## 3 Results  
Scanning electron micrographs (SEM) of TS and AATS of 14 days are presented in Figure 2. From the microstructural observations, the morphology of the samples tends to change after alkali activation as the particles tend to flocculate under the effect of the natural KOH from the activator. This is confirmed by the energy dispersive spectroscopy (EDX) results, where the formation of new elements has been noticed (.a.i and b.i). Moreover, the Fourier transform infrared (FTIR) results in (.a.ii and b.ii) indicate that the molecular bonds were not significantly transformed. However, shifts in the absorption peaks were noticed. The shift of the peaks is mainly attributed to the O-H and Si-O-Si functional group resulting in the formation of new bands during the activation. Results of the compressive strength development reveals an increase in the compressive strength from the early curing age for all the samples. This can be explained by the lower rate of alkalinity consumption in the early stage. However, at the late curing period, the samples showed a complete difference in the trend than the previously observed trend. A possible explanation is that the alkalinity consumption rate was higher at the late ageing, which means the PH of the samples tends to be lower over the time. Besides, the samples containing the activator presented higher compressive strength with curing days for the various curing regimes. As expected, the thermal effect was observed on all the bulk performances (samples cured initially at high temperature). This could be explained by the temperature effect in the gel formation during the chemical reactions taking place between the precursor (TMS) and the activator (natural alum). Figure 3 shows the results of the models developed to predict the compressive strength. These results demonstrate that the testing, training and validation of all the three (3) models were successful. However, the results of the statistical measures (as seen in Figure 4) reveals that the SVM exhibited the highest R\({}^{2}\) (70%). Low R\({}^{2}\) and RMSE of 63% and 0.7, respectively, were displayed by the ANN. Meanwhile, the LR displayed an R\({}^{2}\) and RMSE of 26% and 0.95, respectively. Moreover, results obtained from the LR in terms of R\({}^{2}\) differed significantly from the ANN and SVM models, indicating the weak correlation between the input and output given by the model. The highest R\({}^{2}\) was obtained from the SVM model, pointing out the accuracy of the model in establishing the interdependence between the input and compressive strength of the AATS. These results align with the previous work carried out by [PERSON] et al. ; in their study, they found out that the SVM displayed the best performance over the ANN and MR [47]. In addition, the RMSE displayed by all the models indicates the insignificance of the errors that arose from the prediction models during the training. However, the LR exhibited an RMSE of 0.95, indicating the near inexistence of errors during the training. Furthermore, the SVM and ANN also displayed an RMSE higher than 0.5 (0.6 and 0.7, respectively). Figure 4 shows the results of the regression analysis for all three techniques. In Figure 4a, it is observed that the SVM outperforms ANN and LR in terms of R\({}^{2}\) score, where the SVM achieves an R\({}^{2}\) of 70% while the ANN and LR achieves an R\({}^{2}\) of 63% and 26%, respectively. In terms of RMSE, the SVM outperforms the ANN and LR as shown in Figure 4b where the SVM records an RMSE of 0.6 while the ANN and LR records an RMSE of 0.7 and 0.95, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Kernel & linear \\ C & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter settings for SVM.