Citation: Naderpour, H.; Akbari, M.; Mirrashid, M.; Kontoni, D.-P.N. Compressive Capacity Prediction of Stirrup-Confined Concrete Columns Using Neuro-Fuzzy System. Buildings 2022, 12, 1386. https:// doi.org/10.3390/buildings12091386 Academic Editor: Jianguang Fang Received: 10 August 2022 Accepted: 2 September 2022 Published: 5 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). buildings Article Compressive Capacity Prediction of Stirrup-Confined Concrete Columns Using Neuro-Fuzzy System Hosein Naderpour 1 , Mahdi Akbari 1 , Masoomeh Mirrashid 1 and Denise-Penelope N. Kontoni 2,3, * 1 Faculty of Civil Engineering, Semnan University, Semnan 3513119111, Iran 2 Department of Civil Engineering, School of Engineering, University of the Peloponnese, GR-26334 Patras, Greece 3 School of Science and Technology, Hellenic Open University, GR-26335 Patras, Greece * Correspondence: kontoni@uop.gr 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. Keywords: axial load; concrete column; compressive capacity; stirrup; neuro-fuzzy 1. Introduction In building structures, the role of columns in the overall performance of the structure is very important. The failure of this element can lead to a great reduction in the strength and even collapse of the whole structure. Therefore, determining the capacity of the columns is one of the most important parameters considered in the design process. Reinforced Concrete (RC) columns, as an axial member, should be able to perform optimally against axial pressures and meet the designer’s expectations. Researchers have conducted various laboratory and analytical investigations to understand the behavior of columns better and improve their performance. Dujmovi´ c et al. [1] studied the composite columns subjected to axial compressive and bending loads. They found that the design of such columns should be based on the combination of loads. The behavior of RC columns under axial compression loads was investigated by Jain et al. [2]. The proposed effective strengthening methods for the damaged columns concluded that the most efficient technique combines near-surface mounting and external bonding. Miao and Zheng [3] experimentally studied the effects of bond stress on the compressive behavior of RC columns and developed a formulation to estimate the bearing capacity [4–6]. Concrete core has a significant role in the compressive behavior of RC columns. Therefore, some researchers studied this part of columns and its improvements to increase the compressive capacity. Sunayana and Barai [7] studied the performance of RC columns under compression incorporated by recycled coarse aggregates and fly ash. Their results indicated that the load capacity of the columns with recycled aggregates was higher than Buildings 2022, 12, 1386. https://doi.org/10.3390/buildings12091386 https://www.mdpi.com/journal/buildings