High strength concrete modeling by artificial neural networks
Artificial Neural Networks (ANN) of the backpropagation type were used to map the strength of High Strength Concrete (HSC) given the design mix. Several ANN models were trained and simulated using 89 sets of data composed of the amount of cement, water, admixture, slag, silica fume, RHA, fine aggreg...
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| Main Authors: | , , , |
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| Format: | text |
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Animo Repository
2002
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/8816 |
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| Institution: | De La Salle University |
| Summary: | Artificial Neural Networks (ANN) of the backpropagation type were used to map the strength of High Strength Concrete (HSC) given the design mix. Several ANN models were trained and simulated using 89 sets of data composed of the amount of cement, water, admixture, slag, silica fume, RHA, fine aggregates, coarse aggregates, fly ash and metakaolin, and the corresponding compressive strength of concrete at 28 days. Past studies on the behavior of HSC were also discussed to validate and compare with the results from the ANN models. The results show that ANN can be used to trace the behavior of HSC and predict its strength. |
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