## 3 Results  
All networks were tried with a different number of neurons until the optimum values were reached. For each trial with a different number of neurons, the performance measures of RMSE, MAPE and CC were observed for the test set. The number of neurons with the best values of above-mentioned performance measures was selected for further comparisons. Table 3 shows the number of neurons for each case with different types of ANNs. The number for neurons for RBFNN is generally higher than that for MLP. This trend could be attributed to the algorithms of these types, wherein RBFNN applies a more complicated approach of calculating the center of each neuron and applying a Gaussian function to it. On the other hand, MLP applies a relatively simplistic function to each neuron resembling to generalized regression. The combined dataset was used for the development of a hierarchical model. It was found that less than 5 neurons were sufficient for MLP, while more than 10 neurons were required for RBFNN in all cases. The calculated number of neurons in the input layer is exactly equivalent to the number of input variables (or independent), whereas the output layer containing one neuron as a single output is desired from the model. Data presented in Tables 4-6 depict the comparison of performance measures for MLP and RBFNN models for different types of samples. Models were calculated only for moisture damaged and aged samples since they are more critical as compared to fresh samples for pavement design. The scatterplots for all these models are shown in Figures 3-8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of ANN** & **Dataset** & **RMSE** & **MAPE** & **CC** \\ \hline \multirow{3}{*}{MLP} & Training & 49.54 & 23.02 & 0.82 \\ \cline{2-5}  & Test & 52.31 & 30.06 & 0.80 \\ \hline \multirow{3}{*}{RBFNN} & Training & 44.68 & 20.07 & 0.84 \\ \cline{2-5}  & Test & 51.49 & 22.64 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance measures for hierarchical ANNs of combined samples. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Type of Sample**} & \multicolumn{3}{c}{**Number of Neurons**} \\ \cline{2-4}  & **MLP** & **RBFNN** \\ \hline Combined & 2 & 34 \\ \hline Moisture-Damaged & 2 & 12 \\ \hline Aged & 4 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Type of ANNs and number of neurons. Figure 4: Scatterplot for hierarchical RBFNN. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of ANN** & **Dataset** & **RMSE** & **MAPE** & **CC** \\ \hline \multirow{2}{*}{MLP} & Training & 31.39 & 15.63 & 0.92 \\ \cline{2-5}  & Test & 31.74 & 22.52 & 0.84 \\ \hline \multirow{2}{*}{RBFNN} & Training & 36.57 & 19.58 & 0.90 \\ \cline{2-5}  & Test & 38.88 & 25.31 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance measures for ANNs of aged samples. Figure 3: Scatterplot for hierarchical MLP. Figure 5: Scatterplot for MLP of moisture-damaged samples. Figure 6: Scatterplot for RBFNN of moisture-damaged samples. Figure 7: Scatterplot for MLP of aged samples. It can be observed that RBFNN performs better than MLP for the hierarchical model, while MLP had better accuracy for moisture-damaged and aged sample models. Hence, it can be said that the use of RBFNN is more suitable for relatively complex problems while MLP should be preferred for simpler problems. The results of this study also point to the fact that complex models may not always provide better models, and rather they should be selected based upon the nature of problem. This in conformance with the results from [PERSON] and [PERSON] [38] in their study related to Quantitative Systems Pharmacology (QSP) models. The superior performance of RBFNN and MLP in different cases can be attributed to their processing mechanisms, as shown in Figure 2. MLP uses a non-linear regression-like approach for calculating weights, which is useful for data items with less variability.