Predicting the compressive strength of green concretes using Harris hawks optimization-based data-driven methods

Emadaldin Mohammadi Golafshani, Mehrdad Arashpour, Ali Behnood

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)


This study develops a practical tool for measuring the compressive strength (CS) of concrete using data-driven techniques. To develop predictive models for the CS of concretes containing supplementary cementitious materials, multi-layer neural network (MLNN) and radial basis function neural network (RBFNN) are extended by the Harris hawks optimization (HHO) algorithm. A comprehensive dataset was used containing information on 1374 unique concrete mixture proportions, curing age, and CS. The MLNN was trained using classic, HHO, and hybridized algorithms, while classic and HHO algorithms were deployed for training the RBFNN. Based on statistical metrics, the MLNN model developed by the hybridized training algorithm has the best performance among all developed models. The results indicate that cement, coarse and fine aggregate are the top three variables influencing the CS, while slag, fly ash, and concrete age are the least effective variables.

Original languageEnglish
Article number125944
Number of pages13
JournalConstruction and Building Materials
Publication statusPublished - 7 Feb 2022


  • Concrete compressive strength
  • Harris hawk optimization
  • Multi-layer neural network
  • Radial basis function neural network

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