Seismic response prediction of asymmetric structures with SMA dampers using machine learning algorithms
Yazarlar (6)
Ananth Parghi
S. V. National Institute Of Technology, Hindistan
S. V. National Institute Of Technology, Hindistan
Jay Gohel
S. V. National Institute Of Technology, Hindistan
S. V. National Institute Of Technology, Hindistan
Apurwa Rastogi
S. V. National Institute Of Technology, Hindistan
S. V. National Institute Of Technology, Hindistan
Dr. Öğr. Üyesi Melda Yücel İstanbul Aydın Üniversitesi, Türkiye
Doç. Dr. Çiğdem AVCI KARATAŞ Yalova Üniversitesi, Türkiye
Snehal Mevada
Birla Vishvakarma Mahavidyalaya Engineering College, Hindistan
Birla Vishvakarma Mahavidyalaya Engineering College, Hindistan
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Asian Journal of Civil Engineering | ||
| Dergi ISSN | 1563-0854 Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCOPUS | ||
| Makale Dili | Türkçe | Basım Tarihi | 04-2025 |
| Kabul Tarihi | 25-03-2025 | Yayınlanma Tarihi | 21-04-2025 |
| Cilt / Sayı / Sayfa | 26 / 6 / 2475–2497 | DOI | 10.1007/s42107-025-01323-w |
| Makale Linki | https://doi.org/10.1007/s42107-025-01323-w | ||
| UAK Araştırma Alanları |
Çelik Yapılar
Deprem
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| Özet |
| The dynamic response of asymmetric structures to seismic forces is challenging due to mass, stiffness, and damping distribution irregularities. Shape memory alloy (SMA) dampers have successfully dealt with these issues because of their distinctive super elasticity and energy dissipation characteristics. In this work, we study regression algorithms’ effectiveness in predicting the seismic behavior of asymmetric structures installed with SMA dampers. A numerical simulation produces a comprehensive dataset of structural parameters consisting of the structure’s varying periods, frequency ratios, and eccentricity ratios. The critical responses of structures, including lateral and torsional displacement, lateral and torsional acceleration, and stiff and flexible edge damper forces, are predicted using machine learning (ML) techniques, artificial neural networks, decision trees, support vector machines, ensemble bagged trees … |
| Anahtar Kelimeler |
| Advanced materials | Asymmetric structure | Machine learning | Regression algorithms | Seismic response prediction | Shape memory alloys | Structural eccentricity |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 3 |
| Google Scholar | 3 |