Assessment of concrete compressive strength after fire based on evolutionary neural network

被引：0
|
作者：
Zhao Wangda [1 ]
Liu Yongqiu [1 ]
Wang Yang [1 ]
机构：
[1] Cent S Univ, Coll Civil & Architectural Engn, Changsha 410075, Hunan, Peoples R China
来源：
PROGRESS IN SAFETY SCIENCE AND TECHNOLOGY, VOL 6, PTS A AND B | 2006年 / 6卷
关键词：
fire; ultrasonic and rebound combined method; radial basis function neural network; genetic algorithm; concrete compressive strength;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Assessment of concrete compressive strength is one of the most essential tasks in the damage degree and bearing capacity diagnosis and identification of concrete structure damaged by fire. An evolutionary algorithm radial basis function neural network model (EARBFNN) optimized was introduced to assessing concrete compressive strength, and an ultrasonic and rebound combined method is adopted to collect original experiment data for concrete component after fire. At last, a regressive calculation is applied to comparing the assessment effect with the EARBFNN method, and the experimental test and simulation analysis result has proven that EARBFNN has higher precision than that of regressive calculation.
引用
收藏
页码：979 / 983
页数：5
相关论文
共 50 条
  • [41] Recurrent neural network-based prediction of compressive and flexural strength of steel slag mixed concrete
    Gupta, Tanvi
    Sachdeva, S.N.
    Neural Computing and Applications, 2021, 33 (12) : 6951 - 6963
  • [42] Recurrent neural network-based prediction of compressive and flexural strength of steel slag mixed concrete
    Gupta, Tanvi
    Sachdeva, S. N.
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (12): : 6951 - 6963
  • [43] Recurrent neural network-based prediction of compressive and flexural strength of steel slag mixed concrete
    Tanvi Gupta
    S. N. Sachdeva
    Neural Computing and Applications, 2021, 33 : 6951 - 6963
  • [44] Application of artificial neural network in the prediction of compressive strength of long standing concrete after exposure to high temperature
    Lu, Tian-Qi
    Zhao, Guo-Fan
    Lin, Zhi-Shen
    Gongcheng Lixue/Engineering Mechanics, 2003, 20 (06): : 52 - 57
  • [45] Prediction of compressive strength of concrete by neural networks
    Ni, HG
    Wang, JZ
    CEMENT AND CONCRETE RESEARCH, 2000, 30 (08) : 1245 - 1250
  • [46] The effect of the speed and the method of cooling down on the residual compressive strength of concrete after fire
    Sandor, Fehervari
    Nehme, Salem Georges
    EPITOANYAG-JOURNAL OF SILICATE BASED AND COMPOSITE MATERIALS, 2009, 61 (04): : 118 - 123
  • [47] Neural networks analysis of compressive strength of lightweight concrete after high temperatures
    Bingol, A. Ferhat
    Tortum, Ahmet
    Gul, Rustem
    MATERIALS & DESIGN, 2013, 52 : 258 - 264
  • [48] Prediction of concrete strength based on BP neural network
    Jiang Jianping
    MATERIAL AND MANUFACTURING TECHNOLOGY II, PTS 1 AND 2, 2012, 341-342 : 58 - 62
  • [49] Ultrasonic Assessment of the Concrete Residual Strength after a Real Fire Exposure
    Wroblewski, Roman
    Stawiski, Bohdan
    BUILDINGS, 2020, 10 (09) : 1 - 13
  • [50] Optimized artificial neural network model for accurate prediction of compressive strength of normal and high strength concrete
    Khan, Arslan Qayyum
    Awan, Hasnain Ahmad
    Rasul, Mehboob
    Siddiqi, Zahid Ahmad
    Pimanmas, Amorn
    CLEANER MATERIALS, 2023, 10
12345