Article 2025 · 5 atıf

Optimized machine learning models for predicting ultra-high-performance concrete compressive strength: a hyperopt-based approach

Akarsu, Oguzhan, Aydin, Abdulkadir Cueneyt

Özet

Predicting Ultra-High-Performance Concrete (UHPC) compressive strength using advanced machine learning (ML) techniques represents a paradigm shift in material science. This study investigates the application of nine ML models, including CatBoost, Extra Trees Regressor, and XGBoost, optimized through the Hyperopt framework to enhance predictive accuracy. A comprehensive dataset comprising 810 observations was employed, and critical performance metrics such as R2, RMSE, a20-index, Nash-Sutcliffe Efficiency (NSE), and sMAPE(Symmetric Mean Absolute Percentage Error) were analyzed to assess model efficacy. The CatBoost model, optimized using the Hyperopt framework, demonstrated superior predictive performance, achieving an R2 of 0.9742, RMSE of 6.20 MPa, a20-index of 1.0, NSE of 0.9742, and sMAPE of 3.6445 on the test set. Feature interpretability was further advanced using SHAP and Partial Dependence Plots (PDP), which identified Age, Fiber Content (Fi), and Cement-to-Water Ratio (C/W) as the most influential predictors. These tools elucidated complex nonlinear relationships and provided actionable insights for optimizing UHPC mix designs. This research highlights the transformative potential of ML in reducing experimental overhead, promoting sustainable construction practices, and enhancing material performance. While the study focused on steel-fiber-reinforced UHPC under controlled conditions, future research should explore diverse fiber types, environmental factors, and dynamic material behaviors. By advancing the integration of ML with experimental methodologies, this study paves the way for innovative solutions in high-performance concrete applications.

Anahtar Kelimeler

CatBoost Machine learning Hyperparameter optimization Concrete mixture SHAP Partial dependence plots RANDOM FORESTS TREES

WoS Kategorileri

Engineering, Multidisciplinary Engineering, Mechanical Materials Science, Multidisciplinary

Sürdürülebilir Kalkınma Amaçları

Sorumlu Üretim ve Tüketim

Künye

Dergi: MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN
Yıl: 2025
Cilt: 8
Sayı: 8
ISSN: 2520-8160
eISSN: 2520-8179
DOI: 10.1007/s41939-025-00957-2
WoS UID: WOS:001522799100001

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