Efficient machine learning models for prediction of concrete strengths

Hoang Nguyen*, Thanh Vu, Thuc P. Vo, Huu Tai Thai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)
40 Downloads (Pure)


In this study, an efficient implementation of machine learning models to predict compressive and tensile strengths of high-performance concrete (HPC) is presented. Four predictive algorithms including support vector regression (SVR), multilayer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGBoost) are employed. The process of hyperparameter tuning is based on random search that results in trained models with better predictive performances. In addition, the missing data is handled by filling with the mean of the available data which allows more information to be used in the training process. The results on two popular datasets of compressive and tensile strengths of high performance concrete show significant improvement of the current approach in terms of both prediction accuracy and computational effort. The comparative studies reveal that, for this particular prediction problem, the trained models based on GBR and XGBoost perform better than those of SVR and MLP.

Original languageEnglish
Article number120950
Number of pages17
JournalConstruction and Building Materials
Issue numberPart B
Early online date17 Oct 2020
Publication statusPublished - 10 Jan 2021


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