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Prediction of mechanical properties of glass and basalt fiber reinforced concrete using ANN
https://orcid.org/http://orcid.org/0000-0002-7765-223X
The random distribution of glass and basalt fibers in normal concrete produces a composite that alters the behavior of hardened concrete members. Hence, it is quite complicated to predict the strengths of glass fiber reinforced concrete (GFRC) and basalt fiber reinforced concrete (BFRC). In the present study, an artificial neural network (ANN) model has been developed for predicting the strengths of concrete containing glass and basalt fibers at a concrete age of 28 days using data taken from the literature. The parameters considered for the ANN inputs are fine aggregate–cement ratio, coarse aggregate–cement ratio, water–cement ratio, fly ash–cement ratio, super plasticizer–cement ratio, fiber content, its diameter, density, elastic modulus, length and the concrete strengths as targets. The results from training and testing models have shown the great potential of ANN in predicting the compressive, split tensile and flexural strengths of GFRC and BFRC.
Prediction of mechanical properties of glass and basalt fiber reinforced concrete using ANN
https://orcid.org/http://orcid.org/0000-0002-7765-223X
The random distribution of glass and basalt fibers in normal concrete produces a composite that alters the behavior of hardened concrete members. Hence, it is quite complicated to predict the strengths of glass fiber reinforced concrete (GFRC) and basalt fiber reinforced concrete (BFRC). In the present study, an artificial neural network (ANN) model has been developed for predicting the strengths of concrete containing glass and basalt fibers at a concrete age of 28 days using data taken from the literature. The parameters considered for the ANN inputs are fine aggregate–cement ratio, coarse aggregate–cement ratio, water–cement ratio, fly ash–cement ratio, super plasticizer–cement ratio, fiber content, its diameter, density, elastic modulus, length and the concrete strengths as targets. The results from training and testing models have shown the great potential of ANN in predicting the compressive, split tensile and flexural strengths of GFRC and BFRC.
Prediction of mechanical properties of glass and basalt fiber reinforced concrete using ANN
https://orcid.org/http://orcid.org/0000-0002-7765-223X
The random distribution of glass and basalt fibers in normal concrete produces a composite that alters the behavior of hardened concrete members. Hence, it is quite complicated to predict the strengths of glass fiber reinforced concrete (GFRC) and basalt fiber reinforced concrete (BFRC). In the present study, an artificial neural network (ANN) model has been developed for predicting the strengths of concrete containing glass and basalt fibers at a concrete age of 28 days using data taken from the literature. The parameters considered for the ANN inputs are fine aggregate–cement ratio, coarse aggregate–cement ratio, water–cement ratio, fly ash–cement ratio, super plasticizer–cement ratio, fiber content, its diameter, density, elastic modulus, length and the concrete strengths as targets. The results from training and testing models have shown the great potential of ANN in predicting the compressive, split tensile and flexural strengths of GFRC and BFRC.
Prediction of mechanical properties of glass and basalt fiber reinforced concrete using ANN
Asian J Civ Eng
Kavya, B. R. (author) / Sureshchandra, H. S. (author) / Prashantha, S. J. (author) / Shrikanth, A. S. (author)
Asian Journal of Civil Engineering ; 23 ; 877-886
2022-09-01
10 pages
Article (Journal)
Electronic Resource
English
Mechanical Properties Test and Strength Prediction on Basalt Fiber Reinforced Recycled Concrete
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