Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete

Min Chang Kang, Doo Yeol Yoo, Rishi Gupta

Research output: Contribution to journalArticlepeer-review

172 Citations (Scopus)


Steel fiber-reinforced concrete (SFRC) has a performance superior to that of normal concrete because of the addition of discontinuous fibers. The development of strengths prediction technique of SFRC is, however, still in its infancy compared to that of normal concrete because of its complexity and limited available data. To overcome this limitation, research was conducted to develop an optimum machine learning algorithm for predicting the compressive and flexural strengths of SFRC. The resulting feature impact was also analyzed to confirm the reliability of the models. To achieve this, compressive and flexural strengths data from SFRC were collected through extensive literature reviews, and a database was created. Eleven machine learning algorithms were then established based on the dataset. K-fold validation was conducted to prevent overfitting, and the algorithms were regulated. The boosting- and tree-based models had the optimal performance, whereas the K-nearest neighbor, linear, ridge, lasso regressor, support vector regressor, and multilayer perceptron models had the worst performance. The water-to-cement ratio and silica fume content were the most influential factors in the prediction of compressive strength of SFRC, whereas the silica fume and fiber volume fraction most strongly influenced the flexural strength. Finally, it was found that, in general, the compressive strength prediction performance was better than the flexural strength prediction performance, regardless of the machine learning algorithm.

Original languageEnglish
Article number121117
JournalConstruction and Building Materials
Publication statusPublished - 2021 Jan 10

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • General Materials Science


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