High strength concrete modeling by artificial neural networks

Authors

Tiffany Ng, De La Salle University, Manila
Christian Carlo Roxas, De La Salle University, Manila
Arturo Flores Jr., De La Salle University, Manila
Andres Winston C. Oreta, De La Salle University, Manila

College

Gokongwei College of Engineering

Department/Unit

Civil Engineering

Document Type

Archival Material/Manuscript

Publication Date

2002

Abstract

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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Recommended Citation

Ng, T., Roxas, C., Flores, A., & Oreta, A. C. (2002). High strength concrete modeling by artificial neural networks. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/8816

Disciplines

Civil Engineering

Keywords

High strength concrete—Testing

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