An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete

Machine Learning (ML) has made significant progress in several fields, and materials science is no exception. ML models are popular in the materials science community, especially for predicting the compressive behavior of high-performance concrete. However, little attention has been given to enhancing the predictive capabilities and explainability of off-the-shelf ML models. Therefore, in this paper, we improve both the predictability and explainability of our ML model through pipelining, automated feature selection, cross-validated hyperparameter tuning, and a game theory approach to generate explainable insights that could lead to the development of materials with novel or substantially improved properties. •Proposed model improves the predictability of high-performance concrete compressive strength (HPCCS).•A game theory approach enriches the explainability of the proposed model.•The age, water to binder ratio, and cement content greatly influences the HPCCS.•A comprehensive representation explains the interactions between features and the HPCCS.•The elucidated relationship between concrete composition vs. strength enriches our understanding of concrete behavior.