Towards white box modeling of compressive strength of sustainable ternary cement concrete using explainable artificial intelligence (XAI)

Abstract

Since the production of sustainable ternary cement concrete (TCC) involves a large range of constituents which can affect the compressive strength (CS) of TCC in different ways, the evaluation of CS in a unified manner is necessary. Unlike the black box machine learning (ML) models, the predictions of CS of TCC using local explanations to global understanding with explainable artificial intelligence (XAI) models have been investigated in a systematic approach by means of SHapley Additive exPlanations (SHAP). For this, two conventional ML and three ensemble ML models were used. Hyper-parameter tuning was also carried out. The ensemble ML models had a higher accuracy than the conventional ML models. Taylor diagram, model evaluation parameters and the SHAP values were used to interpret machine learning model predictivity. From insights obtained through local explanations, it can be concluded that predictions made by black box models should be used carefully.