Machine-Learning-Based Prediction of the Compressive Strength of Superabsorbent Polymer-Incorporated Cementitious Materials: Modeling and Experimental Insights into Polymer Characteristics

This repository provides the Python code used in the study "Machine-Learning-Based Prediction of the Compressive Strength of Superabsorbent Polymer-Incorporated Cementitious Materials: Modeling and Experimental Insights into Polymer Characteristics". The code implements the M2 model for predicting the compressive strength of SAP-incorporated cementitious materials, and includes the CatBoost model (best-performing among the five ML methods evaluated) together with scripts to generate SHAP-based interpretability plots. For detailed information regarding the execution environment, installation, usage, and required libraries, please refer to the README.txt file. For comprehensive methodological details, experimental design, and model interpretation, please refer to the published journal paper.