Investigation of Buckling Behavior of Beams with Artificial Neural Network
Abstract
Keywords
Neural Network Algorithm, buckling, beam, critical buckling load”
References
- Referans 1 S. Timoshenko and M.G. James, Theory of Elastic Stability, New York: McGraw-Hill, 1961.
- Referans2. Z.P. Bazant and L. Cedolin, Stability of Structures: Elastic, Inelastic, Fracture and Damage Theories. New York: Oxford University Press. 1991.
- Referans3 S. Albayrak, Yanal burkulma etkisindeki I kesitli kirişlerde ideal desteklerin belirlenmesi ve yapay sinir ağları yaklaşımı, doktora tezi, Karadeniz Teknik Üniversitesi, Fen bilimleri Enstitüsü, 2011.
- Referans 4 M.R. Sheidaii, R. Bahraminejad, “Evaluation of compression member buckling and post-buckling behavior using artificial neural network”, Journal of Constructional Steel Research, vol. 70, p. 71-77, 2012.
- Referans 5 M. Hosseinpour, Y.Sharifi, H. Sharifi, “Neural network application for distortional buckling capacity assessment of castellated steel beams”, Structures, vol. 27, p. 1174-1183, 2020.
- Referans 6 R.R. Kumar, T. Mukhopadhya, K.M. Pandey, S. Dey, “Chapter 5 - Prediction capability of polynomial neural network for uncertain buckling behavior of sandwich plates”, Handbook of Probabilistic Models, p. 131-140, 2020.
- Referans7 Z. Sun, Z. Lei, R. Bai, H. Jiang, J. Zou, Y. Ma, C.Yan, “Prediction of compression buckling load and buckling mode of hat-stiffened panels using artificial neural network”,Engineering Structures, vol. 242, p. 112275, 2021.
- Referans8 F. Susac, E.F. Beznea and N. Baroiu , “Artificial neural network applied to prediction of buckling behavior of the thin-walled box” Advanced Engineering Forum 21, p.141-150, 2016.
- Referans9 Z. Chi, Z. Jiang, M.M. Kamruzzaman, B.A. Hafshejaniü M. Safarpour, “Adaptive momentum-based optimization to train deep neural network for simulating the static stability of the composite structure”. Engineering with Computers, online,1 mart 2021.
- Referans10 S Guzel and E Gurses, “Determination of the 1st Buckling and Collapse Loads for Integrally Stiffened Panels by Artificial Neural Network and Design of Experiment Methodology”, IOP Conf. Ser.: Mater. Sci. Eng. 1024, 012080,2021.