## 4 ML-Based Classification Model for the Determination of Column Failure Modes
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{2}{c}{**Actual Class**} \\ \cline{2-4}  & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Predict Class} & Positive & TP (True Positive) & FP (False Positive) \\ \cline{2-4}  & Negative & FN (False Negative) & TN (True Negative) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion Matrix Concept. scores for shear failure modes are below average, whereas these metrics for flexure failure modes are above average. Therefore, flexure failure modes can be accurately predicted, but predictions for shear failure modes are not accurate. The DT model generally shows below average precision, recall, and F1-scores for shear failure and flexure-shear failure modes, whereas these metrics for flexure failure modes are above average. Therefore, the DT model can accurately predict flexure failure modes but not shear and flexure-shear failure modes. The RF model demonstrates above-average precision, recall, and F1-scores for flexure failure modes, whereas the precision and F1-scores for shear failure modes, excluding recall, are below average. Additionally, for flexure-shear failure modes, the recall and F1-scores, excluding precision, are below the average values. Therefore, flexure failure modes are accurately predicted, but predictions for shear failure modes can be considered conservative. Figure 4 displays the ROC curves for each machine learning model investigated in this study. Excluding the KNN model, the other three models show high AUC values. The AUC values for the flexural failure type are more than 0.95 for all four models. Similarly, the AUC values for the shear failure type and flexural-shear failure type are more than 0.85 for all models except KNN. Figure 3: Confusion matrix of each machine learning methodology for the testing dataset. Table 5 presents the accuracy, precision, recall, F1-score, and AUC values for each failure mode according to the machine learning models based on the confusion matrices. The KNN model exhibited the highest accuracy. Whereas accuracy is the most intuitive indicator of a model's performance, other metrics (precision, recall, and AUC) are preferred when data are imbalanced. Overall, all models successfully predict flexure failure modes over 90% of the time. Based on the average values of precision, recall, F1-score, and AUC, the RF model is determined to have the best performance. The RF model demonstrates excellent performance in predicting the flexural failure type, with precision, recall, F1-score, and AUC values all exceeding 0.9. For the shear failure type, the precision is 0.71 and the recall is 0.83, indicating a relatively lower performance compared with the flexural failure type; however, this is still considered excellent based on the numerical values. For the flexural-shear failure type, the precision is 0.83 and the recall is 0.5, suggesting some limitations in predicting this failure type. The performance of each machine learning model based on the F1-score, which is the harmonic mean of precision and recall, decreases in order of RF, DT, KNN, and ANN. The ANN classifier shows the highest recall for the flexure-shear failure modes along with the RF model, but notably lower recall for shear failure modes is observed compared with the RF and DT models. Considering that the machine-learning-based models developed in this study use simple information to predict column failure modes, a model that makes conservative predictions is deemed acceptable. Therefore, a model with a higher recall for shear failure modes should be selected. Thus, considering the precision, recall, F1-score, AUC, and average values of these indicators for each failure mode, the RF model is shown to provide the best predictions. Figure 4: ROC curve for all machine learning models. Figure 5 illustrates the relative importance of the input variables.