Bearing capacity of steel-caged RC columns under combined bending and axial loads: Estimation based on Artificial Neural Networks

Abstract

The use of steel caging for strengthening a reinforced concrete (RC) column is an economical and common solution. However, the design of the optimum steel cage is a complex task. Artificial Neural Networks (ANN) has shown to be a useful device for engineers to solve tasks related to the modelling and prediction of the behavior of complex engineering problems. This mathematical tool can be trained from a series of inputs in order to obtain a desired output, without the need to reproduce the phenomenon under study. Based on a total of 950 results obtained with a validated finite element (FE) model, this paper presents the use of ANN to predict the axial–bending moment (N–M) interaction diagram of steel-caged RC columns under combined bending and axial loads. The data is arranged in a format of six input parameters taking into account several aspects such as the geometry of the RC column, the size of the steel cage, the concrete compressive strength, the steel yield stress and the axial load level. The output is the bending moment reached by the steel-caged RC column. Since the way of solving the beam–column joint plays a key role in the behavior of the strengthened column, four ANNs are developed in this paper, related to the beam–column connection type: using capitals, using capitals with chemical anchors, using capitals and steel bars, and without any element. The ANNs developed show excellent results, which are far better to those given by three design analytical proposals. Based on the ANNs performed, a simple mathematical expression is developed, which can be used by practitioners when facing the design of a steel-caged RC column subjected to axial loads and bending moments.