Abstract
Ultra-high-performance concrete (UHPC) is a specialized class of cementitious composites that is increasingly used in various applications, including bridge decks, connections between precast components, piers, columns, overlays, and the repair and strengthening of bridge elements. The mechanical and durability properties of UHPC are significantly influenced by factors such as low water-to-binder ratios, the inclusion of supplementary cementitious materials (SCMs), and fiber reinforcement. Machine learning (ML) has been employed to predict the performance of UHPC and optimize its mixture designs by using various raw materials. This study first provides a comprehensive review of ML applications in UHPC, focusing on predicting workability, mechanical, and thermal properties. The use of data crossing, generative AI, physics-guided ML models, and field-applicable software are explored as practical directions for future research. This study also develops ML models to predict the compressive strength of UHPC by using a database containing 1300 data-records. The influence of various input variables is evaluated using SHapley Additive exPlanations (SHAP), revealing that chemical compositions have relatively minor impacts, given the material types used. By excluding insignificant variables, the models enhance both efficiency and accuracy in predicting strength. This advancement facilitates optimized material design and performance prediction while reducing the experimental workload required to inform ML models. Adding more diverse data to the database could further enhance the prediction performance and generalizability of the proposed ML models.
Recommended Citation
B. K. Aylas-Paredes et al., "Data Driven Design Of Ultra High Performance Concrete Prospects And Application," Scientific Reports, vol. 15, no. 1, article no. 9248, Nature Research, Dec 2025.
The definitive version is available at https://doi.org/10.1038/s41598-025-94484-2
Department(s)
Electrical and Computer Engineering
Second Department
Materials Science and Engineering
Third Department
Civil, Architectural and Environmental Engineering
Publication Status
Open Access
Keywords and Phrases
Artificial intelligence; Compressive strength; Feature selection; Supplementary cementitious materials; Ultra-high performance concrete
International Standard Serial Number (ISSN)
2045-2322
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2025
Included in
Ceramic Materials Commons, Civil and Environmental Engineering Commons, Electrical and Computer Engineering Commons, Structural Materials Commons
Comments
National Science Foundation, Grant 2034856