475
Views
7
CrossRef citations to date
0
Altmetric
Original Articles

Energy absorption prediction and optimization of corrugation-reinforced multicell square tubes based on machine learning

, , , , , & show all
Pages 5511-5529 | Received 24 Apr 2021, Accepted 16 Jul 2021, Published online: 28 Jul 2021

References

  • S. Yao, Z. Li, J. Yan, P. Xu, and Y. Peng, Analysis and parameters optimization of an expanding energy-absorbing structure for a rail vehicle coupler, Thin-Wall. Struct., vol. 125, pp. 129–139, 2018. DOI: 10.1016/j.tws.2018.01.011.
  • S. Xie, X. Du, H. Zhou, D. Wang, and Z. Feng, Analysis of the crashworthiness design and collision dynamics of a subway train, Proc. Inst. Mech. Eng. F., vol. 234, no. 10, pp. 1117–1128, 2020. DOI: 10.1177/0954409719880770.
  • W. Ma, Z. Li, and S. Xie, Crashworthiness analysis of thin-walled bio-inspired multi-cell corrugated tubes under quasi-static axial loading, Eng. Struct., vol. 204, pp. 110069, 2020. DOI: 10.1016/j.engstruct.2019.110069.
  • T. Wierzbicki and W. Abramowicz, On the crushing mechanics of thin-walled structures, J. Appl. Mech., vol. 50, no. 4a, pp. 727–734, 1983. DOI: 10.1115/1.3167137.
  • W. Ma, S. Xie, and Z. Li, Mechanical performance of bio-inspired corrugated tubes with varying vertex configurations, Int. J. Mech. Sci., vol. 172, pp. 105399, 2020. DOI: 10.1016/j.ijmecsci.2019.105399.
  • S. Reddy, M. Abbasi, and M. Fard, Multi-cornered thin-walled sheet metal members for enhanced crashworthiness and occupant protection, Thin-Wall. Struct., vol. 94, pp. 56–66, 2015. DOI: 10.1016/j.tws.2015.03.029.
  • W. Liu, Z. Lin, J. He, N. Wang, and X. Deng, Crushing behavior and multi-objective optimization on the crashworthiness of sandwich structure with star-shaped tube in the center, Thin-Wall. Struct., vol. 108, pp. 205–214, 2016. DOI: 10.1016/j.tws.2016.08.021.
  • J. Wang, Y. Zhang, N. He, and C. H. Wang, Crashworthiness behavior of Koch fractal structures, Mater. Des., vol. 144, pp. 229–244, 2018. DOI: 10.1016/j.matdes.2018.02.035.
  • Z. Li, W. Ma, L. Hou, P. Xu, and S. Yao, Crashworthiness analysis of corrugations reinforced multi-cell square tubes, Thin-Wall. Struct., vol. 150, pp. 106708, 2020. DOI: 10.1016/j.tws.2020.106708.
  • Z. Li, W. Ma, P. Xu, and S. Yao, Crushing behavior of circumferentially corrugated square tube with different cross inner ribs, Thin-Wall. Struct., vol. 144, pp. 106370, 2019. DOI: 10.1016/j.tws.2019.106370.
  • X. Deng and W. Liu, Experimental and numerical investigation of a novel sandwich sinusoidal lateral corrugated tubular structure under axial compression, Int. J. Mech. Sci., vol. 151, pp. 274–287, 2019. DOI: 10.1016/j.ijmecsci.2018.11.010.
  • Z. Tang, S. Liu, and Z. Zhang, Energy absorption properties of non-convex multi-corner thin-walled columns, Thin-Wall. Struct., vol. 51, pp. 112–120, 2012. DOI: 10.1016/j.tws.2011.10.005.
  • Z. Li, S. Yao, W. Ma, P. Xu, and Q. Che, Energy-absorption characteristics of a circumferentially corrugated square tube with a cosine profile, Thin-Wall. Struct., vol. 135, pp. 385–399, 2019. DOI: 10.1016/j.tws.2018.11.028.
  • H. Nikkhah, A. Baroutaji, and A. G. Olabi, Crashworthiness design and optimisation of windowed tubes under axial impact loading, Thin-Wall. Struct., vol. 142, pp. 132–148, 2019. DOI: 10.1016/j.tws.2019.04.052.
