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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Vydáno v:	Asian journal of civil engineering. Building and housing Ročník 26; číslo 12; s. 5139 - 5154
Hlavní autoři:	Brahmeswari, L. Chandana, Rao, B. D. V. Chandra Mohan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V
Témata:	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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References	A Alsalman (1476_CR5) 2017; 153 1476_CR23 A Kaveh (1476_CR27) 2022; 66 BB Adhikary (1476_CR3) 2006; 20 Y Yua (1476_CR44) 2018; 184 A Kaveh (1476_CR24) 2023; 52 Z Waszczyszyn (1476_CR42) 2001; 79 SSA Kumar (1476_CR30) 2025; 26 SR Wani (1476_CR41) 2025; 26 JA Abdalla (1476_CR1) 2007; 334 T Bansal (1476_CR8) 2024; 25 F Alsharari (1476_CR6) 2025; 26 M Hassan (1476_CR18) 2017; 144 MG Sohail (1476_CR36) 2018; 30 K Habel (1476_CR15) 2006; 36 OR Abuodeh (1476_CR2) 2020; 95 K Wille (1476_CR43) 2015; 86 HM Tanarslan (1476_CR38) 2012; 30 M Shafieifar (1476_CR35) 2017; 156 SC Sapkota (1476_CR33) 2025; 160 C George (1476_CR10) 2025; 26 1476_CR11 BA Graybeal (1476_CR13) 2007; 104 M Alkaysi (1476_CR4) 2017; 144 A Kaveh (1476_CR26) 2017; 7 J Kasperkiewicz (1476_CR22) 1995; 9 G Tayfur (1476_CR39) 2014; 26 MN Hadi (1476_CR16) 2003; 81 AW Oreta (1476_CR32) 2004; 26 A Satyanarayana (1476_CR34) 2024; 25 RK Tipu (1476_CR40) 2023; 24 P Kumar (1476_CR28) 2024; 25 DK Bui (1476_CR9) 2018; 180 CTG Awodiji (1476_CR7) 2018; 4 N Islam (1476_CR20) 2024; 25 DL Nguyen (1476_CR31) 2024; 25 M Gupta (1476_CR14) 2024; 25 A Kaveh (1476_CR25) 2018; 62 R Zhong (1476_CR45) 2018; 160 AMT Hassan (1476_CR17) 2012; 37 M Ghrici (1476_CR12) 2017; 109 P Hosseini (1476_CR19) 2023; 13 HO Jang (1476_CR21) 2017; 152 SH Subramanya (1476_CR37) 2025; 26 R Kumar (1476_CR29) 2025; 2025
References_xml	– volume: 26 start-page: 1683 year: 2025 ident: 1476_CR30 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-025-01277-z – volume: 160 start-page: 505 year: 2018 ident: 1476_CR45 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2017.11.049 – volume: 66 start-page: 18 issue: 1 year: 2022 ident: 1476_CR27 publication-title: Periodica Polytechnica Civil Engineering doi: 10.3311/PPci.18901 – volume: 160 start-page: 1 year: 2025 ident: 1476_CR33 publication-title: Multiscale and Multidisciplinary Modeling Experiments and Design doi: 10.1007/s41939-025-00737-y – volume: 334 start-page: 741 issue: 5 year: 2007 ident: 1476_CR1 publication-title: Journal of the Franklin Institute doi: 10.1016/j.conbuildmat.2018.05.201 – volume: 25 start-page: 5865 year: 2024 ident: 1476_CR34 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-024-01151-4 – volume: 52 start-page: 256 year: 2023 ident: 1476_CR24 publication-title: Structures doi: 10.1016/j.istruc.2023.03.178 – volume: 153 start-page: 918 year: 2017 ident: 1476_CR5 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2017.07.158 – volume: 144 start-page: 412 year: 2017 ident: 1476_CR4 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2017.03.091 – volume: 25 start-page: 5741 year: 2024 ident: 1476_CR14 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-024-01142-5 – volume: 109 start-page: 542 year: 2017 ident: 1476_CR12 publication-title: Cement and Concrete Composites doi: 10.1016/j.cemconcomp.2007.04.009 – volume: 26 start-page: 373 year: 2025 ident: 1476_CR41 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-024-01195-6 – volume: 184 start-page: 229 year: 2018 ident: 1476_CR44 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2018.06.219 – volume: 95 start-page: 106552 year: 2020 ident: 1476_CR2 publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106552 – volume: 25 