Support vector machines in structural engineering: a review
Yazarlar (5)
Prof. Dr. Abdulkadir Çevik Gaziantep Üniversitesi, Türkiye
Doç. Dr. Ahmet Emin Kurtoğlu İstanbul Gelişim Üniversitesi, Türkiye
Prof. Dr. Mahmut BİLGEHAN İstanbul Arel Üniversitesi, Türkiye
Prof. Dr. Mehmet Eren Gülşan Gaziantep Üniversitesi, Türkiye
Hasan M Albegmprlı
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Journal of Civil Engineering and Management | ||
| Dergi ISSN | 1392-3730 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2015 |
| Cilt / Sayı / Sayfa | 21 / 3 / 261–281 | DOI | 10.3846/13923730.2015.1005021 |
| Makale Linki | http://www.tandfonline.com/doi/abs/10.3846/13923730.2015.1005021 | ||
| UAK Araştırma Alanları |
Betonarme Yapılar
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| Özet |
| Recent development in data processing systems had directed study and research of engineering towards the creation of intelligent systems to evolve models for a wide range of engineering problems. In this respect, several modeling techniques have been created to simulate various civil engineering systems. This study aims to review the studies on support vector machines (SVM) in structural engineering and investigate the usability of this machine learning based approach by providing three case studies focusing on structural engineering problems. Firstly, the concept of SVM is explained and then, the recent studies on the application of SVM in structural engineering are summarized and discussed. Next, we performed three case studies using the experimental studies provided. Applicability of SVM in structural engineering is confirmed by these case studies. The results showed that SVM is superior to various … |
| Anahtar Kelimeler |
| FRP reinforcement,ultimate load capacity,structural engineering,haunched beams,support vector machines,SFRC corbels,statistical learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 146 |