Investigation of Buckling Behavior of Beams with Artificial Neural Network

Key Findings & Data:The critical load of beams with different cross-sections was found by numerical analysis and these results were confirmed by analytical analyses.

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Conclusion & Impact:The training and testing data of this model are detailed for I and tubular beams.

🔬Design:Modeling & Simulation

📍Scope:Comprehensive Sample

⚙️Variables:Neural Network Algorithm · Buckling · Beam

Abstract

In this study, the buckling behavior of a beam simply supported at both ends was analyzed analytically and numerically. The critical load of beams with different cross-sections was found by numerical analysis and these results were confirmed by analytical analyses. Then, the Artificial Neural Network (ANN) model was created using the data of different beam sections. An ANN model is presented in order to find the critical load quickly and effectively for beams with different geometries with the obtained data sets. The training and testing data of this model are detailed for I and tubular beams.

Özet

In this study, the buckling behavior of a beam simply supported at both ends was analyzed analytically and numerically. The critical load of beams with different cross-sections was found by numerical analysis and these results were confirmed by analytical analyses. Then, the Artificial Neural Network (ANN) model was created using the data of different beam sections. An ANN model is presented in order to find the critical load quickly and effectively for beams with different geometries with the obtained data sets. The training and testing data of this model are detailed for I and tubular beams.

Keywords

Neural Network Algorithm Buckling Beam Critical Buckling Load buckling beam critical buckling load”

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Cite This Article

DEMİRBAŞ, M. D., OĞUZ, M., ERİŞEN, İ. (2021). Investigation of Buckling Behavior of Beams with Artificial Neural Network. International Scientific and Vocational Studies Journal, Cilt: 5 - Sayı: 1, 94–106. https://ajindex.com/articles/769787

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References (1)

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.