An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete

Graphene oxide (GO) has shown promise in improving concrete strength. Despite its frequent use in cement composites, its effect on concrete properties is less explored. The influence of GO on concrete remains unclear due to various interactions among constituents such as GO type and percentage, Superplasticiser (SP) type and percentage, dispersion technique, and curing age. Therefore, making prediction of compressive strength using simple mathematical formation is challenging. This study represents the first-ever attempt at developing a comprehensively validated machine learning model to predict GO-concrete's compressive strength characteristics by considering all key variable parameters. A comprehensive laboratory experiment program was conducted to collect the data required for training the machine learning (ML) models. Once the ML models were trained, they were used to predict the relationship of each of those input variables towards the compressive strength properties. Four machine learning models—(a) multiple linear regression (MLR), (b) k-nearest neighbour (KNN), (c) random forest (RF) and (d) extreme gradient boost (XGB)—were utilised to model the relationship between input parameters and compressive strength. Also, explainable machine learning (XML) methods were employed to elucidate the impact of mixed constituents on the compressive strength of GO concrete. The results from test predictions showcase that the XGB has attained a coefficient of determination (R 2 ) of 0.981, coefficient of correlation (R) of 0.99, mean square error (MSE) of 0.9, mean normalised bias (MNB) of 0.004, scatter index (SI) of 0.2 with mean absolute error (MAE) of 0.8 MPa for the predictions, outperforming the remaining models. XML highlighted the true impact of GO and the remaining variables, emphasising that the presence of GO significantly improves compressive strength. The optimum GO content and optimum superplasticiser content observed from the experimental work agreed with results obtained from the XML analysis, showing the consistency of the explanation with the experimental results. This implies the superiority of using XML methods in concrete mix design applications in industry. • Compressive strength of GO-added concrete was predicted using XML. • XGB model achieved the highest accurate compressive strength predictions. • SHAP revealed the impact of GO on concrete. • Developed an XML-based predictive tool for GO-added concrete.