## 2 Experimental Database
### Input Variables Influencing Failure Behavior  
In this study, the input variables are the concrete compressive strength (\(f_{c}\)), steel yield strength (\(f_{y}\)), axial load ratio (P/\(A_{g}\)_f_c), height-to-depth aspect ratio (L/D), longitudinal reinforcing ratio (\(\rho_{l}\)), and transverse reinforcing ratio (\(\rho_{l}\)). Figure 2 shows the data distribution for the six input variables used in this study as key influencing factors determining the failure types. It indicates that the data are well distributed within the training range, for the critical parameters affecting the column failure type.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Failure Mode** & **Description** \\ \hline \multirow{2}{*}{Flexure} & Degradation occurred due to flexural deformation \\  & after the yielding of the longitudinal reinforcement \\ \hline \multirow{2}{*}{Shear} & Degradation (diagonal cracks) occurred due to shear distress \\  & before the yielding of the longitudinal reinforcement \\ \hline \multirow{2}{*}{Flexure–shear} & Degradation occurred after the yielding of the longitudinal \\  & reinforcement but results from shear distress \\ \hline \hline \end{tabular}
\end{table}
Table 1: The considered failure mode.  
Figure 1: Constituents of the reinforced concrete column database.  
Concrete and steel strengths were selected as input variables to predict failure modes with different strength values. Previously, [PERSON] et al. [19] demonstrated that the shear strength of RC columns increases with increasing concrete strength and axial stress. The shear strength is known to increase with increasing axial load, as indicated by the following equation:  
\[V_{ACI} =\frac{1}{6}\bigg{(}1+\frac{N_{u}}{14A_{\mathrm{g}}}\bigg{)}\sqrt{f_{c}} b_{w}d+\frac{A_{v}f_{y}d}{s} \tag{1}\]  
where \(N_{u}\) is the axial load (\(N_{u}\)) acting perpendicular to the cross section simultaneously with \(V_{ACI}\), \(A_{g}\) is total cross-sectional area of the column (mm\({}^{2}\)), \(f_{c}\) is the designed compressive strength of the concrete (MPa), \(b_{w}\) is the web width of the member (mm), \(d\) is the effective depth of the column (mm), \(A_{v}\) is the total cross-sectional area of the transverse reinforcement within spacing (mm\({}^{2}\)), \(f_{y}\) is the designed yield strength of the reinforcement (MPa), and \(s\) is the spacing of the transverse reinforcement (mm).  
The height-to-depth ratio significantly influences the shear behavior characteristics of columns. Higher height-to-depth ratios in columns result in increased stiffness, strength, and non-ductility, leading to shear failure [20]. [PERSON] and [PERSON] [20] demonstrated that as the slenderness ratio of a column increases, the size effects become more pronounced and the brittleness increases. Therefore, this variable is deemed essential and has been included in the machine learning models for predicting column failure types. [PERSON] et al. [21] demonstrated that an increase in the longitudinal reinforcing ratio enhances flexural strength but has little impact on displacement, whereas an increase in the transverse reinforcing ratio strengthens the column and improves its energy dissipation capacity. Furthermore, the input variables utilized in this study can be determined under basic conditions without the need for complex calculations or experiments. Table 2 shows the minimum, maximum, and nominal values of the input variables. The nominal values were determined based on statistical data, representing the average values within the most concentrated range of data points. Because there are no cases with an axial load ratio of 0, the minimum value was selected to be the smallest value after 0.  
Figure 2: Frequency of input parameters.