2025年4月22日 周二
首页本刊简介投稿须知编委会成员论文撰写模板论文格式要求过刊全文链接审稿单联系我们English
首页 > 过刊浏览>2018年第46卷第01期 >0030-0039. DOI:10.11908/j.issn.0253-374x.2018.01.005
PDF HTML阅读 XML下载 导出引用 引用提醒
基于优化算法的能量等值延性指标评价
CSTR:
[cstr]
作者:
作者单位:

1.同济大学航空航天与力学学院 2.集美大学,同济大学航空航天与力学学院

中图分类号:

P315.9

基金项目:

国家自然科学基金（No.5578066）福建省教育厅科研基金（JAT170328）（FBJG20170066）集大博士科研启动基金。


Evaluation on Energy Equivalent Ductility Index Based on Optimized Algorithm
Author:
  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [27]
    • |
  • 相似文献 [20]
    • | | |
  • 文章评论
    摘要:

    设计多层前馈BP、径向基RBF和遗传优化GABP算法训练并预测能量等值法极限承载延性指标μΔe，采用5个控制参数及PEER库与拟静力试验作为训练和预测样本.编写BP及其隐含层节点数和学习率的优化算法，编写RBF及其节点数和中心宽度的优化算法，采用GA优化BP算法权值wij和阈值θj并抑制局部最优及逼近数据规律，并实现算法预测足尺框架柱延性指标μΔe值.研究表明GABP具有最佳适用性和计算优越性，提供结构损伤评价新方法.

    Abstract:

    back propagation(BP), radial basis function(RBF) and genetic algorithmback propagation (GABP) are used to train and predict the ultimate ductility index μΔeof dualenergy equivalent method. Five control parameters and PEER database and quasistatic test are used as training and prediction samples. BP and optimization algorithm of its node numbers in hidden layer and learning rate are compiled, RBF and optimization algorithm of its node numbers and central width are compiled. Genetic algorithm (GA) is used for optimizing weight wij and threshold θj in backpropagation (BP) to inhibit the local optimum, making relationship between testing data approximate to the real mapping principle. Algorithms is realized to predict μΔe of fullscale columns. Research indicates GABP has optimal adaptability and computing superiority, provide new method for structural damage evaluation.

    参考文献
    [1] FEMA. Next-generation performance-based seismic design guidelines(FEMA445)[R]. Washington D.C.: Department of Homeland Security(DHS), 2016.
    [2] Xe K, Tzig W B, Petryna Y S. Quasistatic seismic damage indicators for RC structures from dissipating energies in tangential subspaces[J]. Mathematical Problems in Engineering. 2014, 2014: 1-11.
    [3] FEMA. Prestandard and commentary for the seismic rehabilitation of buildings[S]. Washington D.C., 2000.
    [4] Taylor A W, Kuo C, Wellenius K, et al. A summary of cyclic lateral load tests on rectangular reinforced concrete columns[R]. Gaithesburg,USA: Building and Fire Research Laboratory,National Institute of Standards and Technology, 1997.
    [5] Taylor A W, Stone W C. A summary of cyclic lateral load tests on spiral reinforced concrete columns[R]. Gaithersburg,USA: Building and Fire Research Laboratory, National Institute of Standards and Technology, 1993.
    [6] Lin H, Tang S, Lan C. Damage analysis and evaluation of high strength concrete frame based on deformation-energy damage model[J]. Mathematical Problems in Engineering. 2015, 2015(1): 1-14.
    [7] Lin H, Tang S, Lan C. Control parametric analysis on improving Park restoring force model and damage evaluation of high-strength structure[J]. Advances in Materials Science and Engineering. 2016, 2016(1): 1-11.
    [8] Hopfield J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the National Academy of Sciences(PNAS). 1988, 79(4): 2554-2558.
    [9] Haykin S O. Neural Networks and Learning Machines(Third Edition)[M]. Canada: Prentice Hall, 2015: 936.
    [10] Fredric M H, Ivica K. Principles of Neurocomputer for Science and Engineering[M]. New York: McGraw-Hill Computer Inc, 2007: 490.
    [11] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature. 1986, 323(10): 533-536.
    [12] Lecun Y. Une procedure d'apprentissage pour reseau a seuil assymetrique[J]. A la Frontiere de l'Intelligence Artificielle des Sciences de la Connaissance des Neurosciences. 1985, 85(1): 599-604.
    [13] Rumelhart D, Mcclelland J. Learning Internal Representations by Error Propagation[M]. Massachusetts: MIT Press, 2016: 318-362.
    [14] M. T. Hagan, Demuth H B, Beals M. Neural Network Design[M]. Boston: PWS Publishing Company, 1996.
    [15] Hunter A. Feature selection using probabilistic neural networks[J]. Neural Computing and Applications. 2000, 2(9): 124-132.
    [16] Saatcioglu M, Grira M. Confinement of reinforced concrete colummns with welded reinforcement grids[J]. ACI Structural Journal. 1999, 96(1): 29-39.
    [17] Matamoros A B. Study of drift limits for high-strength concrete columns[D]. Illinois: Universtiy of Illinois at Urbana-Champaign, 2000.
    [18] Légeron F, Paultre P. Behavior of high-strength concrete columns under cyclic flexure and constant axial load[J]. ACI Structural Journal. 2000, 97(4): 591-601.
    [19] Paultre P, Légeron F, Mongeau D. Influence of concrete strength and transverse reinforcement yield strength on behavior of high strength concrete columns[J]. ACI Structural Journal. 2001, 98(4): 490-501.
    [20] Powell M J D. Radial basis funcition for multivariable interpolation:A review[C]. Shrivenham UK: 1985.
    [21] Broomhead D S, Lowe D. Multivariable functional interpolation and adaptive networks[J]. Complex Systems. 1988, 3(2): 321-355.
    [22] Jackson S J, Stevens D, Giddings D, et al. An adaptive RBF finite collocation approach to track transport processes across moving fronts[J]. Computers amp; Mathematics with Applications. 2016, 71(1): 278-300.
    [23] Chen S, Billings S A, Cowan C F N, et al. Practical identification of NARMAX models using radial basis functions[J]. International Journal of Control. 1990, 52(6): 1327-1350.
    [24] Webb A R, Shannon S. Shape-adaptive radial basis functions[J]. IEEE Trans. Neural Networks. 1998, 9(6): 1155-1166.
    [25] Broomhead D S, Lowe D. Multivariable fucntional interpolation and adaptive networks[J]. Complex Systems. 1988, 2(3): 321-355.
    [26] Holland J H. Building blocks,cohort Genetic Algorithms and hyperplane-defined functions[J]. Evolutionary Computation. 2000, 4(8): 373-391.
    [27] Holland J H. Adaptation in Natural and Artificial Systems[M]. MA USA: MIT Press Cambridge, 1992.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

林煌斌,唐寿高.基于优化算法的能量等值延性指标评价[J].同济大学学报(自然科学版),2018,46(01):0030~0039

复制
分享
文章指标
  • 点击次数:1886
  • 下载次数: 1136
  • HTML阅读次数: 782
  • 引用次数: 0
历史
  • 收稿日期:2017-03-24
  • 最后修改日期:2017-11-19
  • 录用日期:2017-10-09
  • 在线发布日期: 2018-02-01
文章二维码

您是本站第 10541085 访问者

版权所有：《同济大学学报（自然科学版）》     主管单位：教育部    主办单位：同济大学

地址：上海市四平路1239号同济大学内    邮编：200092    电话：021-65982344     E-mail：zrxb@tongji.edu.cn

本系统由北京勤云科技发展有限公司设计