Abstract
The rack column is one of the essential elements in the pallet rack system. However, due to its distinctive perforation feature, it is challenging to analyze its stability using traditional theories for cold-formed steel structures. In this paper, we are interested in the comparison analysis of strength prediction on the perforated columns using finite element method (FEM), regression analysis (RA) and artificial neural network (ANN) methods respectively. First, a refined finite element (FE) model considering the perforation and nonlinearity behavior was generated and calibrated against the experimental results. Subsequently, the validated FE model was used to perform the parametric analysis for the different holes in columns. Given experimental and simulated data, a regression model with an equivalent thickness was proposed for the design strength prediction of thin-walled steel perforated sections. For comparison of the RA model, two powerful tools such as the FEM and ANN are also employed to predict the design strength of different perforated sections. Four indicators were used to assess the accuracy and generalization performance of the three models, including the root mean square error, the mean absolute percentage error, the correlation coefficient and the mean relative percentage. The obtained results show that although they both have good consistency, FEM still slightly outperforms the other two models. Since the values calculated from ANN and regression models are usually smaller than the experimental data, they are reasonably recommended as effective and safer design tools than FEM models from the perspective of engineering applications.
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References
Alberto Prieto n., Beatriz Prieto, Eva Martinez Ortigosa, et al. (2016). Neural networks: an overview of early research, current frameworks and new challenges, Neuro computing,214, 242–268.
Al-Jabri, K. S., & Al-Alawi, S. M. (2010). An advanced ANN model for predicting the rotational behaviour of semi-rigid composite joints in fire using the back-propagation paradigm. International Journal of Steel Structures, 10(4), 337–347.
ANSYS Inc. (2010). ANSYS mechanical APDL structural analysis guide. ANSYS release 13.0, USA.
Baldassino, N., Bernuzzi, C., Di Gioia, A., & Simoncelli, M. (2019). An experimental investigation on solid and perforated steel storage racks uprights. Journal of Constructional Steel Research, 155, 409–425.
Bernuzzi, C., & Maxenti, F. (2015). European alternatives to design perforated thin-walled cold-formed beam–columns for steel storage systems. Journal of Constructional Steel Research, 110, 121–136.
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.
British Standard EN 1993-1-3:2006. (2006). Eurocode 3—Design of steel structures—Part 1–3: General rules—Supplementary rules for cold-formed members and sheeting.
Casafont, M., Pastor, M., Bonada, J., et al. (2012). Linear buckling analysis of perforated steel storage rack columns with the Finite Strip Method. Thin-Walled Structures, 61, 71–85.
Chojaczyka, A. A., Teixeira, A. P., Neves, L. C., Cardoso, J. B., & Guedes Soares, C. (2015). Review and application of artificial neural networks models in reliability analysis of steel structures. Structural Safety, 52, 78–89.
Davies, J. M., Leach, P., & Taylor, A. (1997). The design of perforated cold-formed steel sections subject to axial load and bending. Thin-Walled Structures, 29, 141–157.
Elias, G. C., et al. (2018). Ultimate load of steel storage systems uprights. Engineering Structures, 170, 53–62.
El-Kassas, E. M. A., Mackie, R. I., & El-Sheikh, A. I. (2001). Using networks in cold-formed steel design. Computer and Structures, 79, 1687–1696.
European Standard EN 15512:2009. (2009). Steel static storage systems—Adjustable pallet racking systems—Principles for structural design. Brussels: European Committee for standardization.
European Standard EN 1993-1-5:2006. (2009). Eurocode 3—Design of steel structures—Part 1–5: Plated structural elements.
Freitas, A. M. S., Freitas, M. S. R., & Souza, F. T. (2005). Analysis of steel storage rack columns. Journal of Constructional Steel Research, 61, 1135–1146.
GB 50017-2003. (2003). Code for design of steel structures (in Chinese).
