Crack damage identification of Reinforced concrete Simply supported beam based on BP Neural Network

被引：1
|
作者：
Yang, Xiaoming [1 ]
Li, Fu [1 ]
机构：
[1] Inner Mongolia Univ Technol, Civil Engn Coll, Hohhot 010051, Peoples R China
来源：
AUTOMATION EQUIPMENT AND SYSTEMS, PTS 1-4 | 2012年 / 468-471卷
关键词：
BP neural network; reinforced concrete simply supported beam; natural frequency; damage identification;
D O I：
10.4028/www.scientific.net/AMR.468-471.738
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Considering the good forecasting capability of BP neural network, a new crack damage identification method for reinforced concrete simply supported beam is proposed in this paper. After simulating the crack damage of a reinforced concrete simply supported beam, the natural frequency of the beam is chosen as the input parameters of the BP neural network. The data before and after damage of the simply supported beam are put into the trained neural network to judge the structural damage. The results demonstrate that the approach has a better application prospects in structural damage identification.
引用
收藏
页码：738 / 741
页数：4
相关论文
共 50 条
  • [41] Damage Identification of Reinforced Concrete Beam Using Modal Curvature Approach
    Chaurasia, A.
    Panigrahi, S. K.
    Patel, S. S.
    [J]. JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, 2018, 77 (06): : 337 - 341
  • [42] EARTHQUAKE ANALYSIS OF SIMPLY SUPPORTED REINFORCED-CONCRETE SLABS
    AYVAZ, Y
    DURMUS, A
    [J]. JOURNAL OF SOUND AND VIBRATION, 1995, 187 (03) : 531 - 539
  • [43] Damage identification based on convolutional neural network and recurrence graph for beam bridge
    He, Hao-xiang
    Zheng, Jia-cheng
    Liao, Li-can
    Chen, Yan-jiang
    [J]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2021, 20 (04): : 1392 - 1408
  • [44] Structural Behavior of Simply Supported Hemp-Reinforced Concrete
    Awwad, Elie
    Hamad, Bilal
    Mabsout, Mounir
    Khatib, Helmi
    [J]. ACI STRUCTURAL JOURNAL, 2014, 111 (06) : 1307 - 1316
  • [45] A probabilistic approach for damage identification and crack mode classification in reinforced concrete structures
    Farhidzadeh, Alireza
    Salamone, Salvatore
    Singla, Puneet
    [J]. JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, 2013, 24 (14) : 1722 - 1735
  • [46] Dynamic nonlinearities for identification of the breathing crack type damage in reinforced concrete bridges
    Voggu, Srinivas
    Sasmal, Saptarshi
    [J]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2021, 20 (01): : 339 - 359
  • [47] AUTOMATIC PAVEMENT CRACK RECOGNITION BASED ON BP NEURAL NETWORK
    Li, Li
    Sun, Lijun
    Ning, Guobao
    Tan, Shengguang
    [J]. PROMET-TRAFFIC & TRANSPORTATION, 2014, 26 (01): : 11 - 22
  • [48] Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network
    Xu, Yang
    Wei, Shiyin
    Bao, Yuequan
    Li, Hui
    [J]. STRUCTURAL CONTROL & HEALTH MONITORING, 2019, 26 (03):
  • [49] Based on BP Neural Network: Prediction of Interface Bond Strength between CFRP Layers and Reinforced Concrete
    Al-Bukhaiti, Khalil
    Liu, Yanhui
    Zhao, Shichun
    Han, Daguang
    [J]. PRACTICE PERIODICAL ON STRUCTURAL DESIGN AND CONSTRUCTION, 2024, 29 (02)
  • [50] A truss analogy and neural network for reinforced concrete deep beam analysis
    Jenkins, W.M.
    [J]. 2002, ICE Publishing Ltd (152)
12345