Document Type
Article
Publication Date
10-27-2021
Publication Title
Applied Sciences
Volume
11
Issue
21
First page number:
1
Last page number:
28
Abstract
Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the occurrence of residual displacement is inevitable. To address this issue, in this study, a new smart type of bar hysteretic dampers equipped with shape memory alloy (SMA) bars with recentring features, as the supplementary damper, is introduced and investigated. In this regard, 630 numerical models of SMA-equipped bar hysteretic dampers (SMA-BHDs) were constructed based on experimental samples with different lengths, numbers, and cross sections of SMA bars. Furthermore, by utilising hysteresis curves and the corresponding ideal bilinear curves, the role of geometrical and mechanical parameters in the cyclic behaviour of SMA-BHDs was examined. Due to the deficiency of existing analytical models, proposed previously for steel bar hysteretic dampers (SBHDs), to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs accurately, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) approaches. The results showed that, although the ANN models outperform GMDH ones, both ANN-and GMDH-based models can accurately estimate the linear and nonlinear behaviour of SMA-BHDs in pre-and post-yield parts with low errors and high accuracy and consistency.
Keywords
Artificial neural network (ANN); Group method of data handling (GMDH); Hysteresis curves; Shape memory alloy (SMA); SMA-equipped bar hysteretic dampers (SMA-BHDs)
Disciplines
Social and Behavioral Sciences
File Format
File Size
12591 KB
Rights
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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Farhangi, V.,
Jahangir, H.,
Eidgahee, D.,
Karimipour, A.,
Javan, S.,
Hasani, H.,
Fasihihour, N.,
Karakouzian, M.
(2021).
Behaviour Investigation of Sma-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques.
Applied Sciences, 11(21),
1-28.
http://dx.doi.org/10.3390/app112110057