Neural network prediction of the ultimate capacity of shear stud connectors on composite beams with profiled steel sheeting
Küçük Resim Yok
Tarih
2013
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER SCIENCE BV
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, the efficiency of different Artificial Neural Networks (ANNs) in predicting the ultimate shear capacity of shear stud connectors is explored. Experimental data involving push-out test specimens of 118 composite beams from an existing database in the literature were used to develop the ANN model. The input parameters affecting the shear capacity were selected as sheeting, stud dimensions, slab dimensions, reinforcement in the slab and concrete compression strength. Each parameter was arranged in an input vector and a corresponding output vector, which includes the ultimate shear capacity of composite beams. For the experimental test results, the ANN models were trained and tested using three layered back-propagation methods. The prediction performance of the ANN was obtained. In addition to these, the paper presents a short review of the codes in relation to the design of composite beams. The accuracy of the codes in predicting the ultimate shear capacity of composite beams was also examined in a comparable way using the same test data. At the end of the study, the effect of all parameters is also discussed. The study concludes that all ANN models predict the ultimate shear capacity of beams better than codes. (C) 2013 Sharif University of Technology. All rights reserved.
Açıklama
Anahtar Kelimeler
Shear stud, Shear connection, Composite beams, Push-out tests, Artificial neural network
Kaynak
SCIENTIA IRANICA
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
20
Sayı
4
