Evaluating the relevance of eggshell and glass powder for cement-based materials using machine learning and SHapley Additive exPlanations (SHAP) analysis

This study used machine learning methods to predict the water absorption (W-A) of cement-based material (CBM) containing eggshell and glass powder as sand and cement substitutes. A dataset from the laboratory experiments consisting of 234 points and seven input variables was used to develop models, including multilayer perceptron neural network (MLPNN), support vector machine (SVM), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). Additionally, a SHapley Additive exPlanations (SHAP) analysis was performed to investigate the relevance and interaction of raw components. When evaluating the prediction models for the W-A of CBM, it was found that the MLPNN and SVM models were moderately accurate (R2 = 0.74 and 0.78, respectively), while the AdaBoost and XGBoost models showed good agreement with the lab test results (R2 = 0.86 and 0.91, respectively). The SHAP approach revealed that while the cement quantity had a higher negative association with W-A of CBM, the quantities of eggshell powder, sand, and glass powder showed both favourable and detrimental correlations. Therefore, eggshell and glass powder must be used in optimal proportions of around 60 kg/m3 and 80 kg/m3, respectively, for maximum resistance to W-A. The AdaBoost and XGBoost models can potentially compute the W-A of CBMs by utilising various input parameter values, which may help decrease unnecessary test trials in labs. Furthermore, the SHAP investigation revealed the impact and relationship of the inputs on the W-A of CBMs, which might potentially assist researchers and the industry in determining the appropriate amount of raw materials during CBM production.