Prediction of seven-day compressive strength of field concrete

This study has explored nine machine learning methods that cover linear, non-linear, and ensemble learning models to predict the compressive strength of field concrete at 7 days. Seven concrete constituents (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizing admixture, and water reducing admixture) are used as the predictors. A dataset of 12,107 field concrete observations associated with 25 unique mix designs has been used to train and test the predictive models. Evaluated against seven performance metrics, it is found that non-linear models perform better than linear models in general and that the random forest model of ensemble learning performs the best. Compared to previous studies, the models of this study significantly improve in terms of the various performance metrics. Besides, this study confirms that data visualization is useful in learning about and summarizing the data, understanding the relationships of the variables, and making premodeling assumptions. This study has also assessed the relative importance of the seven concrete constituents (an aspect previous studies had not investigated), and identified cement, water reducing admixture and fine aggregate as the top three most significant constituents in the development of the seven-day compressive strength.

Automated summary

This study explored nine machine learning methods and found that nonlinear models generally perform better than linear models, with the random forest model of ensemble learning performing the best. The study also confirmed the usefulness of data visualization in learning, summarizing data, understanding variable relationships, and making premodeling assumptions, as well as identified the top three most significant concrete constituents affecting the seven-day compressive strength.