Non-tuned machine learning approach for predicting the compressive strength of high-performance concrete

Abobakr Khalil Al-Shamiri, Tian Feng Yuan, Joong Hoon Kim

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously published studies. The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. k-fold cross-validation was used to assess the prediction reliability of the developed RELM model. The prediction results of the RELM model were evaluated using various error measures and compared with that of the standard extreme learning machine (ELM) and other methods presented in the literature. The findings of this research indicate that the compressive strength of HPC can be accurately estimated using the proposed RELM model.

Original languageEnglish
Article number1023
Issue number5
Publication statusPublished - 2020 Mar 1


  • Compressive strength
  • Extreme learning machine
  • High-performance concrete
  • Prediction
  • Regularization

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics


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