This study utilizes robust machine learning (ML) techniques to predict compressive strength of fly-ash-based geopolymer concrete (GPC), a sustainable replacement for traditional concrete. Popular ensemble models, specifically Random Forest (RF) and Extreme Gradient Boosting (XGB), were taken as base models and were hybridized using metaheuristic algorithms (Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) for hyperparameter optimization. A 5 × 5 nested cross-validation (nCV) approach was adopted, where inner folds were used for hyperparameter tuning, and outer folds for unbiased performance evaluation for the limited 273-sample dataset. The findings revealed that GWO-XGB outperformed other hybridized processes, with aggregated R2 and RMSE of 0.9661 ± 0.011 and 3.0401 ± 0.5363, respectively, in the testing phases. The performance ranking for both the training and testing phases was: GWO-XGB > PSO-XGB > GWO-RF > PSO-RF. Further, SHAP analysis was performed on models obtained from the best-tuning process, which identified Curing Period (CTP) and Curing Temperature (CTR) as the most critical parameters influencing FA-GPC strength. The best-optimized model was also used to build a graphical user interface (GUI). This work offers a reliable framework, not only for predicting CS but also for demonstrating how feature interactions contribute to strength development, providing insights into effective ML use in concrete technology.
Subodh Subedi, Ajaya Subedi, S. Adhikari et al.· Bulletin of Computational In...· 0 citations
The slant shear bond strength of the UHPC-NSC interface is a key parameter governing load transfer and structural reliability in composite concrete members. However, accurate prediction remains challenging because of strong nonlinear relationships among influencing parameters and the limited availability of experimental data. This study presents an optimized machine learning framework for reliable bond strength prediction by integrating Extra Trees Regressor (ETR) and CatBoost (CATB) with Grasshopper Optimization (GO) and Northern Goshawk Optimization (NG) for hyperparameter optimization. Model performance was evaluated using cross-validation and independent testing to ensure reliable generalization. Among the developed models, the optimized CATB-NG achieved the highest predictive accuracy with an R2 of 0.934, RMSE of 3.081 MPa, and MAE of 2.186 MPa. SHapley Additive exPlanations (SHAP) identified NSC surface treatment and compressive strength as the dominant factors influencing bond strength, while Individual Conditional Expectation (ICE) analysis revealed nonlinear feature interactions and threshold behaviors. To facilitate practical engineering applications, the optimized model was implemented in a graphical user interface (GUI) for real-time prediction with standardized feature encoding. The proposed framework provides an accurate, interpretable, and user-friendly tool for predicting UHPC-NSC interfacial bond strength and supports engineering design and decision making.
Sanjog Chhetri Sapkota, S. Adhikari, Nisha Panta et al.· Buildings· 0 citations