Roughness

Than Trong Khanh Dat — 2026-05-28

In contemporary industrial operations, machining precision has undergone significant advancements, placing surface roughness measurement at the forefront of quality assessment. However, current methods for measuring surface roughness are not only time-consuming but also labor-intensive, often requiring additional effort for equipment preparation and fixture setup. Moreover, the current practice often involves direct measurement on the sample, necessitating the removal of the sample from the machining equipment, which can introduce setup errors and potentially compromise the original machining standards. Recognizing these challenges, our project aimed to streamline the post-machining inspection process by evaluating surface roughness through imaging of the machined surface. This article explores the application of deep learning models, facilitated by MATLAB software, to diagnose surface roughness in machining. Additionally, we propose a comprehensive method for sampling measurement, including procedures and conditions, to guide further project development based on collected samples or by incorporating any missing conditions to enhance the project in the future. Leveraging non-contact measurement methods ensures precise surface details regarding roughness and glossiness, making them suitable for processing. This advancement represents a significant step forward in testing and measurement, with wide-ranging applications in mechanical machining aimed at boosting productivity, reducing costs, and machining time, ultimately optimizing profits and yielding substantial economic benefits in the long run. The results obtained from our study exhibit promising signals and a high level of feasibility in diagnosing and verifying surface quality in machining. The number of measured samples will be synthesized, supplemented, and provided to relevant parties to significantly increase the sample size, thereby enhancing the accuracy of the AI model and accelerating prediction capabilities through vast data. To bolster the reliability of these findings, it is imperative to augment the model with additional data, maximizing its effectiveness and applicability in real-world scenarios.