ConcreteML: A machine learning software for predicting concrete compressive strength with explainable AI

Accurate prediction of concrete compressive strength is essential for structural safety and sustainable infrastructure development. Yet, conventional empirical approaches such as water-cement ratio laws and ACI/Eurocode formulations cannot fully capture the nonlinear interactions among modern concrete constituents, particularly supplementary cementitious materials and chemical admixtures. This paper presents ConcreteML, an open-source desktop application employing ensemble machine learning with engineering-informed compositional descriptors for interpretable and high-accuracy strength prediction. The framework integrates five machine learning algorithms (Random Forest, Gradient Boosting, XGBoost, CatBoost, and Neural Networks) trained on a multi-source dataset of 5186 concrete mixtures covering conventional, high-performance, and blended-cement concretes with curing ages from 1 to 365 days. The model incorporates 16 engineered descriptors, including water-cement ratio, total binder content, SCM percentage, and aggregate proportions, to transform raw mixture data into mechanistically meaningful inputs. The production XGBoost model achieved R² = 0.923, RMSE = 4.48 MPa, and MAE = 2.85 MPa on held-out test data, while 5-fold cross-validation (CV R² = 0.910 ± 0.008) confirmed its generalization. SHAP analysis provided transparent feature attribution, identifying age, total binder content, and water-cement ratio as the dominant predictors of strength development. ConcreteML includes a graphical user interface supporting single predictions, batch CSV/Excel processing, automated explainability visualizations, and standalone deployment without programming requirements.By combining predictive accuracy, explainability, and engineering usability, ConcreteML provides an accessible tool for quality control, mixture optimization, and data-driven concrete design.