###### Abstract  
Columns play a very important role in structural performance and, therefore, this paper contributes to the critical need for failure mode prediction of reinforced concrete (RC) columns by exploring the capabilities of random forest machine learning (ML) based on a well-known experimental column database. Known as the PEER structural performance database, it assembles the results of over 400 cyclic, lateral-load tests of reinforced concrete columns. The database describes tests of spiral or circular hoop-confined columns, rectangular tied columns and columns with or without lap splices of longitudinal reinforcement at the critical sections. The efficiency towards the aforementioned goal of supervised ML methods such as random forests using a randomly assigned test set from the Pacific Earthquake Engineering Research Center (PEER) database is examined here. The overall accuracy score for rectangular RC columns is 94% and for circular RC columns is 86%. The latter performances are influenced by the size of the testing and training sets of data and are independent of the number of decision trees in the forest employed in the random forest algorithm. The performances of random forests in postdicting the failure mode of RC columns prove that ML has great promise in revolutionizing the profession of earthquake engineering. e 2023202331328212323132  
are subjected to compressive loads and, in the case of lap splices, the lap splice mechanism of the development of reinforcing bars. Very often, a combination of such mechanisms characterizes the macroscopic behavior of the column, especially in cases of cyclic load reversals. Various predictive models have been developed in the past to determine both the strength as well as the deformation capacity of the columns, with the uncertainty being at least one order of magnitude greater in terms of deformation capacity rather than strength, as evidenced by comparisons with test results [1,2]. System identification and damage detection is a twofold area that utilizes ML to imitate a structural system and predict its deterministic seismic response. Laboratory tests of reinforced concrete (RC) structures have provided one source of data that enables ML methods to identify their failure modes, strength, capacities and constitutive behaviors [3]. ML methods in which the algorithms are used to learn from the data have been used recently for risk assessment and prediction models in civil engineering [4, 5, 6, 7, 8, 9, 10, 11]. In this regard, some studies have focused on predicting failure modes and shear strength for beam-column joints [6, 9, 12, 13]. For instance, [PERSON] et al. (2011) [12] categorized non-ductile joint shear failure versus ductile beam yielding failure for interior beam-column joints. And, [PERSON] et al., 2022 [14] examined the design and application of a low-cycle reciprocating loading test on 23 recycled aggregate concrete-filled steel tube columns and 3 ordinary concrete-filled steel tube columns. Moreover, in the latter study, they applied artificial intelligence to estimate the influence of parameter variation on the seismic performance of concrete columns. Specifically, random forests with hyperparameters tuned by the firefly algorithm were chosen. Similar studies with multi-objective optimization analyses are included in [15]. In this paper, a supervised learning algorithm called the random forest is tested as a predictive model for the first time for its performance in postdicting the failure mode of RC columns against a widely used experimental database originally assembled by [PERSON] and [PERSON] (2004) [16]. Known as the PEER structural performance database, it assembles the results of over 400 cyclic, lateral-load tests of reinforced concrete columns. The database describes tests of spiral or circular hoop-confined columns, rectangular tied columns and columns with or without lap splices of longitudinal reinforcement at the critical sections.