  • S. Wang, Y. Peng, T. Wang, X. Chen, L. Hou, and H. Zhang, The origami inspired optimization design to improve the crashworthiness of a multi-cell thin-walled structure for high speed train, Int. J. Mech. Sci., vol. 159, pp. 345–358, 2019. DOI: 10.1016/j.ijmecsci.2019.06.017.
  • K. Xu, et al., Crashworthiness optimisation for the rectangular tubes with axisymmetric and uniform thicknesses under offset loading, Struct. Multidisc. Optim., vol. 62, no. 2, pp. 957–977, 2020. DOI: 10.1007/s00158-020-02535-1.
  • X. Zhang and H. Zhang, Axial crushing of circular multi-cell columns, Int. J. Impact Eng., vol. 65, pp. 110–125, 2014. DOI: 10.1016/j.ijimpeng.2013.12.002.
  • Z. Li, W. Ma, P. Xu, and S. Yao, Crashworthiness of multi-cell circumferentially corrugated square tubes with cosine and triangular configurations, Int. J. Mech. Sci., vol. 165, pp. 105205, 2020. DOI: 10.1016/j.ijmecsci.2019.105205.
  • Z. Wang, Z. Li, C. Shi, and W. Zhou, Mechanical performance of vertex-based hierarchical vs square thin-walled multi-cell structure, Thin-Wall. Struct., vol. 134, pp. 102–110, 2019. DOI: 10.1016/j.tws.2018.09.017.
  • W. Ma, S. Xie, Z. Li, Z. Feng, and K. Jing, Crushing behaviors of horse-hoof-wall inspired corrugated tubes under multiple loading conditions, Mech. Adv. Mater. Struct., pp. 1–25, 2021.
  • S. Xie, W. Yang, N. Wang, and H. Li, Crashworthiness analysis of multi-cell square tubes under axial loads, Int. J. Mech. Sci., vol. 121, pp. 106–118, 2017. DOI: 10.1016/j.ijmecsci.2016.12.005.
  • J. F. Liu, W. S. Chen, H. Hao, and Z. G. Wang, Numerical study of low-speed impact response of sandwich panel with tube filled honeycomb core, Compos. Struct., vol. 220, pp. 736–748, 2019. DOI: 10.1016/j.compstruct.2019.04.023.
  • Y. Li and Z. You, Origami concave tubes for energy absorption, Int. J. Solids Struct., vol. 169, pp. 21–40, 2019. DOI: 10.1016/j.ijsolstr.2019.03.026.
  • S. C. Xie, Z. J. Feng, H. Zhou, and D. Wang, Three-point bending behavior of Nomex honeycomb sandwich panels: experiment and simulation, Mech. Adv. Mater. Struct., pp. 1–15, 2020.
  • G. X. Gu, C. T. Chen, D. J. Richmond, and M. J. Buehler, Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment, Mater. Horiz., vol. 5, no. 5, pp. 939–945, 2018. DOI: 10.1039/C8MH00653A.
  • A. Sadrmomtazi, J. Sobhani, and M. A. Mirgozar, Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS, Constr. Build. Mater., vol. 42, pp. 205–216, 2013. DOI: 10.1016/j.conbuildmat.2013.01.016.
  • M. A. Bessa, P. Glowacki, and M. Houlder, Bayesian machine learning in metamaterial design: fragile becomes supercompressible, Adv. Mater., vol. 31, no. 48, pp. e1904845, 2019. DOI: 10.1002/adma.201904845.
  • G. Lu and T. Yu, Energy Absorption of Structures and Materials, Woodhead Publishing, Cambridge, UK, 2003.
  • T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Trans. Inform. Theory., vol. 13, no. 1, pp. 21–27, 1967. DOI: 10.1109/TIT.1967.1053964.
  • L. Breiman, Random forests, Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001. DOI: 10.1023/A:1010933404324.
  • G. Ke, LightGBM: A highly efficient gradient boosting decision tree, Adv. Neural Inf. Process. Syst., vol. 30, pp. 3146–3154, 2017.
  • T. Chen and C. Guestrin, XGBoost: a scalable tree boosting system, The 22nd ACM SIGKDD International Conference, 2016.
  • B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Association for Computing Machinery, Pittsburgh, Pennsylvania, USA, pp. 144–152, 1992.
