Materials Science and Engineering Commons^™

Prediction Of Concrete Strengths Enabled By Missing Data Imputation And Interpretable Machine Learning, Gideon A. Lyngdoh, Mohd Zaki, N.M. Anoop Krishnan, Sumanta Das

Faculty Publications - Biomedical, Mechanical, and Civil Engineering

Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes requires a large, complete, and consistent dataset. However, the availability of such datasets is limited as the datasets often suffer from incompleteness because of missing data corresponding to different input features, which makes the development of robust ML-based predictive models challenging. Besides, as the degree of complexity in these ML models increases, the interpretation of the results becomes challenging. These interpretations of results are critical towards the development of efficient materials design strategies for enhanced materials performance. To address these challenges, this paper implements different data imputation approaches for enhanced …