This study investigates the prediction of carbon emissions from fly ash and ground granulated blast furnace slag-based geopolymer concrete (GPC) using advanced ensemble machine learning (ML) techniques. Although ML has been extensively utilized to model GPC’s mechanical performance, its application in estimating environmental impacts, specifically carbon emissions, is limited. The research employs six ensemble ML models, such as random forest, gradient boosting, extreme gradient boosting (XGB), CatBoost, and light gradient boosting machine (LGBM), including versions optimized using ant colony optimization (ACO). Among them, the ACO-enhanced XGB model demonstrated the highest predictive accuracy with a coefficient of determination (R²) of 0.97, with low prediction errors (MAE = 3.92, RMSE = 6.17). However, cross-validation and uncertainty analyses indicate that the performance differences among top models are relatively small. Conversely, LGBM exhibited the least predictive reliability. Feature importance analysis revealed that curing parameters, specifically initial curing time, curing temperature, and the dosage of dry sodium hydroxide, had the most influence on carbon emissions. To evaluate model robustness and interpretability, Monte Carlo simulation and Gaussian white noise analyses were conducted. Results confirmed that CatBoost and ACO–gradient boosting (ACO-GB) demonstrated greater stability under varying and noisy conditions, whereas XGB-based models, although highly accurate, were comparatively more sensitive to input variability. Overall, the research establishes a data-driven, efficient framework for quantifying carbon emissions in GPC, highlighting the importance of evaluating both predictive accuracy and model robustness, advancing sustainable material design through intelligent modelling.

The deterioration of steel reinforcement through corrosion triggers cracking and loss of concrete cover, ultimately weakening the structure’s strength and ductility. In practical design and assessment, it is vital to precisely quantify the shear capacity of corroded reinforced concrete beams (CRCBs). In this paper, machine learning (ML) models are developed to predict the shear capacity of CRCBs, including kernel ridge regression (KRR), K-nearest neighbors (KNN), decision trees (DT), random forest (RF), gradient-boosted regression trees (GBRT), and extreme gradient boosting (XGBoost). A total of 408 data entries on the shear strength of CRCBs under different corrosion conditions were collected to establish an extensive database. The reliability of the proposed ML models is examined by contrasting their outputs with the experimental data. The XGBoost model demonstrated superior predictive capability, achieving an R² value of 0.994 and outperforming all other tested models, including RF, GBRT, and DT. The Shapley Additive Explanations (SHAP) algorithm is adopted to reveal the contribution of each input feature to the predicted shear capacity of CRCBs. The interpretive SHAP results show that the ultimate shear capacity of CRCBs is most influenced by beam depth (h), with the shear span-to-depth ratio (λ) and concrete compressive strength ((Formula presented.)) being the subsequent key contributors. A comparative assessment between the XGBoost model and traditional analytical models was carried out to estimate the shear strength of CRCBs. Results demonstrate that the XGBoost model delivers enhanced predictive accuracy and improved performance. A parametric investigation examined its robustness under variations in geometry and material properties, while a user-friendly interface was created to support its practical use.