Application of machine learning models in predicting the compressive strength of GFRP-confined concrete
Abstract: 158
| 
PDF: 95 
##plugins.themes.academic_pro.article.main##
Author
Keywords:
GFRP-confined concrete
Compressive strength of GFRP-confined concrete
Machine learning models
Predicting the compressive strength of GFRP confined concrete
Strengthening RC structures
Abstract
Accurately predicting the compressive strength of GFRP-confined concrete plays a key role in the use of GFRP in strengthening reinforced concrete structures. This study predicts the compressive strength of GFRP-confined concrete using five machine learning (ML) models. The performance of the ML models was compared with that of the design-oriented stress-strain model suggested by ACI 440.2R-17 for FRP-confined concrete. A dataset of 167 test results of GFRP-confined concrete was used for training and testing the ML models. It has been found that the predicted results obtained from the ML models were in good agreement with the test results. Furthermore, the ML models outperformed the design-oriented stress-strain model in predicting the compressive strength of GFRP-confined concrete. Among the machine learning models, the RandomTree and RandomForest models demonstrated the highest prediction accuracy.
References
-
[1] A. D. Mai, M. N. Sheikh, Q. T. Nguyen, A. D. Pham, and M. N. S. Hadi, "A new approach for the strength interaction diagram of FRP strengthened square steel reinforced high strength concrete columns”, Struct. Concr., vol. 25, no. 1, pp. 637-658, 2023. https://doi.org/10.1002/suco.202300216.
[2] J. Zeng, Y. Guo, L. Li, and W. Chen, "Behavior and three-dimensional finite element modeling of circular concrete columns partially wrapped with FRP strips”, Polymers, vol. 10, no. 3, 2018, no. 253. https://doi.org/10.3390/polym10030253.
[3] M. Z. Naser, R. A. Hawileh, and J. A. Abdalla, "Fiber-reinforced polymer composites in strengthening reinforced concrete structures: A critical review”, Eng. Struct., vol. 198, p. 109542, 2019. https://doi.org/10.1016/j.engstruct.2019.109542.
[4] A. D. Mai, M. N. Sheikh, and M. N. S. Hadi, "Influence of the location of CFRP strips on the behaviour of partially wrapped square reinforced concrete columns under axial compression", Struct., vol. 15, pp. 131-137, 2018. https://doi.org/10.1016/j.istruc.2018.06.007.
[5] L. Lam and J. G. Teng, "Design-oriented stress–strain model for FRP-confined concrete”, Constr. Build. Mater., vol. 17, no. 6–7, pp. 471-489, 2003. https://doi.org/10.1016/S0950-0618(03)00045-X.
[6] L. M. Wang and Y. F. Wu, "Effect of corner radius on the performance of CFRP-confined square concrete columns: Test", Eng. Struct., vol. 30, no. 2, pp. 493-505, 2008. https://doi.org/10.1016/j.engstruct.2007.04.016.
[7] L. Lam and J. G. Teng, "Design-oriented stress-strain model for FRP-confined concrete in rectangular columns”, J. Reinf. Plast. Compost., vol. 22, no. 13, pp. 1149-1186, 2003.
[8] J. G. Teng and L. Lam, "Behavior and modeling of fiber reinforced polymer-confined concrete”, J. Struct. Eng., vol. 130, no. 11, pp. 1713-1723, 2004. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:11(1713).
[9] J. G. Teng, Y. L. Huang, L. Lam, and L. P. Ye, "Theoretical model for fiber-reinforced polymer-confined concrete”, J. Compos. Constr., vol. 11, no. 2, pp. 201-210, 2007. https://doi.org/10.1061/(ASCE)1090-0268(2007)11:2(201).
[10] H. Naderpour, A. Kheyroddin, and G. G. Amiri, "Prediction of FRP-confined compressive strength of concrete using artificial neural networks”, Compos. Struct., vol. 92, no. 12, pp. 2817-2829, 2010. https://doi.org/10.1016/j.compstruct.2010.04.008.
[11] A. Cevik and I. H. Guzelbey, "Neural network modeling of strength enhancement for CFRP confined concrete cylinders”, Building and Environment, vol. 43, no. 5, pp. 751-763, 2008. https://doi.org/10.1016/j.buildenv.2007.01.036.
[12] A. Cascardi, F. Micelli, and M. A. Aiello, "An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns”, Eng. Struct., vol. 140, pp. 199-208, 2017. https://doi.org/10.1016/j.engstruct.2017.02.047.
[13] T. Ozbakkaloglu, J. C. Lim, and T. Vincent, "FRP-confined concrete in circular sections: Review and assessment of stress-strain models”, Eng. Struct., vol. 49, pp. 1068-1088, 2013. https://doi.org/10.1016/j.engstruct.2012.06.010.
