Compressive Capacity Prediction of Stirrup-Confined Concrete Columns Using Neuro-Fuzzy System

Abstract

The compressive capacity of the column is one of the key parameters in the design. The importance of such structural members and their performance under load conditions are very effective in the overall behavior of the structure, and its failure can lead to the collapse of the entire structure. Therefore, determining the capacity of columns is considered an important issue in structural problems. Thus, this article presents an applicable computational framework to predict the compression capacity of stirrups-confined concrete. A machine learning model based on neuro-fuzzy systems was considered to formulate the proposed model. For this purpose, some experimental datasets were gathered from the literature to tune the unknown parameters of the model and evaluate its accuracy. The target, the ratio of the ultimate axial capacity to bearing area, was predicted with consideration of the column properties, including the compressive strength of concrete, stirrups section area, dimension of the stirrups, and the column section. The results showed that the proposed framework could be used as an applicable technique to determine the compressive capacity of the stirrups-confined concrete columns.