Application of Machine Learning in Prediction of Shear Capacity of Headed Steel Studs in Steel–Concrete Composite Structures
Yazarlar (1)
Doç. Dr. Çiğdem AVCI KARATAŞ Yalova Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Steel Structures (Q4) | ||
| Dergi ISSN | 1598-2351 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Ingilizce | Basım Tarihi | 03-2022 |
| Cilt / Sayı / Sayfa | 22 / 2 / 539–556 | DOI | 10.1007/s13296-022-00589-z |
| Makale Linki | http://dx.doi.org/10.1007/s13296-022-00589-z | ||
| UAK Araştırma Alanları |
Çelik Yapılar
Deprem
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| Özet |
| Headed studs are generally utilized as shear connectors at the interface between steel and concrete in composite structures primarily to transfer longitudinal shear force. This paper presents regression methodologies to predict the shear capacity of headed steel studs by using the concepts of minimax probability machine regression (MPMR) and extreme machine learning (EML). MPMR is carried out based on a minimax probability machine classification. EML is an updated version of a single hidden layer feedforward network. From the experimental data presented in extensive literature, key input parameters influencing the shear capacity have been identified and consolidated. The identified parameters include (i) steel stud shank diameter, (ii) compressive strength of concrete, and (iii) tensile strength of headed steel stud. After careful examination of the data and their limits, about 70–75% of the mixed dataset … |
| Anahtar Kelimeler |
| Extreme machine learning | Headed stud | Minimax probability machine regression | Shear strength | Statistical modeling technique | Steel–concrete composite structure |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 26 |
| Scopus | 29 |
| Google Scholar | 31 |