Fly ash is widely recognized as an effective cement replacement material for reducing the environmental impact of concrete production while maintaining acceptable mechanical performance. This study develops an explainable artificial intelligence-based multi-objective framework for the sustainable design of fly ash concrete by combining predictive modeling, feature interpretation, uncertainty assessment, and optimization within a single workflow. A compiled dataset of 1,062 concrete mix samples was used, from which outlier detection was applied only to the training subset, resulting in 58 anomalous samples being removed from the training data while the independent test set remained unchanged. Four artificial intelligence models, namely TabNet, SVR, NGBoost, and SAINT, were trained and compared for predicting 28-day compressive strength. Among them, NGBoost achieved the best testing performance, with RMSE = 4.897, MAE = 3.492, MAPE = 7.844, and R² = 0.92. Feature interpretation showed that cement content, water content, and MCS-28 were the most influential variables governing strength prediction. To further evaluate model reliability, a bootstrap-based uncertainty analysis was conducted, showing that TabNet and NGBoost exhibited comparatively narrower uncertainty bounds. The best-performing predictive model was then coupled with four nature-inspired optimization algorithms to generate concrete mix designs that maximize compressive strength while minimizing CO₂ footprint. The optimization results demonstrated that the proposed framework identifies theoretical Pareto-optimal candidate mixtures within the dataset domain, highlighting its potential as a data-driven decision-support tool for sustainable concrete mix design. The optimized mixtures are computational Pareto-optimal solutions and not experimentally validated production mixes. A limitation of this study is the lack of source-level labels in the compiled dataset, which prevented grouped validation across original studies. Future research may incorporate additional practical constraints, such as water-to-binder ratio and workability requirements, and extend the framework to larger and more diverse datasets.

Accurate estimation of flow discharge, Q, through hydraulic structures such as spillways and gates is of great importance in water resources engineering. Each hydraulic structure, due to its unique characteristics, requires a specific and comprehensive study. In this regard, the present study innovatively focuses on predicting Q through Rectangular Top-Hinged Gates (RTHGs) using advanced Gradient Boosting (GB) models. The GB models evaluated in this study include Categorical Boosting (CatBoost), Histogram-based Gradient Boosting (HistGBoost), Light Gradient Boosting Machine (LightGBoost), Natural Gradient Boosting (NGBoost), and Extreme Gradient Boosting (XGBoost). One of the essential factors in developing artificial intelligence models is the accurate and proper tuning of their hyperparameters. Therefore, four powerful metaheuristic algorithms—Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA)—were evaluated and compared for hyperparameter tuning, using LightGBoost as the baseline model. An assessment of error metrics, convergence speed, stability, and computational cost revealed that SSA achieved the best performance for the hyperparameter optimization of GB models. Consequently, hybrid models combining GB algorithms with SSA were developed to predict Q through RTHGs. Random split was used to divide the dataset into two sets, with 70% for training and 30% for testing. Prediction uncertainty was quantified via Confidence Intervals (CI) and the R-Factor index. CatBoost-SSA produced the most accurate prediction performance among the models (R² = 0.999 training, 0.984 testing), and NGBoost-SSA provided the lowest uncertainty (CI = 0.616, R-Factor = 3.596). The SHapley Additive exPlanations (SHAP) method identified h/B (upstream water depth to channel width ratio) and channel slope, S, as the most influential predictors. Overall, this study confirms the effectiveness of SSA-optimized boosting models for reliable and interpretable hydraulic modeling, offering a robust tool for the design and operation of gated flow control systems.

The online version contains supplementary material available at 10.1038/s41598-026-50458-6.