GB/T228.1-2002. (2002). Metal tensile test method (in Chinese).
Kulatunga, M. P., & Macdonald, M. (2013). Investigation of cold-formed steel structural members with perforations of different arrangements subjected to compression loading. Thin-Walled Structures, 67, 78–87.
Martins, A. D., Camotim, D., & Dinis, P. B. (2017). On the direct strength design of cold-formed steel columns failing in local distortional interactive modes. Thin-Walled Structures, 120, 432–445.
MATLAB. (2017). UserGuide. Retrieved from http://www.mathworks.com/support.
Nedelcu, M. (2014). Buckling mode identification of perforated thin-walled members by using GBT and shell FEA. Thin-Walled Structure, 82, 67–81.
Papangelis, J. P., & Hancock, G. J. (1995). Computer analysis of thin-walled structural members. Computers & Structures, 56, 157–176.
Pastor, M. M., Casafont, M., Bonada, J., & Roure, F. (2014). Imperfection amplitudes for nonlinear analysis of open thin-walled steel cross-sections used in rack column uprights. Thin-Walled Structures, 76, 28–41.
Roure, F., Pastor, M. M., Casafont, M., & Somalo, M. R. (2011). Stub column tests for racking design: Experimental testing, FE analysis and EC3. Thin-Walled Structures, 49, 167–184.
Saaty, T. L. (1994). How to make a decision: the analytic hierarchy process. Interfaces, 14, 19–43.
Schafer, B. W., & Ádány, S. (2006). Buckling analysis of cold-formed steel members using cufsm: Conventional and constrained finite strip methods. Rolla: University of Missouri-Rolla.
Shah, S. N. R., RamliSulong, N. H., Jumaat, M. Z., et al. (2016). State of the art review on the design and performance of steel pallet rack connections. Engineering Failure Analysis, 66, 240–258.
Stoffel, M., Bamer, F., & Markert, B. (2018). Artificial neural networks and intelligent finite elements in non-linear structural mechanics. Thin-Walled Structures, 131, 102–106.
Yao, X., Guo, Y., & Li, Y. (2016). Effective width method for distortional buckling design of cold-formed lipped channel sections. Thin-Walled Structures, 109, 344–351.
Zhang, P., & Alam, M. S. (2017). Experimental investigation and numerical simulation of pallet-rack stub columns under compression load. Journal of Constructional Steel Research, 133, 282–299.
Zhao, X., Rena, C., & Qin, R. (2017). An experimental investigation into perforated and non-perforated steel storage rack uprights. Thin-Walled Structures, 112, 159–172.
Acknowledgements
The writers gratefully acknowledge the financial support provided by National Key R&D Program of China (2017YFB1304000), Shanghai Sailing Program (19YF1401600), Research Program of Shanghai Science and Technology Committee (17DZ2283800).
