Synergistic effects of supplementary cementitious materials and compressive strength prediction of concrete using machine learning algorithms with SHAP and PDP analyses

In order to reduce the CO2 associated with cement production, this study explored the potential of rice husk ash (RHA) and fly ash (FA) as supplementary cementitios materials for partially replacing cement in concrete production. The study aimed to analyze the synergistic effects of a cement-based mixture consisting of RHA and FA in different proportions on concrete's fresh, hardened, non-destructive and microscopic properties. In addition to the experimental work, this study successfully applied machine learning to predict the compressive strength of RHA-FA concrete using three types of algorithms: ANN (Analytical Neural Network), XGB (Extreme Gradient Boosting), and GBM (Gradient Boosting Model). A total of 138 data points were used for this prediction, and statistical and parametric analyses were performed to define the impact of input parameters on the outcome. Furthermore, both destructive and non-destructive tests were conducted on hardened concrete, including compressive strength, split tensile strength, ultrasonic pulse velocity (UPV), and rebound hammer. The scanning electron microscope (SEM) was operated to analyze the microstructural characteristics of concrete. The compressive and split-tensile strength test results showed that the mixture with a higher percentage of fly ash and a lower percentage of rice husk ash achieved maximum strength. The X-ray Fluorescence (XRF) analysis revealed that both ashes contained a significant amount of silica, which gave them excellent pozzolanic properties. After 28 days, both the UPV and the rebound hammer strength align with the destructive compressive strength results. The study also employed SHAP (SHapley Additive exPlanations) and PDP (Partial Dependence Plot) analyses to identify the optimal range for each parameter's contribution to strength improvement. The machine learning models exhibited a strong correlation with the test results, achieving an R2 value of 0.84 for the XGBoost model.