Enhancing seismic design of non-structural components implementing artificial intelligence approach: Predicting component dynamic amplification factors
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Enhancing seismic design of non-structural components implementing artificial intelligence approach: Predicting component dynamic amplification factors
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| Автор |
Bhavani, B. D.
Challagulla, S. P. Noroozinejad, Farsangi, E. Hossain, I. Manne, M. |
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| Тематика |
DYNAMIC INTERACTION
INDIA PRIMARY STRUCTURE SECONDARY STRUCTURE TELANGANA TUNING RATIO ACCELERATION NEURAL NETWORKS SEISMOLOGY COMPONENT DYNAMICS DYNAMIC AMPLIFICATION FACTORS DYNAMIC INTERACTION INDIA NON-STRUCTURAL COMPONENTS PRIMARY STRUCTURES SECONDARY STRUCTURES SEISMIC PERFORMANCE TELANGANUM TUNING RATIO SEISMIC DESIGN |
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| Описание |
The seismic performance of non-structural components (NSCs) has been the focus of intensive study during the last few decades. Modern building codes define design forces on components using too simple relationships. The component accelerates faster than the floor acceleration to which it is connected. Therefore, component dynamic amplification factors (CDAFs) are calculated in this work to quantify the amplification in the acceleration of NSCs for the various damping ratios and tuning ratios of the NSC, and the primary structural periods. From the analysis results, it was observed that CDAF peaks are either underestimated or overestimated by the code-based formulae. A prediction model to ascertain the CDAFs was also developed using artificial neural networks (ANNs). Following that, the suggested model is contrasted with the established relationships from the past research. The ANN model's coefficient of correlation (R) was 0.97. Hence, using an ANN algorithm reduces the necessity of laborious and complex analysis. ©2023 The author(s).
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| Дата |
2024-04-05T16:28:22Z
2024-04-05T16:28:22Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Bhavani, BD, Challagulla, S, Noroozinejad Farsangi, E, Hossain, I & Manne, M 2023, 'Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors', International Journal of Engineering: Transactions A: Basics, Том. 36, № 7, стр. 1211-1218. https://doi.org/10.5829/IJE.2023.36.07A.02
Bhavani, B. D., Challagulla, S., Noroozinejad Farsangi, E., Hossain, I., & Manne, M. (2023). Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors. International Journal of Engineering: Transactions A: Basics, 36(7), 1211-1218. https://doi.org/10.5829/IJE.2023.36.07A.02 1728-144X Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165056482&doi=10.5829%2fije.2023.36.07a.02&partnerID=40&md5=29a3f6c18956ea080b4151a58dfb0d6a https://www.ije.ir/article_170047_592497291dcb9314bf3f75c3d718aead.pdf http://elar.urfu.ru/handle/10995/130647 10.5829/ije.2023.36.07a.02 85165056482 001030701300002 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Materials and Energy Research Center
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| Источник |
International Journal of Engineering
International Journal of Engineering, Transactions B: Applications |
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