## 1 Introduction  
Determining the failure mode of reinforced concrete (RC) columns is a crucial component in the performance evaluation process of buildings and bridges and is closely related to structural safety and rapid strength reduction. In RC frame structures built before the implementation of seismic design, the failure mode of the columns can impact the overall behavior of the structure. Structural engineers must consider reinforcement methods for columns with existing seismic vulnerabilities, particularly those with inadequate structural details. The failure mode of columns is influenced by various structural details (longitudinal reinforcement ratio, transverse reinforcement ratio, etc.) and geometric characteristics (column length, boundary conditions, etc.). This has been verified through various previously conducted experimental studies [1; 2; 3]. [PERSON] et al. [4] attempted to classify the failure modes of RC columns using the shear strength of stirrups. [PERSON] et al. [5] considered parameters such as the shear-span-to-depth ratio, and hoop-spacing-to-depth ratio. Various variables reflecting the failure modes of columns have been investigated via experimental studies. Based on the findings of these studies, standards have been developed, proposing an experiment-based equation to calculate the shear capacity of columns. To determine the failure mode of these columns, comparisons between nonlinear static pushover analysis based on fiber elements and the code-based shear capacity equation specified by American Society of Civil Engineering (ASCE) 41-23 may be necessary. To quickly assess the failure modes of columns, this study utilized a machine learning approach considering several parameters for the column details. Machine learning models for predicting the failure modes of RC structures have been previously investigated. For example, the performance of artificial neural networks (ANNs) was evaluated in several previous studies considering their application toward the strength prediction of RC columns [6] or seismic research on RC frames [7]. [PERSON] et al. [8] evaluated the failure probability of structures using a support vector machine (SVM) approach among other classification techniques. This technique was subsequently used by [PERSON] and [PERSON] [9] to classify the failure modes in RC beam-column joints. Another machine learning model that can be utilized in practice without complex calculations is the decision tree (DT). In a previous study, the DT model classified the failure modes in RC joints [10]. Because the failure modes of columns vary depending on the overall strength [11], energy dissipation capacity, and ductility of RC columns [12], failure grades can be used to aid engineers in selecting optimized characteristics to prevent hazardous failures. [PERSON] et al. [13] proposed a RF model for predicting the failure modes of RC columns and shear walls and conducted a study to identify and rank important variables affecting failure modes. The RF models provide more than 80% accuracy for when using a testing dataset for columns and shear walls. Additionally, [PERSON] and [PERSON] [14] investigated the use of ensemble machine learning, or more specifically the adaptive boosting (AdaBoost) algorithm, to predict the failure modes and bearing capacity in rectangular RC columns with 18 detailed input parameters. Compared with the previous study, this study developed a machine-learning-based model with the simpler structural details predicting the column failure modes. [PERSON] and [PERSON] [15] developed and validated a methodology using the RF machine learning approach to predict the failure types of rectangular and circular RC columns utilizing the PEER database. The study investigated the impact of key design variables on the ductility and failure types of columns. Based on the confusion matrix, the accuracy for rectangular and circular RC columns was precisely classified at 94% and 86%, respectively.