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Volume 16
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10.3390/buildings16010204
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Integration of Machine Learning Models and Tiering Technique in Predicting the Compressive Strength of FRP-Strengthened Circular Concrete Columns

by
Anh Duc Pham
1,
Quynh Chau Truong
1,
Quang Trung Nguyen
1,
Cong Luyen Nguyen
2,
Thi Thao Nguyen Nguyen
1 and
Anh Duc Mai
1,*
1
Construction Management Division, The University of Da Nang—University of Science and Technology, Da Nang 550000, Vietnam
2
Construction Informatics Division, The University of Da Nang—University of Science and Technology, Da Nang 550000, Vietnam
*
Author to whom correspondence should be addressed.
Buildings 2026, 16(1), 204; https://doi.org/10.3390/buildings16010204
Submission received: 27 November 2025 / Revised: 18 December 2025 / Accepted: 23 December 2025 / Published: 2 January 2026
(This article belongs to the Special Issue Structural Health Monitoring Through Advanced Artificial Intelligence)
Versions Notes

Abstract

This study aims to investigate the performance of the combined machine learning (ML) models and tiering technique for predicting the compressive strength of FRP-strengthened circular concrete columns. A dataset consisting of 725 experimental results has been assembled from available research studies to evaluate the prediction models. Pearson’s correlation analysis has been carried out to investigate the relationship between seven input parameters and the target parameter. The Taylor diagram has been plotted to deter-mine the best design-oriented strength model. The prediction performance of the combined ML models and tiering technique was compared with that of single ML models and ten design-oriented strength models. The research outcomes revealed that applying the tiering technique significantly improved the prediction accuracy of the ML models. It was also found that the best ML model for predicting the compressive strength of FRP-strengthened circular concrete columns was the combined random forest model and tiering technique, which outperformed single ML and design-oriented strength models.
Keywords: FRP-strengthened concrete; CFRP fiber; predicting the compressive strength of FRP-strengthened concrete; compressive strength of FRP-strengthened concrete; machine learning model FRP-strengthened concrete; CFRP fiber; predicting the compressive strength of FRP-strengthened concrete; compressive strength of FRP-strengthened concrete; machine learning model

Share and Cite

MDPI and ACS Style

Pham, A.D.; Truong, Q.C.; Nguyen, Q.T.; Nguyen, C.L.; Nguyen, T.T.N.; Mai, A.D. Integration of Machine Learning Models and Tiering Technique in Predicting the Compressive Strength of FRP-Strengthened Circular Concrete Columns. Buildings 2026, 16, 204. https://doi.org/10.3390/buildings16010204

AMA Style

Pham AD, Truong QC, Nguyen QT, Nguyen CL, Nguyen TTN, Mai AD. Integration of Machine Learning Models and Tiering Technique in Predicting the Compressive Strength of FRP-Strengthened Circular Concrete Columns. Buildings. 2026; 16(1):204. https://doi.org/10.3390/buildings16010204

Chicago/Turabian Style

Pham, Anh Duc, Quynh Chau Truong, Quang Trung Nguyen, Cong Luyen Nguyen, Thi Thao Nguyen Nguyen, and Anh Duc Mai. 2026. "Integration of Machine Learning Models and Tiering Technique in Predicting the Compressive Strength of FRP-Strengthened Circular Concrete Columns" Buildings 16, no. 1: 204. https://doi.org/10.3390/buildings16010204

APA Style

Pham, A. D., Truong, Q. C., Nguyen, Q. T., Nguyen, C. L., Nguyen, T. T. N., & Mai, A. D. (2026). Integration of Machine Learning Models and Tiering Technique in Predicting the Compressive Strength of FRP-Strengthened Circular Concrete Columns. Buildings, 16(1), 204. https://doi.org/10.3390/buildings16010204

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