"Data Driven Design Of Ultra High Performance Concrete Prospects And Ap" by Bryan K. Aylas-Paredes, Taihao Han et al.
 

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.

Department(s)

Electrical and Computer Engineering

Second Department

Materials Science and Engineering

Third Department

Civil, Architectural and Environmental Engineering

Publication Status

Open Access

Comments

National Science Foundation, Grant 2034856

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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Dec 2025

Available for download on Monday, December 01, 2025

Plum Print visual indicator of research metrics
PlumX Metrics
  • Usage
    • Abstract Views: 1
  • Captures
    • Readers: 6
  • Mentions
    • News Mentions: 1
see details

Share

 
COinS