Data-Driven Approach to Predict the Plastic Hinge Length of Reinforced Concrete Columns and Its Application

Abstract

Inelastic response of reinforced concrete columns to combined axial and flexural loading is characterized by plastic deformations localized in small regions, which are idealized as plastic hinges. Under extreme events such as earthquakes, the load-carrying and deformation capacities of reinforced concrete beam/columns are highly dependent on the accuracy of this idealization for which the plastic hinge length is a key parameter. From a design perspective, a reinforced concrete column can only attain the ductility characteristics prescribed by its performance level if it is provided with sufficient confinement along the length of its plastic hinge zones. From an analysis standpoint, an efficient, nonlocalized, and objective finite-element simulation of column behavior requires accurate plastic hinge length definitions. This paper presents a novel data-driven model for predicting the plastic hinge length of reinforced concrete columns and its implementation in force-based fiber beam-column elements. The model is based on an ensemble machine learning algorithm named adaptive boosting (AdaBoost) and is trained using the results of 133 reinforced concrete column tests conducted in the period from 1984 to 2013. The performance of the model is assessed using the 10-fold cross-validation technique. It is shown that the prediction accuracy achieved using the proposed method is considerably higher than those of state-of-the-art empirical relationships and several other highly effective machine learning base models. Furthermore, numerical experiments reveal that the force-based beam-column models using plastic hinge length predictions of the developed model closely resemble the monotonic and cyclic behavior observed in laboratory experiments.