CSDL Bài trích Báo - Tạp chí
chủ đề: Civil engineering
1 Influence of rice husk ash on mortar compressive strength at different temperatures: Machine learning based modelling / Tran Thu Hien, Hoang Nhat Duc // Khoa học & Công nghệ Đại học Duy Tân .- 2022 .- Số 5(54) .- P. 27-36 .- 624
The impact of different rice husk ash contents (5, 10, 20%) on mortar strength is examined at different elevated temperatures (150, 300, 450, 750oC). Based on a 45 experimental result data set, three machine learning algorithms including the Artificial Neural Network (ANN), the Least Squares Support Vector Regression (LS-SVR) and the Multivariate Adaptive Regression Splines (MARS) have been used to model the functional relationship between the mixture components and the compressive strength. As a result, it is shown that LS-SVR consists in the most capable approach for modeling mortar strength with a good value of coefficient of determination (R2) = 0.80. Accordingly, this machine learning approach is potential to be used in RHA contained mix design by construction engineers.
2 Image processing-based automatic gradation of stone aggregates / Hoang Nhat Duc, Nguyen Quoc Lam, Pham Quang Nhat // Khoa học & Công nghệ Đại học Duy Tân .- 2022 .- Số 5(54) .- P. 37-42 .- 624
Gradation strongly influences the mechanical properties of stone materials. This paper aims to employ image processing methods to develop a simple tool for automatic gradation of stone aggregates. The methods of image thresholding, Gaussian filtering, median filtering, morphological closing, and contour analysis are employed. The output of the newly constructed system is the plots demonstrating particle size distribution. These plots can be used for further inspection of aggregate gradation. The system has been developed in Python and with the help of the OpenCV library.
3 A review of metaheuristic optimized machine learning regression with applications in construction engineering / Hoang Nhat Duc, Nguyen Quoc Lam, Tran Van Duc // Khoa học & Công nghệ Đại học Duy Tân .- 2022 .- Số 5(54) .- P. 43-49 .- 624
Regression analysis is an essential task in construction engineering. This article aims at reviewing state-of-the-art research works involving the use of metaheuristic optimized machine learning regression models. Recent research articles published in the time period of 2019-2021 are surveyed. Research areas of construction material, construction management, structural engineering, geotechnical engineering, hydraulic engineering, and structural health monitoring are taken into account. It is expected that the present review would enhance interest among the new users in the application of metaheuristic optimized machine learning regression approaches.
4 Logistic regression for data classification developed in Excel VBA / Hoang Nhat Duc // Khoa học & Công nghệ Đại học Duy Tân .- 2022 .- Số 5(54) .- P. 110-115 .- 624
This research work aims at developing a logistic regression based data classification model. This model method is developed in Excel VBA to ease its practical implementations. The newly developed program has been tested with two basic data classification tasks.