PSI - Issue 21

Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2019) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2019) 000 – 000

www.elsevier.com/locate/procedia

www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 21 (2019) 138–145

© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the 1st International Workshop on Plasticity, Damage and Fracture of Engineering Materials organizers © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ Peer-review under responsibility of the 1st International Workshop on Plasticity, Damage and Fracture of Engineering Materials organizers Abstract In this study a sei mic amper of high-rise structures is analyzed parametrically u ing artificial neural network (ANN). The input data for ANN model w s gen rated using experime tall validat d finite el ment (FE) analys . The study investig es amou t of the absorbed nergy dissipated by the plas ic d formation of the tubes involved in the damper. The etwork used in this study computes the absor ed energy of the damping system in terms of three different variables including diam ter ratio, the thickness and e diameter of the outer tube. To train the network, 90% of th FE results are utiliz d as input a d the capability of the network is examined by the rest 10% of data. It is shown that the trained neural struc ur can stimate the energy dissipation with an error less than 2%. According t he result , it is observed that despite the diameter, increasing in the thickness of the outer tube improves the energy absorption measurably. The results also show that the model with the diameter ratio of 1.6, as a critical design parameter, eflects the optimum absorbed energy among all cases. © 2019 The Authors. Publ shed by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ Peer-review under responsibility of the 1st International Workshop on Plasticity, Damage and Fracture of Engineering Materials organizers 1st International Workshop on Plasticity, Damage and Fracture of Engineering Materials Investigating Energy Absorption Accessible by Plastic Deformation of a Seismic Damper Using Artificial Neural Network B. Paygozar*, S.A. Dizaji Department of Mechanical Engineering, TED University, Kolej, Ankara 06420, Turkey Abstract In this study a seismic damper of high-rise structures is analyzed parametrically using artificial neural network (ANN). The input data for ANN model was generated using experimentally validated finite element (FE) analyses. The study investigates the amount of the absorbed energy dissipated by the plastic deformation of the tubes involved in the damper. The network used in this study computes the absorbed energy of the damping system in terms of three different variables including diameter ratio, the thickness and the diameter of the outer tube. To train the network, 90% of the FE results are utilized as input, and the capability of the network is examined by the rest 10% of data. It is shown that the trained neural structure can estimate the energy dissipation with an error less than 2%. According to the results, it is observed that despite the diameter, increasing in the thickness of the outer tube improves the energy absorption measurably. The results also show that the model with the diameter ratio of 1.6, as a critical design parameter, reflects the optimum absorbed energy among all cases. 1st International Workshop on Plasticity, Damage and Fracture of Engineering Materials Investigating Energy Abso ptio Accessible by Pl stic Deformation of a Seismic Damper Using Artificial Neural Network B. Paygozar*, S.A. Dizaji Department of Mechanical Engineering, TED University, Kolej, Ankara 06420, Turkey Keywords: Energy absorption capacity; hysteresis effect; parametric study; artificial neural network.

Keywords: Energy absorption capacity; hysteresis effect; parametric study; artificial neural network.

* Corresponding author. Tel.: +90-312-585-0270. E-mail address: bahman.paygozar@tedu.edu.tr * Corresponding author. Tel.: +90-312-585-0270. E mail address: bahman.paygozar@t u.edu.tr

2452-3216 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ Peer-review under responsibility of the 1st International Workshop on Plasticity, Damage and Fracture of Engineering Materials organizers 2452 3216 © 2019 Th Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ Peer-review under responsibility of the 1st International Workshop on Plasticity, Damage and Fracture of Engineering Materials organizers

2452-3216 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the 1st International Workshop on Plasticity, Damage and Fracture of Engineering Materials organizers 10.1016/j.prostr.2019.12.095