Prediction of fracture parameters of concrete by Artificial Neural Networks

被引:141
|
作者
Ince, R [1 ]
机构
[1] Firat Univ, Fac Engn, Dept Civil Engn, Elazig, Turkey
关键词
concrete; fracture mechanics; two-parameter model; artificial intelligence; artificial neural networks;
D O I
10.1016/j.engfracmech.2003.12.004
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
Modelling of material behaviour generally involves the development of a mathematical model derived from observations and experimental data. An alternative way discussed in this paper is Artificial Neural Network (ANN)-based modelling which is a subfield of artificial intelligence. The main benefit in using an ANN approach is that the network is built directly from experimental data using the self-organising capabilities of the ANN. In this paper the Two-Parameter Model (TPM) in the fracture of cementitious materials is modelled with a back-propagation ANN. The results of an ANN-based TPM look viable and very promising. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2143 / 2159
页数:17
相关论文
共 50 条
  • [41] Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks
    Trtnik, Gregor
    Kavcic, Franci
    Turk, Goran
    [J]. ULTRASONICS, 2009, 49 (01) : 53 - 60
  • [42] Prediction of elastic modulus of normal and high strength concrete by artificial neural networks
    Demir, Fuat
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2008, 22 (07) : 1428 - 1435
  • [43] Prediction of self-compacting concrete strength using artificial neural networks
    Asteris, P. G.
    Kolovos, K. G.
    Douvika, M. G.
    Roinos, K.
    [J]. EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING, 2016, 20 : s102 - s122
  • [44] Prediction of compression strength of high performance concrete using artificial neural networks
    Torre, A.
    Garcia, F.
    Moromi, I.
    Espinoza, P.
    Acuna, L.
    [J]. VII INTERNATIONAL CONGRESS OF ENGINEERING PHYSICS, 2015, 582
  • [45] Prediction of fracture frequency from wireline data with aid of artificial neural networks
    [J]. 2000, Institution of Mining and Metallurgy (109):
  • [46] Prediction of fracture frequency from wireline data with the aid of artificial neural networks
    Rogers, SF
    [J]. TRANSACTIONS OF THE INSTITUTION OF MINING AND METALLURGY SECTION B-APPLIED EARTH SCIENCE, 2000, 109 : B190 - B195
  • [47] Concrete strength prediction with neural networks
    Bai, J.
    Wild, S.
    Sabir, B. B.
    Morris, C. W.
    Angel, P.
    [J]. Proceedings of The Seventh International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, 2003, : 151 - 152
  • [48] Prediction of pharmacokinetic parameters and the assessment of their variability in bioequivalence studies by artificial neural networks
    Opara, J
    Primozic, S
    Cvelbar, P
    [J]. PHARMACEUTICAL RESEARCH, 1999, 16 (06) : 944 - 948
  • [49] Preliminary results of using artificial neural networks for prediction CK planning parameters
    Skrobala, A.
    Ginter, J.
    Pawalowski, B.
    Skowron, M.
    Adamczyk, M.
    Jodda, A.
    Litoborska, J.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2019, 133 : S987 - S987
  • [50] Prediction of Powder Injection Molding Process Parameters Using Artificial Neural Networks
    Rajabi, Javad
    Muhamad, Norhamidi
    Rajabi, Maryam
    Rajabi, Jamal
    [J]. JURNAL TEKNOLOGI, 2012, 59