  • S. Grossberg, Classical and instrumental learning by neural networks. In: R. Rosen and F. M. Snell (eds.), Progress in Theoretical Biology, Academic Press, Heidelberg, Springer, pp. 51–141, 1974.
  • Z. Li, W. Ma, S. Yao, P. Xu, L. Hou, and G. Deng, A machine learning based optimization method towards removing undesired deformation of energy-absorbing structures, Structural and Multidisciplinary Optimization, 2021.
  • H. Hu, Enhanced gabor feature based classification using a regularized locally tensor discriminant model for multiview gait recognition, IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 7, pp. 1274–1286, 2013. DOI: 10.1109/TCSVT.2013.2242640.
  • B. Larivière and D. Van den Poel, Predicting customer retention and profitability by using random forests and regression forests techniques, Expert Syst. Appl., vol. 29, pp. 472–484, 2005.
  • C. Cortes and V. Vapnik, Support-vector networks, Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995. DOI: 10.1007/BF00994018.
  • S. R. Sain, The nature of statistical learning theory, Technometrics, vol. 38, no. 4, pp. 409–409, 1996. DOI: 10.1080/00401706.1996.10484565.
  • A. Çevik, A. E. Kurtoğlu, M. Bilgehan, M. E. Gülşan, and H. M. Albegmprli, Support vector machines in structural engineering: a review, J. Civ. Eng. Manage., vol. 21, no. 3, pp. 261–281, 2015. DOI: 10.3846/13923730.2015.1005021.
  • M. Ahmadi, H. Naderpour, and A. Kheyroddin, Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load, Arch. Civ. Mech. Eng., vol. 14, no. 3, pp. 510–517, 2014. DOI: 10.1016/j.acme.2014.01.006.
  • A. Cascardi, F. Micelli, and M. A. Aiello, An artificial neural networks model for the prediction of the compressive strength of FRP-confined concrete circular columns, Eng. Struct., vol. 140, pp. 199–208, 2017. DOI: 10.1016/j.engstruct.2017.02.047.
  • R. M. Di Benedetto, E. C. Botelho, A. Janotti, A. C. Ancelotti Junior, and G. F. Gomes, Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites, Compos. Struct., vol. 257, pp. 113131, 2021. DOI: 10.1016/j.compstruct.2020.113131.
  • K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: solving problems with box constraints, IEEE Trans. Evol. Computat., vol. 18, no. 4, pp. 577–601, 2014. DOI: 10.1109/TEVC.2013.2281535.
  • M. Behzadian, S. Khanmohammadi Otaghsara, M. Yazdani, and J. Ignatius, A state-of the-art survey of TOPSIS applications, Expert Syst. Appl., vol. 39, no. 17, pp. 13051–13069, 2012. DOI: 10.1016/j.eswa.2012.05.056.
  • F. Cavallaro, E. Zavadskas, and S. Raslanas, Evaluation of combined heat and power (CHP) systems using fuzzy Shannon entropy and fuzzy TOPSIS, Sustainability, vol. 8, no. 6, pp. 556, 2016. DOI: 10.3390/su8060556.
  • J. A. Hanley and B. J. McNeil, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology., vol. 143, no. 1, pp. 29–36, 1982. DOI: 10.1148/radiology.143.1.7063747.
  • Z. Li, W. Ma, S. Yao, and P. Xu, Crashworthiness performance of corrugation- reinforced multicell tubular structures, Int. J. Mech. Sci., vol. 190, pp. 106038, 2021. DOI: 10.1016/j.ijmecsci.2020.106038.
  • L. Hou, et al., An integrated multi-objective optimization method with application to train crashworthiness design, Struct. Multidisc. Optim., vol. 63, no. 3, pp. 1513–1532, 2021. DOI: 10.1007/s00158-020-02758-2.
  • J. Chen, P. Xu, S. Yao, J. Xing, and Z. Hu, The multi-objective structural optimisation design to improve the crashworthiness of a multi-cell structure for high-speed train, Int. J. Crashworthiness, pp. 1–10, 2020.
  • S. Wu, G. Zheng, G. Sun, Q. Liu, G. Li, and Q. Li, On design of multi-cell thin-wall structures for crashworthiness, Int. J. Impact Eng., vol. 88, pp. 102–117, 2016. DOI: 10.1016/j.ijimpeng.2015.09.003.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.