## 5 Conclusions  
In this study, three AI models, namely GAANFIS, PSOANFIS and SVM, were developed and compared for predicting the MS, one of the most important parameters of SMA materials. The best model determined was then applied to predict other important parameters of the SMA materials such as the MF and MQ. For this purpose, a total of 60 groups of the SMA samples were fabricated in our laboratory and then used for generating datasets, which included input parameters (coarse aggregates, bitumen content and cellulose) and output parameter (MS or MF or MQ). Validation of the models was achieved using several criteria such as MAE, RMSE and R. In addition, converged statistical values of criteria deduced from 1000 Monte Carlo simulations were used to evaluate the robustness of the developed models under the variability of inputs. The results showed that all the proposed AI models performed well for predicting the MS of the SMA materials, but the SVM (MAE = 0.3804, RMSE = 0.5978 and R = 0.8711) exhibited the best compared with other methods such as the PSOANFIS (MAE = 0.4361, RMSE = 0.6592 and R = 0.8692) and the GAANFIS (MAE = 0.5015, RMSE = 0.7213 and R = 0.8181). In addition, the robustness analysis results also showed that under input variability, the SVM was the most stable algorithm (MAE = 0.2741, RMSE = 0.4004 and R = 0.9246) compared with others. Other results also confirmed that the SVM has a good performance for predicting other Marshall Parameters (MF and MQ) of the SMA materials. Thus, it can be reasonably concluded that the SVM is a promising method for predicting the MS, MF and MQ of the SMA materials, which can be used to predict other properties of the SMA materials. It should be pointed out that the statistical robustness analysis in this study was according to the considered range of data but may provide efficient information to prepare experiments in further researches with a wider range of the components of SMA samples. It is also noticed that the dataset in this study is still limited (60 samples), therefore the short term perspective would be dedicated to produce more sophisticated dataset in order to develop more accurate and reliable AI models. In addition, the difference of the PSOANFIS and GAANFIS models was not significant in this study; thus, several tests namely Friedman and Wilcoxon sign rank tests should be carried out to find a better model. In general, the results of this study might help in selecting the suitable AI method for quick determination of several important properties of the SMA mixtures. Conceptualization, H.-L.N., T.-H.L., B.T.P. and H.-B.L. ; Methodology, H.-L.N., T.-H.L., C.-T.P., T.-T.L., B.T.P. and H.-B.L. ; Software, T.-T.L., B.T.P., and H.-B.L. ; Validation, H.-L.N., T.-H.L., C.-T.P. and H.-B.L. ; Formal Analysis, H.-L.N., T.-H.L., C.-T.P. and L.S.H. ; Investigation, T.-H.L., C.-T.P., T.-T.L., L.S.H. and V.M.L. ; Resources, H.-L.N., T.-H.L., V.M.L., H.-B.L. and B.T.P. ; Data Curation, H.-L.N. and T.-H.L. ; Writing--Original Draft Preparation, H.-L.N., T.-H.L., C.-T.P., T.-T.L., L.S.H., V.M.L., B.T.P. and H.-B.L. ; Writing--Review & Editing, B.T.P. and H.-B.L. ; Visualization, T.-T.L., V.M.L., B.T.P. and H.-B.L. ; Supervision, B.T.P. and H.-B.L. ; Project Administration, H.-L.N., B.T.P. and H.-B.L. ; Funding Acquisition, T.-H.L. This research received no external funding. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Output** & **A** & **B** & **C** & **D** & **E** & **R (Equation (5))** & **R(SVM)** \\ \hline MS & \(-0.13\) & \(-0.32\) & \(-0.13\) & \(1.22\) & \(18.80\) & \(0.82\) & \(0.92\) \\ MF & \(0.01\) & \(0.45\) & \(0.47\) & \(-0.97\) & \(-0.28\) & \(0.76\) & \(0.94\) \\ MQ & \(-0.05\) & \(-0.41\) & \(-0.38\) & \(0.97\) & \(8.38\) & \(0.73\) & \(0.91\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The constants used to fit MS, MF and MQ using Equation (5), along with the corresponding correlation coefficient. * _[PERSON] et al.