Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks

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  • Additional Information
    • Author-Supplied Keywords:
      Artificial neural networks
      Concrete hollow brick
      Equivalent thermal conductivity
      Genetic algorithm
      Multi-mode heat transfer
      Optimization
    • NAICS/Industry Codes:
      327999 All Other Miscellaneous Nonmetallic Mineral Product Manufacturing
      327320 Ready-Mix Concrete Manufacturing
    • Abstract:
      Abstract: A structure optimization of concrete hollow brick with four rectangle enclosures is carried out to minimize the equivalent thermal conductivity (ETC) in the constraint of variable shape and position parameters. During the optimization hybrid genetic algorithm (HGA) is developed combining with artificial neural networks (ANN). The modified Latin hypercube sampling (i.e. the maximum minimum distance criterion) is employed to make a robust decision. The ETC of the samples is computed using the finite volume method (FVM) on the basis of 3D multi-mode heat transfer simulation. It indicates that the well-trained ANN can accurately predict the ETC of the concrete hollow brick which matches very well with data obtained from the FVM simulation. The optimization obtains 21.69% improvement on the ETC for the given range of design parameters. The optimized concrete hollow brick owns the largest void volume fraction, the minimum rid and wall thickness, same width of the enclosure, and the optimum staggered arrangement with two same large enclosures and two same small enclosures, which is resulted by the multi-mode heat transfer characteristic of the concrete hollow brick. A novel method of the optimum concrete hollow is proposed to construct new concrete hollow brick with many rows of enclosures. Relative Staggered Ratio (RSR) is used to discuss the effect of the staggered form. By combining two or more rows of the optimized enclosures to one brick with the same size the efficiency to block heat transfer is evidently improved. It is concluded by the present work that the combination of ANN and HGA and the popularizing method are powerful to the optimization of the concrete hollow brick. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of International Journal of Heat & Mass Transfer is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
    • Author Affiliations:
      1State Key Laboratory for Mechanical Behavior of Materials, Xian Jiaotong University, Xian 710049, PR China
      2School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu Province, PR China
    • ISSN:
      0017-9310
    • Accession Number:
      10.1016/j.ijheatmasstransfer.2010.07.006
    • Accession Number:
      53335703
  • Citations
    • ABNT:
      SUN, J.; FANG, L.; HAN, J. Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks. International Journal of Heat & Mass Transfer, [s. l.], v. 53, n. 23/24, p. 5509–5518, 2010. DOI 10.1016/j.ijheatmasstransfer.2010.07.006. Disponível em: http://widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=a9h&AN=53335703&authtype=sso&custid=s5834912. Acesso em: 24 jan. 2020.
    • AMA:
      Sun J, Fang L, Han J. Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks. International Journal of Heat & Mass Transfer. 2010;53(23/24):5509-5518. doi:10.1016/j.ijheatmasstransfer.2010.07.006.
    • APA:
      Sun, J., Fang, L., & Han, J. (2010). Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks. International Journal of Heat & Mass Transfer, 53(23/24), 5509–5518. https://doi.org/10.1016/j.ijheatmasstransfer.2010.07.006
    • Chicago/Turabian: Author-Date:
      Sun, Jiapeng, Liang Fang, and Jing Han. 2010. “Optimization of Concrete Hollow Brick Using Hybrid Genetic Algorithm Combining with Artificial Neural Networks.” International Journal of Heat & Mass Transfer 53 (23/24): 5509–18. doi:10.1016/j.ijheatmasstransfer.2010.07.006.
    • Harvard:
      Sun, J., Fang, L. and Han, J. (2010) ‘Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks’, International Journal of Heat & Mass Transfer, 53(23/24), pp. 5509–5518. doi: 10.1016/j.ijheatmasstransfer.2010.07.006.
    • Harvard: Australian:
      Sun, J, Fang, L & Han, J 2010, ‘Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks’, International Journal of Heat & Mass Transfer, vol. 53, no. 23/24, pp. 5509–5518, viewed 24 January 2020, .
    • MLA:
      Sun, Jiapeng, et al. “Optimization of Concrete Hollow Brick Using Hybrid Genetic Algorithm Combining with Artificial Neural Networks.” International Journal of Heat & Mass Transfer, vol. 53, no. 23/24, Nov. 2010, pp. 5509–5518. EBSCOhost, doi:10.1016/j.ijheatmasstransfer.2010.07.006.
    • Chicago/Turabian: Humanities:
      Sun, Jiapeng, Liang Fang, and Jing Han. “Optimization of Concrete Hollow Brick Using Hybrid Genetic Algorithm Combining with Artificial Neural Networks.” International Journal of Heat & Mass Transfer 53, no. 23/24 (November 2010): 5509–18. doi:10.1016/j.ijheatmasstransfer.2010.07.006.
    • Vancouver/ICMJE:
      Sun J, Fang L, Han J. Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks. International Journal of Heat & Mass Transfer [Internet]. 2010 Nov [cited 2020 Jan 24];53(23/24):5509–18. Available from: http://widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=a9h&AN=53335703&authtype=sso&custid=s5834912