Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

Zhongjie Shen; Ahmed Farouk Deifalla; Paweł Kamiński; Artur Dyczko

doi:10.3390/ma15103523

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

Materials (Basel). 2022 May 13;15(10):3523. doi: 10.3390/ma15103523.

Authors

Zhongjie Shen¹, Ahmed Farouk Deifalla², Paweł Kamiński³, Artur Dyczko⁴

Affiliations

¹ Xijing University, Xi'an 710123, China.
² Structural Engineering and Construction Management Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt.
³ Faculty of Civil Engineering and Resource Management, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland.
⁴ Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, J. Wybickiego 7a, 31-261 Kraków, Poland.

Abstract

In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The model's performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R² (0.90) and low errors, was more accurate than the other algorithms, which had a lower R². The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study's findings.

Keywords: UHSC; building material; compressive strength; concrete; soft computing technique.

Grants and funding

This research received no external funding.