## 2 Materials and Methods
### Artificial Neural Network  
Artificial intelligence (AI) is a scientific discipline aimed at improving computers' ability to carry out tasks such as learning, resolving complex challenges, and making informed choices by emulating human cognitive processes [32]. AI is applied across numerous sectors, including healthcare, the military, and engineering. There are various AI algorithms, each designed with specific technical characteristics and intended uses [33]. Frequently utilized AI models encompass ANN, LR, SVM, and Gaussian Process Regression (GPR) approaches. GPR is a probabilistic, non-parametric approach that provides flexible predictions with uncertainty estimations, particularly useful in smaller datasets. SVMs are powerful for classification and regression tasks, maximizing the margin between classes for clearer separability, especially in high-dimensional spaces. Simple and interpretable, LR models linear relationships between input and output, best suited for problems where a linear approximation is sufficient. In this study, data obtained from the experimental research were utilized with different AI algorithms, ultimately proceeding with an ANN that yielded the most favorable results. The ANN is an intelligent technique that mimics biological neural networks and operates similarly to them [30]. The ANN method processes input data and generates optimal decisions based on predefined conditions. It is particularly effective for addressing nonlinear problems, offering practical, straightforward, and rapid solutions [34]. Due to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fiber Length** & **Sample** & **B (\%)** & **BF (\%)** &
\begin{tabular}{c} **Average q\({}_{\text{u}}\)** \\ **(kPa)** \\ \end{tabular} \\ \hline \multirow{6}{*}{6} & B & 100 & 0 & 206.93 \\  & B + \%1 BF & 99 & 1 & 210.24 \\  & B + \%2 BF & 98 & 2 & 220.44 \\  & B + \%3 BF & 97 & 3 & 226.50 \\  & B + \%4 BF & 96 & 4 & 237.48 \\  & B + \%5 BF & 95 & 5 & 200.03 \\  & B & 100 & 0 & 206.93 \\  & B + 1\% BF & 99 & 1 & 223.64 \\
12 & B + 2\% BF & 98 & 2 & 229.00 \\  & B + 3\% BF & 97 & 3 & 247.42 \\  & B + 4\% BF & 96 & 4 & 267.66 \\  & B + 5\% BF & 95 & 5 & 202.74 \\ \hline \hline \end{tabular} B: bentonite clay; BF: basalt fiber.  
\end{table} 
Table 1: Mixture ratios used in the experimental studies and unconfined compressive test results [12; 13]. these attributes, ANNs are gaining popularity across various engineering fields, including geotechnical engineering [29; 30; 31; 35; 36; 37]. This growing use of the ANN approach shows its adaptability and effectiveness in solving complex problems, making it a valuable tool for engineers. Its ability to learn from data and adjust to different situations improves its performance, indicating that its importance in engineering will likely increase as technology progresses. The framework of the ANN comprises input, output, and hidden layers. The hidden layers serve as intermediaries, facilitating the connection between the input and the output layers. Data entered into the input layer undergo processing by the neurons in the hidden layer before being relayed to the output layer, thereby highlighting the critical role of the hidden layer in information transfer. In more intricate network configurations, the number of hidden layers may be expanded to augment the learning capacity. However, this enhancement can also lead to greater complexity and longer execution times for the network [32]. Thus, the optimal number of hidden layers is contingent upon the network's overall design. In this investigation, a single hidden layer was found to be adequate for attaining the desired outcomes, given the characteristics of the network. A segment of the input data is employed to develop samples for the purposes of training, testing, and validation. These samples undergo training using diverse techniques, allowing the ANN to produce outputs corresponding to specific inputs under defined conditions. The experimental data obtained are organized in the lookup tables for utilization within the ANN system.