Abstract: 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.
Authors: Taihao Han, Kamal Khayat, Hongyan Ma, Jie Huang, and Aditya Kumar
Keywords: machine learning; random forest; support vector machine; artificial neural network; compressive strength; modulus of elasticity