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Appendix
Appendix
Column type | Parameter | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
WW (mm) | CT (mm) | FW (mm) | OS (mm) | CL (mm) | RHA (%) | BN | RAN | RN | DSC (MPa) | |
M45-1.5 | 45 | 1.5 | 45 | 25 | 300 | 18.205 | 8 | 4 | 0 | 225.24 |
M45-1.5 | 45 | 1.5 | 45 | 25 | 300 | 16.472 | 8 | 4 | 0 | 228.85 |
M45-1.5 | 45 | 1.5 | 45 | 25 | 300 | 15.605 | 8 | 4 | 0 | 229.27 |
M60-1.8 | 60 | 1.8 | 55 | 34 | 350 | 16.525 | 8 | 4 | 0 | 300.16 |
M60-1.8 | 60 | 1.8 | 55 | 34 | 350 | 14.951 | 8 | 4 | 0 | 303.07 |
M60-1.8 | 60 | 1.8 | 55 | 34 | 350 | 14.164 | 8 | 4 | 0 | 303.89 |
M60-2 | 60 | 2.0 | 55 | 34 | 350 | 16.525 | 8 | 4 | 0 | 316.94 |
M60-2 | 60 | 2.0 | 55 | 34 | 350 | 14.951 | 8 | 4 | 0 | 315.39 |
M60-2 | 60 | 2.0 | 55 | 34 | 350 | 14.164 | 8 | 4 | 0 | 316.6 |
M75-1.8 | 75 | 1.8 | 58 | 45 | 400 | 14.222 | 12 | 4 | 1 | 302.28 |
M75-1.8 | 75 | 1.8 | 58 | 45 | 400 | 12.868 | 12 | 4 | 1 | 312.62 |
M75-1.8 | 75 | 1.8 | 58 | 45 | 400 | 12.190 | 12 | 4 | 1 | 313.68 |
M75-2 | 75 | 2.0 | 58 | 45 | 400 | 14.222 | 12 | 4 | 1 | 317.84 |
M75-2 | 75 | 2.0 | 58 | 45 | 400 | 12.868 | 12 | 4 | 1 | 322.13 |
M75-2 | 75 | 2.0 | 58 | 45 | 400 | 12.190 | 12 | 4 | 1 | 323.76 |
M90A-1.8 | 90 | 1.8 | 65 | 50 | 400 | 11.852 | 12 | 4 | 1 | 289.59 |
M90A-1.8 | 90 | 1.8 | 65 | 50 | 400 | 10.723 | 12 | 4 | 1 | 292.44 |
M90A-1.8 | 90 | 1.8 | 65 | 50 | 400 | 10.159 | 12 | 4 | 1 | 294.01 |
M90A-2 | 90 | 2.0 | 65 | 50 | 400 | 11.287 | 12 | 4 | 1 | 276.06 |
M90A-2 | 90 | 2.0 | 65 | 50 | 400 | 11.852 | 12 | 4 | 1 | 275.33 |
M90A-2 | 90 | 2.0 | 65 | 50 | 400 | 10.723 | 12 | 4 | 1 | 277.98 |
M90A-2 | 90 | 1.8 | 78 | 50 | 400 | 11.287 | 12 | 4 | 1 | 282.89 |
M90B-1.8 | 90 | 1.8 | 78 | 50 | 400 | 11.852 | 12 | 4 | 1 | 274.31 |
M90B-1.8 | 90 | 1.8 | 78 | 50 | 400 | 10.723 | 12 | 4 | 1 | 282.59 |
M90B-1.8 | 90 | 1.8 | 78 | 50 | 400 | 10.159 | 12 | 4 | 1 | 283.99 |
M90B-1.8 | 90 | 2.0 | 78 | 50 | 400 | 11.287 | 12 | 4 | 1 | 282.54 |
M90B-2 | 90 | 2.0 | 78 | 50 | 400 | 11.852 | 12 | 4 | 1 | 273.19 |
M90B-2 | 90 | 2.0 | 78 | 50 | 400 | 10.723 | 12 | 4 | 1 | 275.43 |
M90B-2 | 90 | 2.0 | 78 | 50 | 400 | 10.159 | 12 | 4 | 1 | 276.71 |
M100A-2 | 100 | 2.0 | 90 | 52 | 400 | 10.667 | 20 | 4 | 3 | 279.81 |
M100A-2 | 100 | 2.0 | 90 | 52 | 400 | 9.651 | 20 | 4 | 3 | 289.77 |
M100A-2 | 100 | 2.0 | 90 | 52 | 400 | 9.143 | 20 | 4 | 3 | 290.68 |
