Steel and Composite Structures

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Creation of regression analysis for estimation of carbon fiber reinforced polymer-steel bond strength

  • Xiaomei Sun (School of Civil Engineering, Xijing University) ;
  • Xiaolei Dong (School of Civil Engineering, Xijing University) ;
  • Weiling Teng (School of Civil Engineering, Xijing University) ;
  • Lili Wang (Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University) ;
  • Ebrahim Hassankhani (Dept. of Civil Engineering, Faculty of Geotechnical Engineering, Univ. of Tabriz)
  • Received : 2023.12.08
  • Accepted : 2024.05.09
  • Published : 2024.06.10
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Abstract

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (PU) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the PU challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the PU of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was tc> Lc > EA > tA > Ec > bc > fc > fA from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R2 and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R2 equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

Keywords

Acknowledgement

This paper is supported by Shaanxi Science and Technology Association Enterprise Innovation and Youth Talent Promotion Project (Program No. 20230612).

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