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Open AccessArticle

Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

1
School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea
2
School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University, Daegu 41566, Korea
*
Authors to whom correspondence should be addressed.
Materials 2019, 12(22), 3708; https://doi.org/10.3390/ma12223708
Received: 28 September 2019 / Revised: 31 October 2019 / Accepted: 6 November 2019 / Published: 10 November 2019
(This article belongs to the Section Materials Simulation and Design)
A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete. View Full-Text
Keywords: ground granulated blast furnace slag concrete; artificial neural network; particle swarm optimization; back-propagation; hybrid PSO-BP ground granulated blast furnace slag concrete; artificial neural network; particle swarm optimization; back-propagation; hybrid PSO-BP
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Han, I.-J.; Yuan, T.-F.; Lee, J.-Y.; Yoon, Y.-S.; Kim, J.-H. Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models. Materials 2019, 12, 3708.

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