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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Bibliographic Details
Published in:Asian journal of civil engineering. Building and housing Vol. 26; no. 12; pp. 5139 - 5154
Main Authors: Brahmeswari, L. Chandana, Rao, B. D. V. Chandra Mohan
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2025
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1563-0854
2522-011X
DOI:10.1007/s42107-025-01476-8