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Ultimate Conditions Prediction and Stress–Strain Model for FRP-Confined Concrete Using Machine Learning

  • Research Article-Civil Engineering
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Abstract

Fiber-reinforced polymers (FRP) as an exterior reinforcement material are generally utilized to enhance the effectiveness of the current and new structures. FRP-confined concrete is characterized by its better mechanical properties. Accurately predicting the ultimate conditions and stress–strain responses of FRP-confined concrete make sense to achieve superior reliability and optimized functionality of structures. In this study, four prediction models based on machine learning, containing support vector regression (SVR), back-propagation neural network (BPNN), generalized regression neural network (GRNN) and extreme learning machine (ELM), were established, and their prediction performance were compared to achieve accurate prediction of the ultimate conditions of FRP-confined concrete. Moreover, a BPNN-based model to predict the stress–strain responses of FRP-confined concrete was proposed. A carefully evaluated and scrutinized database containing 384 FRP-confined concrete specimens under compressive load from various open-access sources was used to train these models. The results showed that these prediction models were more accurate in their predictions than the design-oriented models. Moreover, GRNN and SVR had superior prediction accuracy, followed by BPNN. The machine learning-based predictive models proposed in this study served as a valuable reference for the rapid prediction on the mechanical properties of FRP-confined concrete.

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Data availability

Data will be made available on request.

Abbreviations

D :

Diameter of the FRP jacket

E c :

Elastic modulus of concrete

E FRP :

Elastic modulus of the FRP jacket

\(f_{{{\text{cc}}}}^{\prime }\) :

Ultimate axial stress of FRP-confined concrete

\(f_{{{\text{co}}}}^{\prime }\) :

Peak stress of unconfined concrete

\(f_{l}\) :

Confining stress

\(f_{{{\text{cc}}}}^{\prime } /f_{{{\text{co}}}}^{\prime }\) :

Ultimate strength enhancement ratio

\(f_{l} /f_{{{\text{co}}}}^{\prime }\) :

Confining ratio

H :

High of the FRP jacket

n :

Number of experimental samples

t :

Number of analysis steps

T :

Total sickness of the FRP jacket

R :

Radius of the cylinder

\({\mathbf{x}}\) :

Input variables

\({\mathbf{y}}\) :

Output variables

\(y_{i}\) :

Estimated value of ultimate strength

\(\hat{y}_{l}\) :

Experimental value of ultimate strength

\(\overline{y}\) :

Mean of \(y\) value

ε c u :

Ultimate axial strain of FRP-confined concrete

ε c o :

Peak strain of unconfined concrete

ε rup :

Hoop rapture strain of FRP jacket

\(\varepsilon_{t}\) :

Output ultimate strain

\(\rho_{K}\) :

Stiffness ratio

\(\rho_{\varepsilon }\) :

Strain ratio

\(\mu\) :

Specific characteristic’s average

\(\sigma_{rup}\) :

Strain ratio

\(\sigma\) :

Specific characteristic’s standard deviation

\(\sigma_{t}\) :

Output ultimate stress

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 52208160); the Natural Science Foundation of Hebei Province, China (E2021202012); the Science and Technology Research Project of Higher Education Institutions in Hebei Province, China (CXY2023016) and the Hebei Province Graduate Innovation Funding Project, China (CXZZSS2023027).

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Authors and Affiliations

  1. School of Civil and Transportation Engineering, Hebei University of Technology, Tian**, 300401, China

    Jianxin Zhang, Tingwei Zhang, Yueyang Zhai, Pang Chen & Yuanyuan Yue

  2. Civil Engineering Technology Research Center of Hebei Province, Hebei University of Technology, Tian**, 300401, China

    Jianxin Zhang, Pang Chen & Yuanyuan Yue

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Zhang, J., Zhang, T., Zhai, Y. et al. Ultimate Conditions Prediction and Stress–Strain Model for FRP-Confined Concrete Using Machine Learning. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08849-2

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