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 Predicting Concrete Slump using Fly Ash and Stone Powder in Central Vietnam
Tác giả hoặc Nhóm tác giả: Le Thang VUONG*, Cung LE, Dinh Son NGUYEN
Nơi đăng: Technical Gazette 31, 1(2024), 79-87; Số: 31, 1(2024);Từ->đến trang: p79-87;Năm: 2024
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Abstract: Fly ash and stone powder, which are abundant wastes in Central Vietnam, are viable alternatives to cement and sand in concrete production. However, thisreplacement may worsen the compressive strength and slump of concrete. This study deals with the prediction of the slump and compressive strength of concrete using flyash and stone powder in Central Vietnam as cement and sand substitute materials, respectively. First, the Ishikawa diagram was used to analyze the factors affecting theconcrete workability and compressive strength, in combination with the method of design of experiment to determine the required number of testing specimens. A total of 72concrete mixtures with slump of 3 - 12 cm and compressive strengths 10 MPa - 60 MPa were designed. Subsequently, regression and artificial neural network methods wereused to predict the concrete slump and compressive strength. The results demonstrated the high accuracy of the artificial neural network model. In addition, the abovemodels allowed us to determine the proportion of concrete ingredients that met the slump and compressive strength requirements.Keywords: artificial neural network; concrete; design of experiment; fly ash; multivariable linear regression; slump; stone powder
ABSTRACT
Abstract: Fly ash and stone powder, which are abundant wastes in Central Vietnam, are viable alternatives to cement and sand in concrete production. However, thisreplacement may worsen the compressive strength and slump of concrete. This study deals with the prediction of the slump and compressive strength of concrete using flyash and stone powder in Central Vietnam as cement and sand substitute materials, respectively. First, the Ishikawa diagram was used to analyze the factors affecting theconcrete workability and compressive strength, in combination with the method of design of experiment to determine the required number of testing specimens. A total of 72concrete mixtures with slump of 3 - 12 cm and compressive strengths 10 MPa - 60 MPa were designed. Subsequently, regression and artificial neural network methods wereused to predict the concrete slump and compressive strength. The results demonstrated the high accuracy of the artificial neural network model. In addition, the abovemodels allowed us to determine the proportion of concrete ingredients that met the slump and compressive strength requirements.Keywords: artificial neural network; concrete; design of experiment; fly ash; multivariable linear regression; slump; stone powder
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