## 6 Conclusions  
This study proposed a machine learning model for rapidly predicting the failure modes of RC columns. To develop and validate the model for predicting column failure modes, four machine learning methods were employed using column data collected from past experiments and evaluated using classification model performance indicators. The following main conclusions were drawn. 1. Overall, all considered machine learning methodologies accurately predicted the flexure failure mode. Because of the high proportion of flexure failure modes in the considered experimental dataset (flexure failure mode--72.4%, flexure-shear failure mode--14.8%, shear failure mode--12.7%), the flexure failure prediction was more accurate than the prediction of other failure modes (flexure-shear and shear failure modes). Therefore, the additional data are required for flexural-shear failure and shear failure to enhance the learning model's performance. 2. Considering the average values of the classification model performance indicators, excluding accuracy, the RF model showed the highest performance level in precision, recall, F1-score, and AUC, suggesting that it accurately predicts the column failure modes. 3. The RF model demonstrated the highest average values across all performance indicators used in accuracy validation and had the highest recall for shear failure modes. Therefore, the RF model, which provided conservative predictions based on simple structural details (i.e., the material strength, axial load ratio, aspect ratio, reinforcing details, etc. ), was deemed the most rational for predicting failure modes before conducting experiments or analysis. 4. The failure modes determined using the shear capacity and demand curves agreed with the failure modes predicted by the machine learning model for flexure, shear, and flexure-shear failures. This indicates that the machine-learning-based RC column failure-mode prediction model developed in this study can accurately and rapidly predict the failure modes of RC columns using only simple column information, without the need for experimental or analytical processes. Conceptualization, J.S. and K.O. ; methodology, S.K. and H.H. ; validation, J.S. ; formal analysis, S.K. and H.H. ; investigation, S.K. and H.H. ; resources, S.K. and J.S. ; data curation, S.K., K.O. and H.H. ; writing--original draft preparation, S.K. and H.H. ; writing--review and editing, J.S. ; visualization, S.K. and H.H. ; supervision, J.S. ; project administration, J.S. and K.O. ; funding acquisition, J.S. and K.O. All authors have read and agreed to the published version of the manuscript. This work was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-003) and the KICT Research Program (project no. 20230146-001) funded by the Ministry of Science and ICT. Not applicable. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimens** & \begin{tabular}{c} **\(f_{c}\)** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **\(f_{y}\)** \\ **(MPa)** \\ \end{tabular} & **P/Ag\(f_{c}\)** & **L/D** & \(\rho_{l}\) & \(\rho_{t}\) & **Predicted Failure Mode** \\ \hline \begin{tabular}{c} C3-1 \\ ([PERSON] 2000) [1] \\ \end{tabular} & 26.4 & 497 & 0.107 & 3.93 & 0.0214 & 0.0126 & Flexure \\ \hline \
\begin{tabular}{c} 3 SLH18 \\ ([PERSON] et al., 1996) [2] \\ \end{tabular} & 26.9 & 331 & 0.089 & 3.65 & 0.0303 & 0.0015 & Shear \\ \hline
\begin{tabular}{c} Specim 1 \\ ([PERSON] and [PERSON] 2002) [3] \\ \end{tabular} & 21.1 & 434.4 & 0.151 & 3.90 & 0.0247 & 0.004 & Flexure-shear \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Predicted Results of the Classification Model. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidential.