  Citation: Chang, S.; Cho, S.G. An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks. Actuators 2022, 11, 9. https:// doi.org/10.3390/act11010009 Academic Editor: Haim Abramovich Received: 2 November 2021 Accepted: 27 December 2021 Published: 31 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 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/). actuators Article An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks Seongkyu Chang 1 and Sung Gook Cho 2, * 1 Department of Civil Engineering, Gwangju University, 277 Hyodeck-ro, Nam-gu, Gwangju 61743, Korea; skchang@gwangju.ac.kr 2 R&D Center, Innose Tech Co., Ltd., 30, Songdomirae-ro, Yeonsu-gu, Incheon 21990, Korea * Correspondence: sgcho@innose.co.kr Abstract: This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program. Keywords: elasto-plastic; steel coil damper; artificial neural network; damper; energy dissipate; nonlinear behavior 1. Introduction External loads such as earthquakes and winds can induce large vibrations in long-span bridges and high-rise buildings, and these vibrations can last for a long time because of low damping. Isolation systems and dampers have accordingly been applied to these structures to improve their safety and usability by controlling the vibrations. The isolators and vibration control devices have been installed in both newly established structures and to seismic retrofit existing structures [1]. For example, flexible piping systems respond to seismic motion with large displacements. To reduce the large deformation produced by earthquakes, a damper can be installed in the piping system [2]. Seismic isolation devices are used to prevent vibration within a structure, and there are several types of dampers used to isolate structures, depending on the purpose. Such dampers can be classified into elasto-plastic dampers, viscous dampers, mass dampers, and friction dampers. Elasto-plastic dampers work by absorbing energy generated by the hysteretic deformation of a metal material, such as a steel bar, but the fatigue characteristics of repeated loading should be considered [3]. Viscous dampers are not highly affected by stiffness, so they can control from small to large vibrations. However, they are highly dependent on temperature [4,5]. Mass dampers such as a tuned mass damper were applied to various mechanical and civil structures [6–8]. A friction damper has the advantage that its damping force can be arbitrarily changed by the energy absorbed by friction [9]. Added damping and added stiffness (ADAS), triangular added damping and added stiffness (TADAS), unbonded bracing, loop-shaped damper, and lead damper devices were Actuators 2022, 11, 9. https://doi.org/10.3390/act11010009 https://www.mdpi.com/journal/actuators