An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks
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 loa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/11/1/9 |
| _version_ | 1797496912440459264 |
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| author | Seongkyu Chang Sung Gook Cho |
| author_facet | Seongkyu Chang Sung Gook Cho |
| author_sort | Seongkyu Chang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:11:05Z |
| format | Article |
| id | doaj.art-0be878e3d81f4829b902429e519d4c17 |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-10T03:11:05Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-0be878e3d81f4829b902429e519d4c172023-11-23T12:33:32ZengMDPI AGActuators2076-08252021-12-01111910.3390/act11010009An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural NetworksSeongkyu Chang0Sung Gook Cho1Department of Civil Engineering, Gwangju University, 277 Hyodeck-ro, Nam-gu, Gwangju 61743, KoreaR&D Center, Innose Tech Co., Ltd., 30, Songdomirae-ro, Yeonsu-gu, Incheon 21990, KoreaThis 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.https://www.mdpi.com/2076-0825/11/1/9elasto-plasticsteel coil damperartificial neural networkdamperenergy dissipatenonlinear behavior |
| spellingShingle | Seongkyu Chang Sung Gook Cho An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks Actuators elasto-plastic steel coil damper artificial neural network damper energy dissipate nonlinear behavior |
| title | An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks |
| title_full | An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks |
| title_fullStr | An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks |
| title_full_unstemmed | An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks |
| title_short | An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks |
| title_sort | intelligent process to estimate the nonlinear behaviors of an elasto plastic steel coil damper using artificial neural networks |
| topic | elasto-plastic steel coil damper artificial neural network damper energy dissipate nonlinear behavior |
| url | https://www.mdpi.com/2076-0825/11/1/9 |
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