Prediction of Axial Load Capacity of Reinforced Concrete Column Using Artificial Neural Network

The axial load capacity (ALC) of a reinforced concrete (RC) column is a fundamental aspect of a structure. Thus, the systematic computation process plays a vital role in building design. It is widely encouraged to use artificial neural network (ANN) for this uncommon problem to predict the precise outcome. This study used three different algorithms of the ANN model to forecast the ALC of an RC column provided by its specific properties. These are Bayesian regularization backpropagation (BRB), Levenberg-Marquardt, and scaled conjugate gradient. The given dataset from the literature was trained in MATLAB software, and it was found that the BRB algorithm has a more positive performance compared to other algorithms. This study presents a detailed methodology and results of the model. Utilizing ANN would provide a more accurate and robust tool for predicting and giving new recommendations for professionals to design safer, more economical, and more efficient structures.