CSDL Bài trích Báo - Tạp chí
chủ đề: Concrete structures
1 Interval estimation of compressive strength of concrete using artificial neural network developed with Python = Dự báo theo khoảng cường độ chịu nén của bê tông sử dụng mạng nơ-ron thần kinh nhân tạo được phát triển với Python / Hoang Nhat Duc, Nguyen Quoc Lam, Pham Quang Nhat // .- 2023 .- Số 05 (60) - Tháng 10 .- P. 10-15 .- 693
The compressive strength (CS) of concrete mixes is a crucial parameter. This paper aims to construct an artificial neural network (ANN) model for interval estimation of the CS of concrete blended with ground granulated blast furnace slag (GGBFS). The nonlinear regression based method is employed to derive the prediction intervals.
2 The Eurocodes : research and application for concrete structures in Vietnam context = Tiêu chuẩn châu Âu : nghiên cứu và ứng dụng cho kết cấu bê tông cốt thép trong điều kiện Việt Nam / Nguyen Truong Thang, Nguyen Tuan Trung, Dang Viet Hung // .- 2023 .- Quý 2 .- P. 3-13 .- 690
Towards this orientation, significant attentions have also been paid and various number of studies on design of concrete structures to the Eurocodes have been conducted by Vietnamese researches, which will be introduced in this paper to prepare for the comprehensive applications of the Eurocodes for concrete structures in Vietnam in the coming time when appropriable, especially the second generation of the Eurocodes will be expected to be also issued in coming years
3 A method for calculating flexural multi-layer reinforced concrete structures = Phương pháp tính toán kết cấu bê tông cốt thép nhiều lớp chịu uốn / Vu Dinh Tho, Tuan Anh Pham // .- 2023 .- Quý 2 .- P. 22-33 .- 690
In this study, the authors have introduced a theoretical method to calculate and analyze the stress-strain states of flexural reinforced concrete structures with cross-sectional sections consisting of layers from different concrete materials under the effect of load.
4 Artificial neural network with adaptive moment estimation training approaches for prediction of punching shear capacity of steel / Hoang Nhat Duc // .- 2022 .- Số 4(53) .- Tr. 18-22 .- 624
Estimating punching shear capacity (PSC) of steel fibre reinforced concrete slabs (SFRCS) is a crucial task in structural design. This study investigates the performances of artificial neural networks trained by the adaptive moment estimation (Adam) method in dealing with the task of interest. To alleviate overfitting problem, decoupled weight decay (AdamW) and L2regularization (AdamL2) are used. A dataset including 140 samples has been used to train and verify the machine learning approaches. Interms of root mean square error (RMSE), Experimental results including 20 independent runs point out that predictive performances of the AdamW (RMSE = 30.60) and AdamL2(RMSE = 31.74) are better than that of the Adam (RMSE = 36.62). However, performance of a combination of AdamW and AdamL2(RMSE = 32.31) is worse than those obtained from the individual AdamW and AdamL2.