## 3 Results and Discussion  
This investigation utilized 60 experimental data points for each of the three input variables. Among these data points, 42 samples (70%) were designated for training purposes, 9 (15%) were reserved for validation, and the final 9 (15%) were assigned for testing. The Levenberg-Marquardt method was selected as the training method for ANN. The framework of the ANN system designed to evaluate the q\({}_{\text{u}}\) performance is depicted in Figure 2.  
Figure 3 illustrates the Simulink block diagram representing the ANN results for the provided sample input values. The efficacy of the ANN is measured using different metrics. The regression (R) values represent the extent of concordance between the actual data and the outputs generated by the model. An R value close to 1 indicates excellent performance. Given that the outputs from the ANN closely correspond with the experimental findings, the R values are consistently observed to be near 1. Another metric, Mean Squared Error (MSE), is the average of the squared differences between the predicted and actual values. MSE numerically represents the model's error level; lower MSE values indicate a more successful predictive performance. While R serves as a measure of accuracy, MSE is used to evaluate the error rate of the model. Figure 4 illustrates the R values for the training, validation, test, and overall datasets. Upon analyzing the ANN results alongside the experimental data, it becomes evident  
Figure 3: ANN outputs corresponding to sample input values. Figure 2: The ANN architecture. that the ANN predictions exhibit a strong correlation with the experimental outcomes. Specifically, R values of 0.95, 0.95, 0.97, and 0.95 were recorded for the training, validation, test, and overall datasets, respectively. To assess the effectiveness of the ANN model, experimental input values and their corresponding output values generated by the ANN were documented and summarized in Table 2 for comparative analysis. A thorough examination of Table 2 reveals that the ANN outputs closely align with the experimental findings. Consistent with the experimental results, the maximum compressive strength was observed with the incorporation of 4% basalt fiber for both 6 mm and 12 mm basalt fiber lengths, as highlighted in bold in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Inputs**} & \multicolumn{2}{c}{**Experimental**} & \multicolumn{1}{c}{**ANN**} \\ \cline{2-6}  & **Basalt Fiber** & **Clay Ratio** & **Basalt Fiber** & **Output** & **Output** \\  & **Length** & & **Ratio** & & \\ \hline
1 & 6 & 100 & 0 & 206.93 & 208.20 \\
2 & 6 & 99 & 1 & 210.24 & 213.60 \\
3 & 6 & 98 & 2 & 220.44 & 219.60 \\
4 & 6 & 97 & 3 & 226.50 & 226.50 \\
5 & 6 & 96 & **4** & **237.48** & **237.10** \\
6 & 6 & 95 & 5 & 200.03 & 200.00 \\
7 & 12 & 100 & 0 & 206.93 & 206.50 \\
8 & 12 & 99 & 1 & 223.64 & 225.60 \\
9 & 12 & 98 & 2 & 229.00 & 230.50 \\
10 & 12 & 97 & 3 & 247.42 & 250.00 \\
**11** & **12** & **96** & **4** & **267.66** & **269.10** \\
12 & 12 & 95 & 5 & 202.74 & 199.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental and ANN outputs. Figure 4: The R values for training, validation, test, and all datasets. Considering the fiber length and fiber ratios used as input parameters in this study, it was determined that the \(\text{q}_{\text{u}}\) values increased as the fiber length increased, and the 12 mm fiber reinforced samples had higher strength. It was also determined that the \(\text{q}_{\text{u}}\) values increased as the fiber ratio increased, but this increase continued up to 4% fiber ratio. These results show that fiber length is more effective in increasing strength. Following this, the ANN was re-evaluated using experimental data. When the predicted values obtained from the ANN were compared with the experimental results, an R value of 0.99 was observed.