Ultimate strength prediction of I-core sandwich plate based on BP neural network

WEI Yuwen; ZHONG Qiang; WANG Deyu

doi:10.19693/j.issn.1673-3185.02335

Volume 17 Issue 2

Apr. 2022

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Article Navigation > Chinese Journal of Ship Research > 2022 > 17(2): 125-134

WEI Y W, ZHONG Q, WANG D Y. Ultimate strength prediction of I-core sandwich plate based on BP neural network[J]. Chinese Journal of Ship Research, 2022, 17(2): 125–134 doi: 10.19693/j.issn.1673-3185.02335

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Ultimate strength prediction of I-core sandwich plate based on BP neural network

doi: 10.19693/j.issn.1673-3185.02335

State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received Date: 2021-03-30
Rev Recd Date: 2021-05-25

Available Online: 2022-04-06

Publish Date: 2022-04-20

Abstract

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. Methods First, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. Results The mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. Conclusions This study can provide references for the application of I-core sandwich panels in hull structures.
- I-core sandwich panels,
- BP neural network,
- ultimate strength,
- nonlinear finite element method,
- prediction

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References(18)

References

[1]	陈杨科, 何书韬, 刘均, 等. 金属夹层结构的舰船应用研究综述[J]. 中国舰船研究, 2013, 8(6): 6–13. CHEN Y K, HE S T, LIU J, et al. Application and prospect of steel sandwich panels in warships[J]. Chinese Journal of Ship Research, 2013, 8(6): 6–13 (in Chinese).
[2]	LI Z, GOBBI S L. Laser welding for lightweight structures[J]. Journal of Materials Processing Technology, 1997, 70(1): 137–144.
[3]	NOURY P, HAYMAN B, MCGEORGE D, et al. Lightweight construction for advanced shipbuilding-recent development[R]. [S. l. ]: Det Norske Veritas, 2002.
[4]	李政杰, 黄路, 赵南, 等. 单轴压缩下金属夹层板极限承载性能分析[J]. 中国舰船研究, 2020, 15(4): 53–58. LI Z J, HUANG L, ZHAO N, et al. Ultimate bearing capacity for steel sandwich panels under uniaxial compression[J]. Chinese Journal of Ship Research, 2020, 15(4): 53–58 (in Chinese).
[5]	洪婷婷, 田阿利, 潘康华. 组合压载下金属折叠式夹层板的后屈曲极限强度分析[J]. 舰船科学技术, 2018, 40(9): 43–47. doi: 10.3404/j.issn.1672-7649.2018.09.008 HONG T T, TIAN A L, PAN K H. Post-buckling strength analysis of corrugated core sandwich panel under combined compression loading[J]. Ship Science and Technology, 2018, 40(9): 43–47 (in Chinese). doi: 10.3404/j.issn.1672-7649.2018.09.008
[6]	王果, 胡宗文, 王自力, 等. 夹层板面内连接结构力学性能数值仿真分析[J]. 舰船科学技术, 2014, 36(6): 54–59. doi: 10.3404/j.issn.1672-7649.2014.06.010 WANG G, HU Z W, WANG Z L, et al. Numerical simulation technology for mechanical property analysis of sandwich panel connections in plane[J]. Ship Science and Technology, 2014, 36(6): 54–59 (in Chinese). doi: 10.3404/j.issn.1672-7649.2014.06.010
[7]	KOZAK J. Problems of strength modelling of steel sandwich panels under in-plane load[J]. Polish Maritime Research, 2006(Supp 1): 9–12.
[8]	朱扬, 程远胜, 刘均. 激光焊接夹层甲板板格强度计算的子模型方法[J]. 船舶力学, 2014, 18(10): 1228–1236. doi: 10.3969/j.issn.1007-7294.2014.10.009 ZHU Y, CHENG Y S, LIU J. Sub-model method for strength calculation of a laser-welded steel sandwich panel structure[J]. Journal of Ship Mechanics, 2014, 18(10): 1228–1236 (in Chinese). doi: 10.3969/j.issn.1007-7294.2014.10.009
[9]	MESBAHI E, PU Y C. Application of ANN-based response surface method to prediction of ultimate strength of stiffened panels[J]. Journal of Structural Engineering, 2008, 134(10): 1649–1656. doi: 10.1061/(ASCE)0733-9445(2008)134:10(1649)
[10]	王仁华, 赵沙沙. 随机点蚀损伤钢板的极限强度预测[J]. 工程力学, 2018, 35(12): 248–256. WANG R H, ZHAO S S. Ultimate strength prediction of steel plate with random pitting corrosion damage[J]. Engineering Mechanics, 2018, 35(12): 248–256 (in Chinese).
[11]	AHMADI F, RANJI A R, NOWRUZI H. Ultimate strength prediction of corroded plates with center-longitudinal crack using FEM and ANN[J]. Ocean Engineering, 2020, 206: 107281. doi: 10.1016/j.oceaneng.2020.107281
[12]	TOHIDI S, SHARIFI Y. A new predictive model for restrained distortional buckling strength of half-through bridge girders using artificial neural network[J]. KSCE Journal of Civil Engineering, 2016, 20(4): 1392–1403. doi: 10.1007/s12205-015-0176-8
[13]	KOZAK J. Fatigue tests of steel sandwich panel[M]. England: Marine and Maritime, 2003: 59-68.
[14]	METSCHKOW B. Sandwich panels in shipbuilding[J]. Polish Maritime Research, 2006(Supp 1): 5–8.
[15]	BORONSKI D, KOZAK J. Research on deformations of laser-welded joint of a steel sandwich structure model[J]. Polish Maritime Research, 2004, 11(2): 3–8.
[16]	高处, 刘文夫, 邱伟强, 等. I型夹芯金属夹层板振动特性数值仿真分析[J]. 噪声与振动控制, 2018, 38(4): 76–80, 179. doi: 10.3969/j.issn.1006-1355.2018.04.015 GAO C, LIU W F, QIU W Q, et al. Numerical vibration analysis of steel sandwich plates with I-shaped cores[J]. Noise and Vibration Control, 2018, 38(4): 76–80, 179 (in Chinese). doi: 10.3969/j.issn.1006-1355.2018.04.015
[17]	PAIK J K, KIM B J, SEO J W. Methods for ultimate limit state assessment of ships and ship-shaped offshore structures: Part II stiffened panels[J]. Ocean Engineering, 2008, 35(2): 271–280. doi: 10.1016/j.oceaneng.2007.08.007
[18]	GARSON G D. Interpreting neural-network connection weights[J]. AI Expet, 1991, 6(4): 46–51.