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Computers and Concrete Volume 27, Number 1, January 2021 , pages 21-28 DOI: https://doi.org/10.12989/cac.2021.27.1.021 |
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The prediction of compressive strength and non-destructive tests of sustainable concrete by using artificial neural networks |
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Ahmed M. Tahwia, Ashraf Heniegal, Mohamed S. Elgamal and Bassam A. Tayeh
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| Abstract | ||
| The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement. | ||
| Key Words | ||
| artificial neural network; nondestructive tests; Ultrasonic Pulse Velocity (UPV); compressive strength; sustainable concrete; pozzolanic materials | ||
| Address | ||
| Ahmed M. Tahwia: Civil Engineering Department, Faculty of Engineering, Mansoura University, Egypt Ashraf Heniegal: Civil Engineering Department, Faculty of Engineering, Suez University, Egypt Mohamed S. Elgamal: Civil Engineering Department, Faculty of Engineering, Mansoura University, Egypt Bassam A. Tayeh: Civil Engineering Department, Faculty of Engineering, Islamic University of Gaza, Gaza, Palestine | ||