Machine Learning-Based Prediction and Mix Design Optimization of the Mechanical Properties of Engineered Cementitious Composites and Ultra-High-Performance Concrete: Toward Sustainable Construction

Mix design of engineered cementitious composites (ECC) and ultra-high-performance concrete (UHPC) requires accounting for nonlinear relationships between constituent proportions and mechanical properties. This study develops a literature-based framework combining strength prediction, model interpretation, and constrained mix design optimization. A database of 553 records, comprising 247 ECC and 306 UHPC records, was assembled with 14 input features and three strength responses. Interquartile-range screening retained 222 ECC and 292 UHPC records. Categorical features were one-hot encoded, and seven regression algorithms were evaluated using 50 repeated Monte Carlo cross-validation splits. XGBoost achieved reported mean R2 values of 0.971 and 0.900 for compressive and tensile strength, respectively, with corresponding mean absolute percentage errors of 7.560% and 9.424%. CatBoost achieved a mean R2 of 0.951 and a mean absolute percentage error of 15.636% for flexural strength. SHapley Additive exPlanations identified material-dependent associations between mix parameters and predicted strength, particularly for silica fume, cement, and fiber characteristics. The selected predictors were incorporated into a graphical interface and coupled with the nondominated sorting genetic algorithm II to search for mixtures satisfying strength targets and database-derived constraints. In addition to predictive accuracy, the framework contributes to sustainable construction: it predicts the strength of candidate mixtures and optimizes their proportions against strength targets, so that the combinations most worth casting are identified first; this can reduce the number of trial batches required and thereby lower raw-material consumption and cement demand. The better-performing, more durable mixtures it identifies also help to extend service life and reduce the embodied carbon of ECC and UHPC production. The framework supports preliminary screening within the investigated domain; independent experimental validation of the optimized mixtures remains necessary.