M100A-2.5 | 100 | 2.5 | 90 | 52 | 400 | 10.667 | 20 | 4 | 3 | 251.97 |
M100A-2.5 | 100 | 2.5 | 90 | 52 | 400 | 9.651 | 20 | 4 | 3 | 254.15 |
M100A-2.5 | 100 | 2.5 | 90 | 52 | 400 | 9.143 | 20 | 4 | 3 | 255.42 |
M100A-2.5 | 100 | 2.0 | 100 | 52 | 400 | 10.159 | 12 | 4 | 1 | 260.49 |
M100B-2 | 100 | 2.0 | 100 | 52 | 400 | 10.667 | 12 | 4 | 1 | 236.7 |
M100B-2 | 100 | 2.0 | 100 | 52 | 400 | 9.651 | 12 | 4 | 1 | 238.26 |
M100B-2 | 100 | 2.0 | 100 | 52 | 400 | 9.143 | 12 | 4 | 1 | 239.85 |
M100B-2 | 100 | 2.5 | 100 | 52 | 400 | 10.159 | 12 | 4 | 1 | 249.98 |
M100B-2.5 | 100 | 2.5 | 100 | 52 | 400 | 10.667 | 12 | 4 | 1 | 255.99 |
M100B-2.5 | 100 | 2.5 | 100 | 52 | 400 | 9.651 | 12 | 4 | 1 | 256.78 |
M100B-2.5 | 100 | 2.5 | 100 | 52 | 400 | 9.143 | 12 | 4 | 1 | 259.57 |
M100C-2 | 100 | 2.0 | 130 | 52 | 500 | 10.159 | 20 | 4 | 3 | 236.12 |
M100C-2 | 100 | 2.0 | 130 | 52 | 500 | 10.667 | 20 | 4 | 3 | 234.98 |
M100C-2 | 100 | 2.0 | 130 | 52 | 500 | 9.651 | 20 | 4 | 3 | 237.25 |
M100C-2 | 100 | 2.0 | 130 | 52 | 500 | 9.143 | 20 | 4 | 3 | 235.52 |
M100C-3 | 100 | 3.0 | 130 | 52 | 500 | 10.667 | 20 | 4 | 3 | 281.78 |
M100C-3.5 | 100 | 3.0 | 130 | 52 | 500 | 9.651 | 20 | 4 | 3 | 304.92 |
M100C-3.5 | 100 | 3.0 | 130 | 52 | 500 | 9.143 | 20 | 4 | 3 | 305.71 |
M120-2.5 | 120 | 2.5 | 95 | 76 | 500 | 8.466 | 12 | 4 | 1 | 274.23 |
M120-2.5 | 120 | 2.5 | 95 | 76 | 500 | 8.042 | 12 | 4 | 1 | 275.73 |
M120-2.5 | 120 | 2.5 | 95 | 76 | 500 | 7.619 | 12 | 4 | 1 | 276.97 |
M120-3 | 120 | 3.0 | 95 | 76 | 500 | 8.466 | 12 | 4 | 1 | 257.01 |
M120-3 | 120 | 3.0 | 95 | 76 | 500 | 8.889 | 12 | 4 | 1 | 256.75 |
M120-3 | 120 | 3.0 | 95 | 76 | 500 | 8.042 | 12 | 4 | 1 | 259.56 |
M120-3 | 120 | 3.0 | 95 | 76 | 500 | 7.619 | 12 | 4 | 1 | 262.63 |
M120-3.5 | 120 | 3.5 | 150 | 76 | 500 | 8.889 | 20 | 4 | 3 | 264.12 |
M120-3.5 | 120 | 3.5 | 150 | 76 | 500 | 8.042 | 20 | 4 | 3 | 267.17 |
M120-3.5 | 120 | 3.5 | 150 | 76 | 500 | 7.619 | 20 | 4 | 3 | 268.12 |
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Lyu, Z., Zhang, J., Zhao, N. et al. A Comparative Study on the Performance of FEM, RA and ANN Methods in Strength Prediction of Pallet-Rack Stub Columns. Int J Steel Struct 20, 1509–1526 (2020). https://doi.org/10.1007/s13296-020-00386-6
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DOI: https://doi.org/10.1007/s13296-020-00386-6