Efficient machine learning models for prediction of concrete strengths

Nguyen, Hoang, Vu, Thanh, Vo, Thuc P. and Thai, Huu-Tai (2021) Efficient machine learning models for prediction of concrete strengths. Construction and Building Materials, 266 (Part B). p. 120950. ISSN 0950-0618

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Official URL: https://doi.org/10.1016/j.conbuildmat.2020.120950

Abstract

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.

Item Type: Article
Uncontrolled Keywords: High performance concrete, Ensemble learning, Support vector machine, Multi-layer Perceptron, Tree-based algorithms
Subjects: G400 Computer Science
H200 Civil Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 23 Nov 2020 13:51
Last Modified: 24 Nov 2020 09:15
URI: http://nrl.northumbria.ac.uk/id/eprint/44818

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