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10.3390/ma18225116
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Machine Learning-Driven Strength Prediction and Sustainability Analysis of Ultra-High-Performance Concrete

by
Hongliang Rong
1,
Wangwen Sun
2,*,
Haoran Ma
2,
Muhan Luo
2,
Zhenghua You
1,
Guobin Zhang
1,
Pengcheng Zhu
2,
Zhuangzhuang Liu
2,3,4,* and
Lauren Y. Gómez-Zamorano
5
1
Xinjiang Jiaotou Construction Management Co., Ltd., Urumchi 830000, China
2
School of Highway, Chang’an University, Xi’an 710064, China
3
Key Laboratory of Special Area Highway Engineering, Ministry of Education, Xi’an 710064, China
4
International Joint Laboratory for Sustainable Development of Highway Infrastructures in Special Regions, Xi’an 710064, China
5
Programa Doctoral en Ingeniería de Materiales, Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, Ave. Universidad s/n, Ciudad Universitaria, San Nicolás de los Garza 66455, Nuevo León, Mexico
*
Authors to whom correspondence should be addressed.
Materials 2025, 18(22), 5116; https://doi.org/10.3390/ma18225116
Submission received: 17 October 2025 / Revised: 6 November 2025 / Accepted: 9 November 2025 / Published: 11 November 2025
(This article belongs to the Special Issue Innovations in Sustainable Construction and Road Engineering Materials)
Versions Notes

Abstract

Ultra-high-performance concrete (UHPC) is recognized for its exceptional strength and durability. However, the adoption of UHPC frequently leads to higher material and environmental costs. Accurate prediction of compressive strength is crucial for optimizing material design and reducing construction costs. In this study, a dataset of 800 samples was compiled from published articles. Four models, including random forest (RF), Gaussian Process Regression (GPR), Gradient Boosting (GB) and Artificial Neural Network (ANN), were applied. Results show that ANN and GPR achieved the best accuracy and stability. GB also performed well with good adaptability. RF captured general trends but produced larger errors in the high-strength range. Feature importance analysis highlighted curing age and cement content as the most influential factors, with a combined contribution above 65%. The water-to-binder ratio also affected strength through matrix densification. Extended evaluation with regression error characteristic (REC) curves and environmental impact index (EII) revealed the balance between performance and environmental impact. Higher compressive strength often required higher energy, CO2, and resource use. The range of 150–250 MPa showed a better balance between performance and sustainability. This study confirms the robustness of machine learning models for strength prediction and provides guidance for green and low-carbon ultra-high-performance concrete design.
Keywords: ultra-high-performance concrete; machine learning models; compressive strength prediction; sustainability assessment; environmental impact ultra-high-performance concrete; machine learning models; compressive strength prediction; sustainability assessment; environmental impact

Share and Cite

MDPI and ACS Style

Rong, H.; Sun, W.; Ma, H.; Luo, M.; You, Z.; Zhang, G.; Zhu, P.; Liu, Z.; Gómez-Zamorano, L.Y. Machine Learning-Driven Strength Prediction and Sustainability Analysis of Ultra-High-Performance Concrete. Materials 2025, 18, 5116. https://doi.org/10.3390/ma18225116

AMA Style

Rong H, Sun W, Ma H, Luo M, You Z, Zhang G, Zhu P, Liu Z, Gómez-Zamorano LY. Machine Learning-Driven Strength Prediction and Sustainability Analysis of Ultra-High-Performance Concrete. Materials. 2025; 18(22):5116. https://doi.org/10.3390/ma18225116

Chicago/Turabian Style

Rong, Hongliang, Wangwen Sun, Haoran Ma, Muhan Luo, Zhenghua You, Guobin Zhang, Pengcheng Zhu, Zhuangzhuang Liu, and Lauren Y. Gómez-Zamorano. 2025. "Machine Learning-Driven Strength Prediction and Sustainability Analysis of Ultra-High-Performance Concrete" Materials 18, no. 22: 5116. https://doi.org/10.3390/ma18225116

APA Style

Rong, H., Sun, W., Ma, H., Luo, M., You, Z., Zhang, G., Zhu, P., Liu, Z., & Gómez-Zamorano, L. Y. (2025). Machine Learning-Driven Strength Prediction and Sustainability Analysis of Ultra-High-Performance Concrete. Materials, 18(22), 5116. https://doi.org/10.3390/ma18225116

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