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Prediction of fracture energy of concrete notched beam using artificial neural network
The crack and growth of cracks have been an interesting topics for the investigation of fracture mechanics of concrete being brittle material. For the safe design and failure behavior, the study of fracture energy is the most important aspect. The mix design constraint such as water to cement (w/c) ratio, the maximum aggregate size etc. will affect the fracture energy. In this study, the size effect method (SEM) was used to study the fracture energy, the three point bend test was performed on the notched beams. The concrete specimens were prepared using Nano TiO2 (NT) (1, 2, and 3%) and fly ash (FA) (30%). The mechanical properties of concrete were also evaluated. The artificial neural network (ANN) method is used to predict the fracture energy of concrete notched beams. The experimental data of the present study as well as the database obtained from the study of researchers consisting of 229 fracture tests are explored for the study. The parameters such as compressive strength, the maximum aggregate size and water to cement ratio are trained, validated and tested. The result reveals that fracture energy, compressive strength, tensile strength and flexural strength increase with an increase in NT percentage and it is improved by 5.56, 10.92, 9.41, and 16.16% respectively. It is also observed the predicted results obtained from the ANN show very good agreement with the data obtained experimentally.
Prediction of fracture energy of concrete notched beam using artificial neural network
The crack and growth of cracks have been an interesting topics for the investigation of fracture mechanics of concrete being brittle material. For the safe design and failure behavior, the study of fracture energy is the most important aspect. The mix design constraint such as water to cement (w/c) ratio, the maximum aggregate size etc. will affect the fracture energy. In this study, the size effect method (SEM) was used to study the fracture energy, the three point bend test was performed on the notched beams. The concrete specimens were prepared using Nano TiO2 (NT) (1, 2, and 3%) and fly ash (FA) (30%). The mechanical properties of concrete were also evaluated. The artificial neural network (ANN) method is used to predict the fracture energy of concrete notched beams. The experimental data of the present study as well as the database obtained from the study of researchers consisting of 229 fracture tests are explored for the study. The parameters such as compressive strength, the maximum aggregate size and water to cement ratio are trained, validated and tested. The result reveals that fracture energy, compressive strength, tensile strength and flexural strength increase with an increase in NT percentage and it is improved by 5.56, 10.92, 9.41, and 16.16% respectively. It is also observed the predicted results obtained from the ANN show very good agreement with the data obtained experimentally.
Prediction of fracture energy of concrete notched beam using artificial neural network
Asian J Civ Eng
Pathak, Sudhanshu S. (author) / Vesmawala, Gaurang R. (author) / Mane, Sachin J. (author)
Asian Journal of Civil Engineering ; 24 ; 2783-2796
2023-12-01
14 pages
Article (Journal)
Electronic Resource
English
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