## 1 Introduction  
Reinforced concrete (RC) columns are critical load-bearing components in building structures. Accurately and reliably evaluating their mechanical behaviors under dynamic loading is of great importance in the seismic design and analysis of RC structures. It is widely known that RC materials are strain-rate sensitive, meaning that the properties of reinforcement and concrete under various loading rates are quite different [1, 2, 3]. As a result, different mechanical behaviors of RC columns have been measured under dynamic loading and quasi-static loading [4, 5, 6, 7]. Generally, the available dynamic loading tests on RC columns indicate that the bearing capacity and stiffness, as well as the energy dissipation capacity, are increased while the ductility might be decreased and the performance deterioration is aggravated for RC columns under relatively high loading rates [8]. Quite a number of research works have been carried out to test the dynamic mechanical behaviors of RC columns. Among them, the experimental method [9] is the most convincing; however, the test data are limited due to the large financial and human costs. As an alternative, some scholars have made attempts to establish numerical models to simulate the dynamic mechanical behaviors of structural members [10, 11, 12, 13] and further develop empirical models of dynamic increase factor (DIF) to estimate the critical mechanical behavior parameters [14, 15, 16, 17]. Based on the dynamic loading test results and the Bayesianupdating method, [PERSON] et al. [18] developed predictive models of the dynamic modified coefficient (_DMC_) for RC columns, which can be used to estimate the dynamic mechanical behaviors of RC columns under seismic loading. The disadvantages of available studies can be summarized as: (1) the predictive models of DIF at the member level were established on limited experimental or numerical data, and (2) the numerical models are not sufficiently accurate because the dynamic effect on the shear and bond-slip behaviors of RC column members have often been neglected. With the rapid development of data science and computer technology, machine learning has been widely applied to the field of structural engineering in recent years [19; 20; 21; 22]. Many machine learning techniques have been adopted by researchers to predict the mechanical behaviors and failure mode of RC structural members [23; 24; 25]. The empirical-based support vector machine method was employed by [PERSON] et al. [26] to predict the force-deformation backbone with the basic structural parameters of RC columns taken as the inputs. [PERSON] et al. [27] used different machine learning methods to identify different damage states, such as spalling, core crushing and bar buckling of RC bridge piers, and the accuracy of the machine learning-based models was improved as compared with the existing physics-based empirical models. [PERSON] et al. [28] proposed a machine learning-based backbone curve model of force-deformation for flexure- and shear-critical columns, and its robustness and accuracy were demonstrated by comparison with the traditional modeling approaches. By employing ensemble machine learning techniques, [PERSON] et al. [29] studied the failure mode classification and bearing capacity prediction of RC columns. Their proposed method had better performance than the single learning method and the empirical formulas provided by the design codes. For this paper, predictive models of the dynamic modified coefficient (_DMC_) were developed for estimating the dynamic mechanical behaviors of RC columns through machine learning (i.e., the random forest algorithm). In Section 2, the random forest method is introduced for developing _DMC_ predictive models. In Section 3, a numerical model of RC columns considering the dynamic effect is established and validated. Moreover, a numerical database for model training in terms of critical mechanical behavior parameters of RC columns is developed.