## 3 Artificial Intelligence Prediction
### Algorithm Results and Discussion
#### 3.3.1 Normalized Ultimate Load Prediction  
Following the prediction process, to predict the normalized ultimate load, available inputs are tested with different scenarios. Table 6 indicates the best possible input scenario to estimate the normalized ultimate load. As shown in Tables 9-11, different prediction patterns are presented by accuracy criteria (by adjusting best population and iteration). From Figures 28-30, it is noted that the normalized ultimate load estimation is perfectly predicted by the proposed algorithm. Furthermore, as shown in the tables and graphs, the prediction accuracy in this study is 100%. After finding the best population, the calculations to find the optimal number of repetitions are performed. According to Table 12, the optimal number of repetitions in the test phase is obtained as 150. According to the results obtained in the test phase, cases with 4 and 5 inputs obtain the same results. However, because it is more efficient to obtain the result with less number of inputs, the number of optimal inputs is chosen to be 4. As shown in Table 12, the ultimate moment has the greatest impact on finding results. In addition, the distance between the bolts causes the results to be skewed and negatively impacts the results. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Population**} & \multicolumn{8}{c}{**Network Result**} \\ \cline{2-11}  & \multicolumn{4}{c}{**Training Phase**} & \multicolumn{8}{c}{**Testing Phase**} \\ \cline{2-11}  & **R** & **NSE** & **RMSE** & **MAE** & **WI** & **r** & **NSE** & **RMSE** & **MAE** & **WI** \\ \hline
150 & 0.996 & 0.992 & 7.193 & 5.041 & 0.998 & 0.905 & 0.780 & 7.227 & 5.035 & 0.948 \\ \hline
250 & 0.996 & 0.992 & 7.244 & 5.270 & 0.998 & 0.907 & 0.798 & 7.199 & 5.246 & 0.951 \\ \hline
350 & 0.996 & 0.991 & 7.381 & 5.578 & 0.998 & 0.900 & 0.776 & 7.534 & 5.679 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The calculated accuracy criteria for the performance of the implemented models (population = 250). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Iteration**} & \multicolumn{8}{c}{**Network Result**} \\ \cline{2-11}  & \multicolumn{4}{c}{**Training Phase**} & \multicolumn{8}{c}{**Testing Phase**} \\ \cline{2-11}  & **r** & **NSE** & **RMSE** & **MAE** & **WI** & **r** & **NSE** & **RMSE** & **MAE** & **WI** \\ \hline
100 & 0.995 & 0.990 & 7.837 & 5.751 & 0.998 & 0.882 & 0.711 & 8.333 & 5.953 & 0.934 \\ \hline
150 & 0.996 & 0.992 & 7.244 & 5.270 & 0.998 & 0.907 & 0.798 & 7.199 & 5.246 & 0.951 \\ \hline
200 & 0.995 & 0.991 & 7.504 & 5.339 & 0.998 & 0.899 & 0.762 & 7.421 & 5.250 & 0.944 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The calculated accuracy criteria for the performance of the implemented models (population = 250). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{8}{c}{**Network Result**} \\ \cline{2-11}  & \multicolumn{4}{c}{**Training Phase**} & \multicolumn{8}{c}{**Testing Phase**} \\ \cline{2-11}  & **R2** & **r** & **NSE** & **RMSE** & **MAE** & **WI** & **R2** & **r** & **NSE** & **RMSE** & **MAE** & **WI** \\ \hline
1 & 0.885 & 0.941 & 0.870 & 0.035 & 0.026 & 0.969 & 0.991 & 0.935 & 0.854 & 0.038 & 0.028 & 0.965 \\ \hline
2 & 0.971 & 0.985 & 0.970 & 0.018 & 0.003 & 0.993 & 0.997 & 0.978 & 0.955 & 0.022 & 0.004 & 0.989 \\ \hline
3 & 1  
Figure 28: Comparison of the predicted and measured load: (**a**) One input, (**b**) two inputs, (**c**) three inputs, (**d**) four input, (**e**) five inputs, (**f**) six inputs through MLP–PSO–FS model. Figure 30: MLP-PSO-FS (4 inputs) Error histograms: (**above**) train phase, (**below**) test phase. Figure 29: MLP-PSO-FS (4 inputs) prediction vs. experimental diagram: (**above**) train phase, (**below**) test phase. Figure 28 illustrates the predicted and measured load by the MLP-PSO-FS model in scatter diagrams.