# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 3 Results and Discussion
### Validation of Models  
Figure 10 shows a robust relationship between the estimated UCS and the observed UCS to further determine the model's accuracy. This indicates the success of the model in predicting the UCS of the samples, an important measure of its overall quality and strength. Additionally, the ultrasonic pulse velocities (UPVs) agree well with the predicted and observed data, as shown in Figure 11. Further, this lends credence to the theory that the model accurately predicts sample strength. The ANN model is quite effective in predicting strengths. Furthermore, it is worth noting that although other research has documented \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **w (\%)** & **WMP (\%)** & **CT** & **UCS** \\ \hline w (\%) & 1 & & \\ WMP (\%) & 0.442476 & 1 & & \\ CT & 0.100023 & 0.350106 & 1 & \\ UCS & 1 & 0.442476 & 0.100023 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pearson correlation matrix between the input data and the UCS values predicted by the ANN model. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **w (\%)** & **WMP (\%)** & **CT** & **UPV** \\ \hline w (\%) & 1 & & & \\ WMP (\%) & 0.738375 & 1 & & \\ CT & 0.032906 & 0.01804 & 1 & \\ UPV & 1 & 0.738375 & 0.032906 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pearson correlation matrix between the input data and the UPV values predicted by the ANN model. the substantial accuracy of artificial neural networks (ANN) in predicting unconfined compressive strength (UCS) [72], this particular model is the first instance of its validation specifically for soil stabilization with marble powder waste. The results demonstrate the effectiveness of artificial neural networks (ANNs) in capturing the specific improvements brought about by the additive in question. The model's accuracy was evaluated over various variables, including different amounts of marble powder content, curing times, and moisture levels. However, further validation efforts should expand the input parameters' range to include a wider range of clay mineralogy and sample types. This study showed a strong relationship between the predictions of the artificial neural network (ANN) model and the experimental data for both unconfined compressive strength (UCS) and ultrasonic pulse velocity (UPV), with correlation coefficients (R) close to 1 for both. The observed values exceed the established standards for robust correlation (R > 0.8), as well as the criteria outlined by [PERSON] [76] for an exceptionally high level of correlation (R > 0.9). The R-values observed in this study are significantly similar to those of previous artificial neural network (ANN) modeling studies on stabilized soils, where R-value ranges are close to 1 [77]. This observation suggests that the present model more accurately represents the improvements resulting from the use of marble powder stabilization. Figure 10: Observed versus predicted values of UCS for water content, curing time, and experimental class (waste marble powder by the percentage of dry soil). Nevertheless, it is critical to conduct a comprehensive evaluation of model performance by considering additional statistical metrics, such as Root Mean Square Error (RMSE) and R-squared (R\({}^{2}\)), as recommended by [PERSON] et al. [78]. Additional experimentation with a wider range of parameter settings would contribute to the generalizability of the model. The following tables show a comparison of the datasets used in the training and testing phases of UCS (Table 6) and UPV (Table 7). Using MATLAB's neural network transfer functions, we found that water content had a greater effect than either curing time or percent of WMP. It was also found that while TANSIG has a better correlation in training, LOGSIG is superior in the test phase of UCS, and LOGSIG is slightly better in UPV in both phases.