Application of Random Forest Algorithm in Estimating Dynamic Mechanical Behaviors of Reinforced Concrete Column Members

Abstract

In this paper, an innovative method is put forward for estimating the dynamic mechanical behaviors of reinforced concrete (RC) column members by applying the random forest algorithm. Firstly, the development of dynamic modified coefficient (DMC) predictive models and the realization of the proposed method were elaborated. Then, due to the lack of dynamic loading tests on RC column members, a numerical model of RC columns considering the dynamic modification on flexural, shear and bond-slip behaviors was developed on the OpenSees platform, and the model accuracy and the effectiveness were verified with the available test results. Moreover, by comparing the simulated results of the hysteretic curve using numerical models with different complexities, the influences of dynamic modification and the deformation sub-element were investigated. Furthermore, a numerical experiment database was established to obtain the training data for developing the DMC predictive models of critical mechanical behavior parameters, including the yielding bearing capacity, ultimate bearing capacity and displacement ductility. Finally, the results of feature importance for different input parameters were studied, and the model accuracy was evaluated using the test set and available experimental data. It was revealed that the predictive models developed using the random forest algorithm can be employed to reliably estimate the dynamic mechanical behaviors of RC column members.