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

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 𝑅 2 and 𝑉𝐴𝐹 were slightly similar with a tiny superiority of 𝐴𝑂 − 𝑅𝐹 compared to 𝐴𝑂 − 𝑆𝑉𝑅 with 𝑅 2 equal to 0.9977 and 𝑉𝐴𝐹 equal to 99.772, but large differences with results of 𝑀𝐴𝑅𝑆.