Structural Damage Identification Based on Rough Sets and Artificial Neural Network

被引：1
|
作者：
Liu, Chengyin [1 ,2 ]
Wu, Xiang [3 ]
Wu, Ning [1 ]
Liu, Chunyu [1 ]
机构：
[1] Harbin Inst Technol, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
[2] Southeast Univ, Key Lab C&PC Struct, Nanjing 211189, Jiangsu, Peoples R China
[3] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150001, Peoples R China
来源：
SCIENTIFIC WORLD JOURNAL | 2014年
关键词：
D O I：
10.1155/2014/193284
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.
引用
收藏
页数：9
相关论文
共 50 条
  • [31] Structural Damage Identification Based on Convolutional Neural Network Group Considering the Sensor Fault
    Yongpeng Luo
    Linkun Wang
    Xu Guo
    Jinlin Zheng
    Feiyu Liao
    Zixiong Guo
    [J]. KSCE Journal of Civil Engineering, 2023, 27 : 3403 - 3417
  • [32] Study on the structural damage identification method with combined parameters based on RBF neural network
    Yang, Y
    Cheng, JS
    Ding, G
    Tian, D
    [J]. 2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 3216 - 3218
  • [33] Application of convolutional neural network in random structural damage identification
    Zhan, Yulin
    Lu, Siji
    Xiang, Tianyu
    Wei, Tao
    [J]. STRUCTURES, 2021, 29 : 570 - 576
  • [34] Identification of Structural Damage in a Vehicular Bridge using Artificial Neural Networks
    Gonzalez-Perez, C.
    Valdes-Gonzalez, J.
    [J]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2011, 10 (01): : 33 - 48
  • [35] Damage detection of structural based on indirect vibration measurement results combined with Artificial Neural Network
    Nhung, Nguyen Thi Cam
    Minh, Tran Quang
    Sousa, Helder S.
    Thu'c, Ngo Van
    Matos, Jose C.
    [J]. JOURNAL OF MATERIALS AND ENGINEERING STRUCTURES, 2022, 9 (04): : 403 - 410
  • [36] Data mining based damage identification using imperialist competitive algorithm and artificial neural network
    Gordan, Meisam
    Razak, Hashim Abdul
    Ismail, Zubaidah
    Ghaedi, Khaled
    [J]. LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES, 2018, 15 (08):
  • [37] Reinforced concrete structural damage diagnosis by using artificial neural network
    Hsu, DS
    Tsai, CH
    [J]. INTELLIGENT INFORMATION SYSTEMS, (IIS'97) PROCEEDINGS, 1997, : 149 - 155
  • [38] Damage identification in plate using wavelet transform and artificial neural network
    Abdulkareem, Muyideen
    Ganiyu, Abideen
    Abd Majid, Muhd Z.
    [J]. 10TH ASIA PACIFIC STRUCTURAL ENGINEERING AND CONSTRUCTION CONFERENCE 2018, 2019, 513
  • [39] Identification of damage degree of concrete by acoustic emission and artificial neural network
    Wang, Yan
    Zhang, Youtao
    Hu, Hongxiang
    Liu, Shaojun
    Yuan, Liang
    [J]. Jianzhu Cailiao Xuebao/Journal of Building Materials, 2014, 17 (04): : 672 - 676
  • [40] A novel RBF neural network for radar emitter recognition based on Rough Sets
    Wu, Zhilu
    Yang, Zhutian
    Yin, Zhendong
    Zuo, Lihua
    Gao, Hansong
    [J]. JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, 2012, 35 (07) : 901 - 907
12345