start-page: 1195 year: 2024 ident: 1476_CR8 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-023-00836-6 – volume: 152 start-page: 16 year: 2017 ident: 1476_CR21 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2017.06.156 – volume: 37 start-page: 874 year: 2012 ident: 1476_CR17 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2012.04.030 – volume: 30 start-page: 556 year: 2012 ident: 1476_CR38 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2011.12.008 – ident: 1476_CR11 doi: 10.1533/9780857099891.61 – volume: 144 start-page: 747 year: 2017 ident: 1476_CR18 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2017.03.185 – volume: 104 start-page: 146 year: 2007 ident: 1476_CR13 publication-title: ACI Materials Journal doi: 10.14359/18577 – volume: 24 start-page: 2985 year: 2023 ident: 1476_CR40 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-023-00689-z – volume: 180 start-page: 320 year: 2018 ident: 1476_CR9 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2018.05.201 – volume: 4 start-page: 3005 issue: 12 year: 2018 ident: 1476_CR7 publication-title: Civil Engineering Journal doi: 10.28991/cej-03091216 – volume: 2025 start-page: 1 year: 2025 ident: 1476_CR29 publication-title: Structural Concrete doi: 10.1002/suco.70178 – volume: 26 start-page: 681 issue: 5 year: 2004 ident: 1476_CR32 publication-title: Engineering Structures doi: 10.1016/j.engstruct.2004.01.009 – volume: 36 start-page: 1362 year: 2006 ident: 1476_CR15 publication-title: Cement and Concrete Research doi: 10.1016/j.cemconres.2006.03.009 – volume: 7 start-page: 48 issue: 1 year: 2017 ident: 1476_CR26 publication-title: International Journal of Modeling and Optimization doi: 10.7763/IJMO.2017.V7.557 – volume: 26 start-page: 04014079 issue: 11 year: 2014 ident: 1476_CR39 publication-title: Journal of Materials in Civil Engineering doi: 10.1061/(ASCE)MT.1943-5533.000098 – volume: 26 start-page: 3533 year: 2025 ident: 1476_CR37 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-025-01386-9 – volume: 25 start-page: 327 year: 2024 ident: 1476_CR20 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-023-00778-z – volume: 26 start-page: 667 year: 2025 ident: 1476_CR10 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-024-01213-7 – volume: 62 start-page: 281 issue: 2 year: 2018 ident: 1476_CR25 publication-title: Periodica Polytechnica Civil Engineering doi: 10.3311/PPci.10799 – volume: 25 start-page: 3363 year: 2024 ident: 1476_CR31 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-023-00984-9 – volume: 13 start-page: 457 issue: 4 year: 2023 ident: 1476_CR19 publication-title: International Journal of Optimization in Civil Engineering doi: 10.22068/ijoce.2023.13.4.566 – volume: 25 start-page: 723 year: 2024 ident: 1476_CR28 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-023-00807-x – volume: 30 start-page: 1 issue: 4 year: 2018 ident: 1476_CR36 publication-title: Journal of Materials in Civil Engineering doi: 10.1061/(ASCE)MT.1943-5533.0002144 – volume: 26 start-page: 179 year: 2025 ident: 1476_CR6 publication-title: Asian Journal of Civil Engineering doi: 10.1007/s42107-024-01183-w – volume: 9 start-page: 279 issue: 4 year: 1995 ident: 1476_CR22 publication-title: Journal of Computing in Civil Engineering doi: 10.1061/(ASCE)0887-3801 – volume: 156 start-page: 402 year: 2017 ident: 1476_CR35 publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2017.08.170 – volume: 79 start-page: 2261 year: 2001 ident: 1476_CR42 publication-title: Computers & Structures doi: 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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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