[14] L. Lam and J. G. Teng, "Strength models for fiber-reinforced plastic-confined concrete”, J. Struct. Eng., vol. 128, no. 5, pp. 612-623, 2002. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:5(612).
[15] Guide for the Design and Construction of Externally Bonded FRP Systems for Strengthening Concrete Structures, ACI (American Concrete Institute), 2017.
[16] Guide for the Design and Construction of Externally Bonded FRP Systems for Strengthening Existing Structures, CNR-DT (Italian National Research Council), 2013.
[17] M. Ye, L. Li, D.-Y. Yoo, H. Li, C. Zhou, and X. Shao, "Prediction of shear strength in UHPC beams using machine learning-based models and SHAP interpretation”, Constr. Build. Mater., vol. 408, p. 133752, 2023. https://doi.org/10.1016/j.conbuildmat.2023.133752.
[18] J. Shu, H. Yu, G. Liu, H. Yang, Y. Chen, and Y. Duan, "BO-Stacking: A novel shear strength prediction model of RC beams with stirrups based on Bayesian Optimization and model stacking”, Struct., vol. 58, p. 105593, 2023. https://doi.org/10.1016/j.istruc.2023.105593.
[19] J.-S. Chou, N.-T. Ngo, and A.-D. Pham, "Shear Strength Prediction in Reinforced Concrete Deep Beams Using Nature-Inspired Metaheuristic Support Vector Regression”, Journal of Computing in Civil Engineering, vol. 30, no. 1, p. 04015002, 2016. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000466.
[20] S. Ali, J. Ahmad, U. Iqbal, S. Khan, and M. N. S. Hadi, "Neural network-based models versus empirical models for the prediction of axial load-carrying capacities of FRP-reinforced circular concrete columns”, Struct. Concr., vol. n/a, no. n/a. https://doi.org/10.1002/suco.202300420.
[21] Q. Yang et al., "A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction”, Geoscience Frontiers, vol. 15, no. 2, p. 101770, 2024. https://doi.org/10.1016/j.gsf.2023.101770.
[22] C. Yang, Y. Yin, J. Zhang, P. Ding, and J. Liu, "A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning”, Geoscience Frontiers, vol. 15, no. 1, p. 101690, 2024. https://doi.org/10.1016/j.gsf.2023.101690.
[23] A. F. Lin and L. Wotherspoon, "The use of global versus region-specific data for the prediction of co-seismic landslides”, Engineering Geology, vol. 327, p. 107335, 2023. https://doi.org/10.1016/j.enggeo.2023.107335.
[24] P. G. Asteris and V. G. Mokos, "Concrete compressive strength using artificial neural networks”, Neural Computing and Applications, vol. 32, no. 15, pp. 11807-11826, 2020. https://doi.org/10.1007/s00521-019-04663-2.
[25] D. J. Armaghani and P. G. Asteris, "A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength”, Neural Computing and Applications, vol. 33, no. 9, pp. 4501-4532, 2021. https://doi.org/10.1007/s00521-020-05244-4.
[26] X. Zhu and P. Lei, "A novel prediction model for failure mechanism of foamed concrete”, Constr. Build. Mater., vol. 370, p. 130625, 2023. https://doi.org/10.1016/j.conbuildmat.2023.130625.
[27] A. Cevik, M. T. Göğüş, İ. H. Güzelbey, and H. Filiz, "Soft computing based formulation for strength enhancement of CFRP confined concrete cylinders”, Advances in Engineering Software, vol. 41, no. 4, pp. 527-536, 2010. https://doi.org/10.1016/j.advengsoft.2009.10.015.
[28] Q. Cao, H. Li, and Z. Lin, "Study on the active confinement of GFRP-confined expansive concrete under axial compression”, Constr. Build. Mater., vol. 227, p. 116683, 2019. https://doi.org/10.1016/j.conbuildmat.2019.116683.
[29] T. Ozbakkaloglu and J. C. Lim, "Axial compressive behavior of FRP-confined concrete: Experimental test database and a new design-oriented model”, Compos. Part B: Eng., vol. 55, pp. 607-634, 2013. https://doi.org/10.1016/j.compositesb.2013.07.025.
Read more
##plugins.themes.academic_pro.article.sidebar##
Published
Jun 30, 2025
Download
How to Cite
Duc, M. A. “Application of Machine Learning Models in Predicting the Compressive Strength of GFRP-Confined Concrete”. The University of Danang - Journal of Science and Technology, vol. 23, no. 6A, June 2025, pp. 29-35, doi:10.31130/ud-jst.2025.23(6A).280.
