Abstract
Concrete is the most extensively used construction material, and cement is its main component. Hybrid machine learning models attract researchers in building materials due to their high applications and prediction accuracy. Hybrid machine learning model interpretability is crucial to apply to the interest of field experts. Therefore, this research study proposes to predict the compressive strength of high-strength concrete (HSC) using supervised HML algorithms (XGBR-BR, SVR-RFR, and GBR-DTR), which is rarely seen in the literature. Additionally, SHAP—a novel black-box interpretation approach—was employed to input variables and elucidate the predictions. The comparison revealed that all selected hybrid ML models provide acceptable accuracy for compressive strength predictions. Moreover, the XGBoost-BR model exhibited superior performance \({R}^{2}\)= 0.99 for the training phase and \({R}^{2}\)= 0.96 for the testing phase. The average error ranges were found to be very closely approximate ± 5. The predictions of the XGBoost-BR model capture significant correlations between the input variables based on the interpretation of SHAP. On the other hand, SHAP offers consistent measurements of a feature’s significance and a variable’s influence on a prediction. It is interesting to note that the SHAP interpretations matched what is typically observed in the compressive behavior of concrete, verifying the causality of the hybrid ML predictions. Applying hybrid machine learning techniques to predict concrete’s compressive strength will benefit the area of civil engineering application.
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AK: supervision, conceptualization, writing—original draft, data curation, data analysis, software, methodology, writing—review and editing. PD: conceptualization, writing, methodology, software, formal analysis, writing—review and editing.
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Kashem, A., Das, P. Compressive strength prediction of high-strength concrete using hybrid machine learning approaches by incorporating SHAP analysis. Asian J Civ Eng 24, 3243–3263 (2023). https://doi.org/10.1007/s42107-023-00707-0
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DOI: https://doi.org/10.1007/s42107-023-00707-0