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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2002
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oai:animorepository.dlsu.edu.ph:faculty_research-98792023-04-11T06:21:10Z High strength concrete modeling by artificial neural networks Ng, Tiffany Roxas, Christian Carlo Flores, Arturo, Jr. Oreta, Andres Winston C. 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. 2002-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/8816 Faculty Research Work Animo Repository High strength concrete—Testing Civil Engineering |
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De La Salle University |
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De La Salle University Library |
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Philippines Philippines |
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DLSU Institutional Repository |
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High strength concrete—Testing Civil Engineering |
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High strength concrete—Testing Civil Engineering Ng, Tiffany Roxas, Christian Carlo Flores, Arturo, Jr. Oreta, Andres Winston C. High strength concrete modeling by artificial neural networks |
| description |
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. |
| format |
text |
| author |
Ng, Tiffany Roxas, Christian Carlo Flores, Arturo, Jr. Oreta, Andres Winston C. |
| author_facet |
Ng, Tiffany Roxas, Christian Carlo Flores, Arturo, Jr. Oreta, Andres Winston C. |
| author_sort |
Ng, Tiffany |
| title |
High strength concrete modeling by artificial neural networks |
| title_short |
High strength concrete modeling by artificial neural networks |
| title_full |
High strength concrete modeling by artificial neural networks |
| title_fullStr |
High strength concrete modeling by artificial neural networks |
| title_full_unstemmed |
High strength concrete modeling by artificial neural networks |
| title_sort |
high strength concrete modeling by artificial neural networks |
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Animo Repository |
| publishDate |
2002 |
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https://animorepository.dlsu.edu.ph/faculty_research/8816 |
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