Use of Technology model

This study investigates the factors affecting students' intention to participate in training and development programs on Digital and Technological skills at Can Tho University of Technology. Using a survey of 339 students, the study employs reliability testing, exploratory factor analysis (EFA), and Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the data.

9p vijiraiya 19-05-2025 1 1   Download

This study aims to examine the effect of organizational justice on the organizational citizenship behaviors of employees in information technology enterprises, with a specific focus on the mediating role of job satisfaction. Data was collected from 300 voluntary respondents working in information technology enterprises in Vietnam, using online structured questionnaires. T

11p visarada 28-04-2025 2 1   Download

This study investigates the impact of intellectual property rights enforcement on Korea’s technological exports to 158 partner countries during 2010–2022. Using a gravity model and GMM estimation, the findings reveal a significant positive effect of IPR on exports across all technology levels.

9p visarada 28-04-2025 1 1   Download

The research focuses on applying deep learning to automate the fruit recognition and classification process, meeting the development needs of modern agriculture. Applying this technology helps improve efficiency and classification quality and reduces labor costs, resulting in lower product prices. The research team used two deep learning models, SSD300 and YOLOv10s, to recognize and classify six types of fruits: apples, bananas, kiwis, lemons, oranges, and strawberries.

9p vimaito 11-04-2025 1 1   Download

In this paper, the effect of continuous carbon fibers (CCF) on the stiffness of 3D printed frames is investigated. Fused deposition modelling (FDM) technology is employed to build three typical frames with 15% of CCF and 85% poliamide 12 which is combined 10% short carbon fibers (PA12-CF) for compress testing.

7p viengfa 28-10-2024 1 1   Download

The main objective of this study is to predict accurately the loaddeflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed.

9p viengfa 28-10-2024 3 2   Download

This article conducts an exhaustive investigation into the utilization of machine learning (ML) methods for forecasting the maximum load capacity (MLC) of circular reinforced concrete columns (CRCC) using Fiber-Reinforced Polymer (FRP). Extreme Gradient Boosting (XGB) algorithm is combined with novel metaheuristic algorithms, namely Sailfish Optimizer and Aquila Optimizer, to fine-tune its hyperparameters.

18p viengfa 28-10-2024 2 2   Download

This study delves into the application of machine learning (ML), specifically a Gradient Boosting (GB) model, for predicting the punching shear strength (PSS) of two-way reinforced concrete flat slabs.

16p viengfa 28-10-2024 3 2   Download

The present paper also includes strength prediction models that consider the influence of curing temperature. In addition, the Thermogravimetry analysis (TGA) used to determine the chemically bound water content in cement-treated soil and the X-ray diffraction (XRD) test to explain the chemical mechanism in cement-treated soil are also mentioned.

18p viengfa 28-10-2024 4 2   Download

This paper presents the development of an Artificial Intelligence (AI) and Machine Learning (ML) model designed to detect cracks on concrete surfaces. The objective is to enhance the automation, precision, and performance of crack detection using the computer vision algorithm.

13p viengfa 28-10-2024 2 2   Download

The results of this study would be useful in quickly and accurately predicting CPI to the management agencies, investors, construction contractors to pre-plan the construction investment costs. This will also help in suitably adjusting changing construction cost with time.

11p viengfa 28-10-2024 2 2   Download

This study focuses on optimizing the hyperparameters of the XGB model by a grid search to find an optimal XGB predictive model. In addition, the effect of input parameters on the SCCRF's CS is studied using Shapley Additive exExplanations (SHAP) values technique.

14p viengfa 28-10-2024 6 2   Download

This paper is aimed to apply hybrid machine learning model namely GA-ANFIS, which is a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA), for the prediction of total bearing capability of driven piles.

8p viengfa 28-10-2024 3 2   Download

In this study, we propose a machine learning technique for estimating the shear strength of CRC beams across a range of service periods. To do this, we gathered 158 CRC beam shear tests and used Artificial Neural Network (ANN) to create a forecast model for the considered output.

12p viengfa 28-10-2024 3 2   Download

This study proposes the application of Ensemble Decision Tree Boosted (EDT Boosted) model for forecasting the surface chloride concentration of marine concrete Cs. A database of 386 experimental results was collected from 17 different sources covering twelve variables was used to build and verify the predictive power of the EDT model.

12p viengfa 28-10-2024 5 2   Download

In this study, an artificial neural networkbased Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition.

12p viengfa 28-10-2024 2 2   Download

This paper presents the results of applying the Artificial Neural Network (ANN) model in determining pile bearing capacity. The traditional methods used to calculate the bearing capacity of piles still have many disadvantages that need to be overcome such as high cost, complicated calculation, time-consuming.

8p viengfa 28-10-2024 3 2   Download

In this study, the Machine Learning (ML) approach has been adopted using Random Forest (RF) model to estimate the CBR of the soil based on 10 input parameters such as Plasticity Index (PI), Liquid Limit (LL), Silt Clay content (SC), Fine Sand content (FS), Coarse sand content (CS), Optimum Water Content (OWC), Organic content (O), Plastic Limit (PL), Gravel content (G), and Maximum Dry Density (MDD), which can be easily determined in the laboratory.

14p viengfa 28-10-2024 6 2   Download

Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) algorithms were utilized to produce numerical tools for predicting the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets.

11p viengfa 28-10-2024 3 2   Download

Light Gradient Boosting Machine is a new machine learning technique developed by Microsoft corporation which has been proposed in the present study to determine the CBR of stabilized expansive soils. Model performance of the ML model are evaluated by different criteria such as correlation coefficient R, root mean square error RMSE and mean absolute error MAE.

8p viengfa 28-10-2024 4 2   Download