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Automated Reinforcement during Large-Scale Additive Manufacturing: Structural Assessment of a Dual Approach

Author(s):
ORCID
ORCID

ORCID

ORCID
Medium: journal article
Language(s): English
Published in: Buildings, , n. 4, v. 14
Page(s): 1167
DOI: 10.3390/buildings14041167
Abstract:

Automated and seamless integration of reinforcement is one of the main unresolved challenges in large-scale additive construction. This study leverages a dual-reinforcement solution consisting of high-dosage steel fiber (up to 2.5% by volume) and short vertical reinforcements as a complementary reinforcement technique for 3D-printed elements. The mechanical performance of the printing material was characterized by measuring the compressive, flexural, and uniaxial tensile strengths of mold-cast specimens. Furthermore, the flexural performance of the plain and fiber-reinforced 3D-printed beams was evaluated in the three main loading directions (X, Y, and Z-directions in-plane). In addition, short vertical threaded reinforcements were inserted into the fiber-reinforced 3D-printed beams tested in the Z-direction. The experimental results revealed the superior flexural performance of the fiber-reinforced beams loaded in the longitudinal directions (X and Y). Moreover, the threaded reinforcement significantly increases the flexural strength and ductility of beams loaded along the interface, compared to the control. Overall, the proposed dual-reinforcement approach, which exhibited notably less porosity compared to the mold-cast counterpart, holds great potential as a reinforcement solution for 3D-printed structures without the need for manual operations.

Copyright: © 2024 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
    10773674
  • Published on:
    29/04/2024
  • Last updated on:
    05/06/2024
 
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