Implementation of Soft Computing Techniques in Fine-Tuning of TRM-Concrete Bond Strength Models

The bond strength in between reinforcing steel bars and the concrete is a critical factors lies in the structural integrity of reinforced structures of concrete. Some approaches have been proposed to foresee the strength of bond, but these models may not accurately represent the complex nature of the bond phenomenon, leading to inaccurate predictions. This study aims to improve the accuracy of existing TRM-concrete bond strength models by fine-tuning them using soft computing techniques. The results of this study demonstrate that fine-tuning existing TRM-concrete bond strength models using soft computing techniques significantly improves their accuracy in predicting bond strength. The fine-tuned models also outperform the original models, indicating that the soft computing techniques used in this study are effective in improving the accuracy of existing TRM-concrete bond strength models. This study contributes to the development of accurate TRM-concrete bond strength prediction models that can be used in the design of reinforced concrete structures. The fine-tuning process using soft computing techniques can also be applied to other existing models to improve their accuracy in predicting complex phenomena.

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