Ultra-high performance concrete compressive strength prediction using machine learning boosting algorithms

Concrete with Ultra-High Performance (UHPC) is a next-generation cement-based material known for its ultra-high compressive strength, enhanced ductility, and extreme durability. By eliminating coarse aggregates and incorporating optimized fine particle packing, UHPC achieves strengths above 150 MPa....

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Veröffentlicht in:	Asian journal of civil engineering. Building and housing Jg. 26; H. 12; S. 5139 - 5154
Hauptverfasser:	Brahmeswari, L. Chandana, Rao, B. D. V. Chandra Mohan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Building Materials Cement Civil Engineering Compressive strength Concrete Concrete properties Critical infrastructure Datasets Deep learning Ductility Durability Engineering Fiber reinforcement Fuzzy logic Machine learning Neural networks Pozzolans Reinforced concrete Service life Shear strength Silica fume Steel fibers Sustainable Architecture/Green Buildings Tensile strength Ultra high performance concrete score R Normalized mean square error (NMSE) Gradient boosting algorithm (GB) Ultra-high performance concrete (UHPC) Compressive strength Extreme gradient boosting algorithm (XGBoost)
ISSN:	1563-0854, 2522-011X
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Abstract	Concrete with Ultra-High Performance (UHPC) is a next-generation cement-based material known for its ultra-high compressive strength, enhanced ductility, and extreme durability. By eliminating coarse aggregates and incorporating optimized fine particle packing, UHPC achieves strengths above 150 MPa. The synergy of low water-to-binder ratio, high-reactivity pozzolans like silica fume, and steel fiber reinforcement contributes to its exceptional mechanical and durability performance. UHPC offers self-consolidation, reduced permeability, and superior resistance to aggressive environments, making it ideal for critical infrastructure. Its ability to outperform traditional concrete in both structural and service life applications is transforming the future of construction. This study examines how well two sophisticated machine learning boosting models, Gradient Boosting algorithm (GB) and Extreme Gradient Boosting algorithm (XGBoost) performs while predicting the ultra-high performance concrete’s compressive strength. To evaluate this potential, a dataset consisting of 110 experimental results, compiled from existing literature, was employed to test and train the models. The GB model achieved a R² of 0.960 and Normalized Mean Square Error, NMSE of 0.041 on the train data and R² of 0.727 and NMSE of 0.452 on test data. The XGBoost model achieved a R² of 0.961 and NMSE of 0.039 on the train data and R² of 0.840 and NMSE of 0.160 on test data. These results demonstrate that XGBoost and GB, both have excellent predictive accuracy in modeling UHPC compressive strength and shown a significant improvement over the existing literature, Omar R. Abuodeh (2020). Overall, this research confirms that leveraging GB and XGBoost significantly enhances model performance and offers valuable insights into the compressive strength behavior of UHPC.
AbstractList	Concrete with Ultra-High Performance (UHPC) is a next-generation cement-based material known for its ultra-high compressive strength, enhanced ductility, and extreme durability. By eliminating coarse aggregates and incorporating optimized fine particle packing, UHPC achieves strengths above 150 MPa. The synergy of low water-to-binder ratio, high-reactivity pozzolans like silica fume, and steel fiber reinforcement contributes to its exceptional mechanical and durability performance. UHPC offers self-consolidation, reduced permeability, and superior resistance to aggressive environments, making it ideal for critical infrastructure. Its ability to outperform traditional concrete in both structural and service life applications is transforming the future of construction. This study examines how well two sophisticated machine learning boosting models, Gradient Boosting algorithm (GB) and Extreme Gradient Boosting algorithm (XGBoost) performs while predicting the ultra-high performance concrete’s compressive strength. To evaluate this potential, a dataset consisting of 110 experimental results, compiled from existing literature, was employed to test and train the models. The GB model achieved a R² of 0.960 and Normalized Mean Square Error, NMSE of 0.041 on the train data and R² of 0.727 and NMSE of 0.452 on test data. The XGBoost model achieved a R² of 0.961 and NMSE of 0.039 on the train data and R² of 0.840 and NMSE of 0.160 on test data. These results demonstrate that XGBoost and GB, both have excellent predictive accuracy in modeling UHPC compressive strength and shown a significant improvement over the existing literature, Omar R. Abuodeh (2020). Overall, this research confirms that leveraging GB and XGBoost significantly enhances model performance and offers valuable insights into the compressive strength behavior of UHPC. Concrete with Ultra-High Performance (UHPC) is a next-generation cement-based material known for its ultra-high compressive strength, enhanced ductility, and extreme durability. By eliminating coarse aggregates and incorporating optimized fine particle packing, UHPC achieves strengths above 150 MPa. The synergy of low water-to-binder ratio, high-reactivity pozzolans like silica fume, and steel fiber reinforcement contributes to its exceptional mechanical and durability performance. UHPC offers self-consolidation, reduced permeability, and superior resistance to aggressive environments, making it ideal for critical infrastructure. Its ability to outperform traditional concrete in both structural and service life applications is transforming the future of construction. This study examines how well two sophisticated machine learning boosting models, Gradient Boosting algorithm (GB) and Extreme Gradient Boosting algorithm (XGBoost) performs while predicting the ultra-high performance concrete’s compressive strength. To evaluate this potential, a dataset consisting of 110 experimental results, compiled from existing literature, was employed to test and train the models. The GB model achieved a R² of 0.960 and Normalized Mean Square Error, NMSE of 0.041 on the train data and R² of 0.727 and NMSE of 0.452 on test data. The XGBoost model achieved a R² of 0.961 and NMSE of 0.039 on the train data and R² of 0.840 and NMSE of 0.160 on test data. These results demonstrate that XGBoost and GB, both have excellent predictive accuracy in modeling UHPC compressive strength and shown a significant improvement over the existing literature, Omar R. Abuodeh (2020). Overall, this research confirms that leveraging GB and XGBoost significantly enhances model performance and offers valuable insights into the compressive strength behavior of UHPC.
Author	Brahmeswari, L. Chandana Rao, B. D. V. Chandra Mohan
Author_xml	– sequence: 1 givenname: L. Chandana surname: Brahmeswari fullname: Brahmeswari, L. Chandana organization: Department of Civil Engineering, VNR Vignana Jyothi Institute of Engineering and Technology – sequence: 2 givenname: B. D. V. Chandra Mohan surname: Rao fullname: Rao, B. D. V. Chandra Mohan email: bdvcmrao@gmail.com organization: Department of Civil Engineering, VNR Vignana Jyothi Institute of Engineering and Technology
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SubjectTerms	Accuracy Algorithms Building Materials Cement Civil Engineering Compressive strength Concrete Concrete properties Critical infrastructure Datasets Deep learning Ductility Durability Engineering Fiber reinforcement Fuzzy logic Machine learning Neural networks Pozzolans Reinforced concrete Service life Shear strength Silica fume Steel fibers Sustainable Architecture/Green Buildings Tensile strength Ultra high performance concrete
Title	Ultra-high performance concrete compressive strength prediction using machine learning boosting algorithms
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