International Science Index


10007601

Comparison of ANN and Finite Element Model for the Prediction of Ultimate Load of Thin-Walled Steel Perforated Sections in Compression

Abstract:The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.
References:
[1] Papangelis J. P., Hancock G. J., Computer analysis of thin-walled structural members, Computers & Structures 1995;56:157–76.
[2] Schafer B. W., Adany S., Buckling analysis of cold-formed steel members using CUFSM: conventional and constrained finite strip methods. In: Proceedings of the eighteenth international specialty conference on cold-formed steel structures. Orlando, FL; October 2006.
[3] Bebiano R, Pina P, Silvestre N, Camotim D., GBTUL—buckling and vibration analysis of thin-walled members. DECivil/IST. Technical University of Lisbon; 2008. .
[4] F. Roure, M.M. Pastor, Stub column tests for racking design: Experimental testing, FE analysis and EC3, Thin-Walled Structures, 49(2011):167–184
[5] Peng Zhang, M. Shahria Alam, Experimental investigation and numerical simulation of pallet-rack stub columns under compression load, Journal of Constructional Steel Research, 133 (2017): 282–299.
[6] Xianzhong Zhao, Chong Rena, An experimental investigation into perforated and non-perforated steel storage rack uprights, Thin-Walled Structures ,112 (2017) 159–172.
[7] Claudio Bernuzzi, Fabrizio Maxenti, European alternatives to design perforated thin-walled cold-formed beam–columns for steel storage systems, Journal of Constructional Steel Research ,110 (2015): 121–136
[8] S. N. R. Shah, N. H. RamliSulong, M. Z. Jumaat, et al. State of the art review on the design and performance of steel pallet rack connections. Engineering Failure Analysis. 2016 (66). p.240–258.
[9] Alberto Prieto n, Beatriz Prieto, Eva Martinez Ortigosa, et al, Neural networks: an overview of early research, current frameworks and new challenges, Neuro computing, 214(2016), 242–268.
[10] European Standard EN 15512:2009, Steel static storage systems—adjustable pallet racking systems—principles for structural design. Brussels: European Committee for standardization; 2009.
[11] ANSYS, Inc. Ansys Documentation.