http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Yasser Sharifi,Mahmoud Hosseinpour,Adel Moghbeli,Hojjat Sharifi 한국강구조학회 2019 International Journal of Steel Structures Vol.19 No.5
A new model based on Artifi cial Neural Network (ANN) was established as a trustworthy technique for predicting ultimate lateral torsional buckling (LTB) capacity of castellated steel beams. The required information for training, validating and testing of the developed model obtained from a reliable database. Consequently, a new formulation based on the ANN has been off ered for predicting the failure load of castellated steel beams exposed to LTB. All parameters which may aff ect the LTB capacity of castellated beams were considered for presentation of this formula. Then, outcomes of the proposed formula were compared with predictions of Australian Standard (AS4100) for LTB capacity of castellated beams. This comparison indicated that proposed formula has a good performance for prediction of ultimate strength in castellated beams subjected to LTB. At the end, Garson’s algorithm has been established as a sensitivity analysis to determinate importance of each input in the proposed formula.