Learned prediction of compressive strength of GGBFS concrete using hybrid artificial neural network models

In Ji Han, Tian Feng Yuan, Jin Young Lee, Young Soo Yoon, Joong Hoon Kim

Research output: Contribution to journalArticle


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.

Original languageEnglish
Article number3708
Issue number22
Publication statusPublished - 2019 Nov 1



  • Artificial neural network
  • Back-propagation
  • Ground granulated blast furnace slag concrete
  • Hybrid PSO-BP
  • Particle swarm optimization

ASJC Scopus subject areas

  • Materials Science(all)

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