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Finite Element Modelling to Predict the Fire Performance of Bio-Inspired 3D-Printed Concrete Wall Panels Exposed to Realistic Fire

Author(s):


ORCID

ORCID

Medium: journal article
Language(s): English
Published in: Buildings, , n. 2, v. 12
Page(s): 111
DOI: 10.3390/buildings12020111
Abstract:

Large-scale additive manufacturing (AM), also known as 3D concrete printing, is becoming well-recognized and, therefore, has gained intensive research attention. However, this technology requires appropriate specifications and standard guidelines. Furthermore, the performance of printable concrete in elevated temperature circumstances has not yet been explored extensively. Hence, the authors believe that there is a demand for a set of standardized findings obtained with the support of experiments and numerical modelling of the fire performance of 3D-printed concrete structural elements. In general, fire experiments and simulations focus on ISO 834 standard fire. However, this may not simulate the real fire behaviour of 3D-printed concrete walls. With the aim of bridging this knowledge disparity, this article presents an analysis of the fire performance of 3D-printed concrete walls with biomimetic hollow cross sections exposed to realistic individual fire circumstances. The fire performance of the non-load-bearing 3D-printed concrete wall was identified by developing a suitable numerical heat transfer model. The legitimacy of the developed numerical model was proved by comparing the time–temperature changes with existing results derived from fire experiments on 3D-printed concrete walls. A parametric study of 96 numerical models was consequently performed and included different 3D-printed concrete wall configurations under four fire curves (standard, prolonged, rapid, and hydrocarbon fire). Moreover, 3D-printed concrete walls and mineral wool cavity infilled wall panels showed enhanced fire performance. Moreover, the cellular structures demonstrated superior insulation fire ratings compared to the other configurations.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10657731
  • Published on:
    17/02/2022
  • Last updated on:
    01/06/2022
 
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