## 2 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Design**} & \multirow{2}{*}{**X (Varying HN)**} & \multirow{2}{*}{**L1**} & \multirow{2}{*}{**L2**} & \multicolumn{3}{c}{**Pearson Correlation Coefficient (\(R\))**} \\ \cline{5-8}  & & & & **Training** & **Validating** & **Testing** \\  & & & & **Data** & **Data** & **Data** \\ \hline \multirow{8}{*}{A} & 3 & \multirow{8}{*}{LOSIG} & \multirow{8}{*}{PURELIN} & 0.89 & 0.88 & 0.91 \\  & 4 & & & 0.90 & 0.81 & 0.88 \\  & 5 & LOSIG & PURELIN & 0.92 & 0.91 & 0.88 \\  & 6 & & & 0.90 & 0.90 & 0.93 \\  & 11 & & & 0.90 & 0.92 & 0.88 \\ \cline{2-8}  & 3 & \multirow{8}{*}{TANSIG} & \multirow{8}{*}{PURELIN} & 0.89 & 0.83 & 0.93 \\  & 4 & & & 0.89 & 0.79 & 0.89 \\  & 5 & & & 0.89 & 0.84 & 0.91 \\  & 6 & & & 0.92 & 0.84 & 0.88 \\  & 11 & & & 0.91 & 0.90 & 0.91 \\ \hline \multirow{8}{*}{B} & 3 & \multirow{8}{*}{LOSIG} & \multirow{8}{*}{PURELIN} & 0.82 & 0.83 & 0.76 \\  & 4 & & & 0.88 & 0.73 & 0.90 \\  & 5 & LOSIG & PURELIN & 0.84 & 0.89 & 0.90 \\  & 6 & & & 0.87 & 0.81 & 0.89 \\  & 11 & & & & 0.87 & 0.93 & 0.92 \\ \cline{2-8}  & 3 & \multirow{8}{*}{TANSIG} & \multirow{8}{*}{PURELIN} & 0.80 & 0.84 & 0.84 \\  & 4 & & & 0.86 & 0.85 & 0.83 \\  & 5 & & & 0.82 & 0.89 & 0.87 \\  & 6 & & & 0.85 & 0.73 & 0.83 \\  & 11 & & & & 0.87 & 0.95 & 0.80 \\ \hline \multirow{8}{*}{C} & 3 & \multirow{8}{*}{LOSIG} & \multirow{8}{*}{PURELIN} & 0.61 & 0.67 & 0.68 \\  & 4 & & & 0.70 & 0.71 & 0.59 \\ \cline{1-1}  & 5 & LOSIG & PURELIN & 0.63 & 0.74 & 0.77 \\ \cline{1-1}  & 6 & & & 0.70 & 0.84 & 0.55 \\ \cline{1-1}  & 11 & & & 0.70 & 0.87 & 0.87 \\ \cline{1-1} \cline{2-8}  & 3 & \multirow{8}{*}{TANSIG} & \multirow{8}{*}{PURELIN} & 0.64 & 0.71 & 0.46 \\ \cline{1-1}  & 4 & & & 0.63 & 0.82 & 0.73 \\ \cline{1-1}  & 5 & & & 0.73 & 0.72 & 0.64 \\ \cline{1-1}  & 6 & & & 0.72 & 0.75 & 0.72 \\ \cline{1-1}  & 11 & & & 0.75 & 0.87 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trained, validated, and tested ANN models for WC40 concrete. Figure 10: WC40C Specimen three: NA versus load. were 5 IN-6 HN-1 ON, 5 IN-11 HN-1 ON, and 5 IN-5 HN-1 ON, respectively. On the other hand, WC60A, B and C had optimum ANN models as 5 IN-11 HN-1 ON, 5 IN-11 HN-1 ON, and 5 IN-11 HN-1 ON, respectively. It was noted that some of the trained and tested models gave \(R\) values less than 0.50 which was considered very low and poor in performance. These were attributed by complex relationships of input and output parameters with the use of simple ANN structures comprising of one hidden layer only. It was recommended to further investigate and explore the use of ANN structures with deep learning or with large number of hidden layers to prevent low or poor performing models from the selection list of available models. After selection of optimum model, amplitude sensitivity analysis was introduced as shown in Equation (3) for PPA, A1, A2, and A3 [3]. This was used to analyze the behavior of the amplitude when it decreased with an increasing load. On the other hand, the NA sensitivity analysis showed opposite behavior compared to the behaviors of the four parameters mentioned. Equation (4) was used for NA which increased when also the load increased. \[Da=1-f(x)/\textit{fmax} \tag{3}\]  
\[Da=f(x)/\textit{fmax} \tag{4}\]  
where: \(Da\): Sensitivity of parameter to load; \(f(x)\): Value of the parameter; _fmax_: Maximum value of the same parameter \(f(x)\). Sensitivity is difficult to measure. In this paper, simulation of optimum ANN models is carried out and classification of sensitivity to four groups is made as shown in Table 4. These groups are: Long range sensitivity (LRS), Short range sensitivity (SRS), Insensitive to load (IL), and Insensitive to parameter (IP). Shown in Figure 11 is the simulation of the three optimum models for WC60A, B and C where A3 is varied while the four parameters are constant at 0.5. Sensitivity classification of WC60A, B and C in Figure 11 shows that WC60A is classified as LRS, WC60B as SRS, WC60C as IL.