Prediction of Compressive Strength and Modulus of Elasticity of Concrete using Machine Learning Models
This paper presents machine learning (ML) models for high fidelity prediction of compressive strength and modulus of elasticity (MOE) of concrete in relation to primary attributes of its mixture design. Two comprehensive databases, consisting of over 1000 and 500 data-records consolidated from technical publications, were used for training and testing the ML models that included random forests (RF), support vector machine (SVM) and multilayer perceptron artificial neural network (MLP-ANN). The metrics used for evaluation of prediction performance included five different statistical parameters and composite performance index (CPI). Results show that the RF model consistently outperforms the other two ML models in terms of prediction accuracy. Overall, machine learning is a very powerful and efficient tool for prediction of concrete properties as well as for the optimization of its mixture design to meet a set of desired performance criteria.
T. Han et al., "Prediction of Compressive Strength and Modulus of Elasticity of Concrete using Machine Learning Models," Proceedings of Materials Science and Technology (2019, Portland, OR), pp. 604-611, Materials Science and Technology, Oct 2019.
The definitive version is available at https://doi.org/10.7449/2019/MST_2019_604_611
Materials Science and Technology 2019, MS & T 2019 (2019: Sep. 29-Oct. 3, Portland, OR)
Civil, Architectural and Environmental Engineering
Electrical and Computer Engineering
Materials Science and Engineering
Re-Cast Tier1 University Transportation Center
Keywords and Phrases
Artificial neural network; Compressive strength; Machine learning; Modulus of elasticity; Random forest; Support vector machine
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Oct 2019