# A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders
## 6 Concluding Remarks  
A soft computing model for the ultimate strength estimation of FRPCCC has been proposed in this paper. A set of experimental data from the published literature has been collected and divided into input and output parameters. Firstly, the ANN model has been created and analyzed. The mean squared error and R-values have been used to verify the efficiency of the network.  
The results of the analysis indicate that a network with 15 hidden neurons has the best performance. However, it should be noted that the basic ANN technique cannot propose a formulation to forecast the compressive strength of FRPCCC. Therefore, in the next step of the study, the author's improvement approach has been presented. A model with a K-fold cross-validation technique in the feed-forward backpropagation neural network has been presented. The correlation coefficient, root mean square error, mean percentage of error and average absolute error have been used to check its efficiency. The structure with 11 neurons in the hidden layer has been found to give the best performance. Finally, a comparison between the proposed formula and existing empirical ones has been conducted. To verify the proposed formula, five known models described in this paper have been selected. The results of the study show that the proposed method can estimate the compressive strengths of the FRPCCC with higher precision compared to the existing formulas. Moreover, it can be used to predict the compressive strength of FRPCCC with different types and arbitrary thicknesses of FRP (carbon,amid, and glass). It should be noted that the mean percentage of error and the correlation coefficient for the proposed method are equal to 3.49% and 0.9809, respectively. Meanwhile, the corresponding values for other existing methods are equal to over 13% and 0.41, respectively. It means that, for the proposed formula, more than 96% of the simulated results are entirely consistent with the experimental results. Furthermore, the minimum values of RMSE and AAE have been obtained for the proposed formula. Therefore, it should be pointed out that the proposed formula is very accurate compared to other existing methods, for which the accuracy is usually lower than 85%. It should also be added that the proposed method can be easily employed using a calculator with high precision while, in the case of the neuro-fuzzy network, neural network and other known methods, a computer and sophisticated software is usually needed. Therefore, our model can be used to estimate the ultimate compressive capacity of FRP-confined concrete cylinders in the initial design of practical projects.  
Finally, it should be noted that there is a lack of experimental tests on concrete cylinders made of seawater and sea sand retrofitted with FRP sheets in order to propose a formula that covers the entire region. This should be a focus in future studies.  
Conceptualization, R.K. and H.N.; methodology, R.K., H.N., H.E.K., A.J.-G. and R.J.; software, R.K., H.N. and H.E.K.; validation, R.K., H.N., H.E.K., A.J.-G. and R.J.; formal analysis, R.K., H.N. and H.E.K.; investigation, R.K. and H.N.; writing--original draft preparation, R.K., H.N., and H.E.K; writing--review and editing, A.J.-G. and R.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.