Model Updating of A Steel Truss Based on Artificial Neural Networks

被引：3
|
作者：
Zhang, Shilei [5 ,1 ]
Chen, Shaofeng [2 ]
Wang, Huanding [1 ]
Wang, Wei [1 ]
机构：
[1] Harbin Inst Technol, Sch Civil Engn, Harbin 150090, Peoples R China
[2] Harbin Inst Technol, Sch Transportat Sci & Engn, Harbin 150090, Peoples R China
来源：
FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6 | 2012年 / 121-126卷
关键词：
model updating; neural network; back propagation; sample; truss; DERIVATIVES; MATRIX;
D O I：
10.4028/www.scientific.net/AMM.121-126.1363
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Based on the artificial neural network, the parameters of a steel truss are identified. And the finite element model of truss is corrected. In order to improve the efficiency of model updating by artificial neural networks, the momentum is introduced into the back propagation algorithm. Based on the theory of probability and mathematical statistics, the expectation confidence interval of the measured deflections and strains is obtained. In this way, the samples to train the neural network are optimized. The numerical results show that the back propagation neural network proposed on this paper is able to correct the finite element model of the truss effectively.
引用
收藏
页码：1363 / +
页数：2
相关论文
共 50 条
  • [41] A constitutive artificial neural networks-based mechanical model of the pneumatic artificial muscles
    Wang, Shuopeng
    Wang, Rixin
    Ma, Binwu
    Zhang, Ying
    Hao, Lina
    PHYSICA SCRIPTA, 2025, 100 (02)
  • [42] Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks
    Mardanshahi, Ali
    Mardanshahi, Masoud
    Izadi, Ahmad
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2020, 234 (21) : 4288 - 4304
  • [43] Artificial neural networks for modelling of the impact toughness of steel
    Dunne, D
    Tsuei, H
    Sterjovski, Z
    ISIJ INTERNATIONAL, 2004, 44 (09) : 1599 - 1607
  • [44] Artificial neural networks for the presetting of a steel temper mill
    Pican, N
    Alexandre, F
    Bresson, P
    IEEE EXPERT-INTELLIGENT SYSTEMS & THEIR APPLICATIONS, 1996, 11 (01): : 22 - 27
  • [45] Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks
    Hasancebi, Oguzhan
    Dumlupinar, Taha
    COMPUTERS & STRUCTURES, 2013, 119 : 1 - 11
  • [46] A Comparison of Neural Networks and Model Updating Methods for Damage Localization
    Garcia-Gonzalez, A.
    Gonzalez-Herrera, A.
    Velasco, J. F.
    Garcia-Cerezo, A.
    Diaz Santiago, J. M.
    PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY, 2010, 93
  • [47] Selection of training samples for model updating using neural networks
    Chang, CC
    Chang, TYP
    Xu, YG
    To, WM
    JOURNAL OF SOUND AND VIBRATION, 2002, 249 (05) : 867 - 883
  • [48] Dynamic finite element model updating using neural networks
    Univ of Bristol, Bristol, United Kingdom
    J Sound Vib, 5 (593-607):
  • [49] Dynamic finite element model updating using neural networks
    Levin, RI
    Lieven, NAJ
    JOURNAL OF SOUND AND VIBRATION, 1998, 210 (05) : 593 - 607
  • [50] Road traffic noise prediction model based on artificial neural networks
    Acosta, Oscar
    Montenegro, Carlos
    Crespo, Ruben Gonzalez
    HELIYON, 2024, 10 (17)
12345