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Buildability of Complex 3D Printed Concrete Geometries Using Peridynamics (2024-06)

10.1016/j.autcon.2024.105575

Zhu Jinggao,  Cervera Miguel,  Ren Xiaodan
Journal Article - Automation in Construction, Vol. 165, No. 105575

Abstract

As a formwork-free construction method, 3D–printed concrete (3DPC) shows great advantages in forming complex structures; but encounters buildability problems. To address this, this paper presents a fluid-solid integrated peridynamic (PD) model to describe the fluid-to-solid transition of 3DPC. On this basis, an automatic strategy is proposed to deal with complex geometries. Programming by Python, PD particles are automatically generated by the input geometric model and arranged in a layer-wise manner for 3D printing simulation. The particles are then connected into inter- and intra-layer bonds to model the weak interface between layers. This establishes a connection between digital fabrication and PD simulation and offers a general way for 3DPC buildability analysis, which is assessed by the modeling of a 3D printing test and two digitally designed structures. The satisfactory results prove that the proposed method is applicable to structures with different geometries, thus enhancing the advantages of 3D printing.

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BibTeX
@article{zhu_cerv_ren.2024.BoC3PCGUP,
  author            = "Jinggao Zhu and Miguel Cervera and Xiaodan Ren",
  title             = "Buildability of Complex 3D Printed Concrete Geometries Using Peridynamics",
  doi               = "10.1016/j.autcon.2024.105575",
  year              = "2024",
  journal           = "Automation in Construction",
  volume            = "165",
  pages             = "105575",
}
Formatted Citation

J. Zhu, M. Cervera and X. Ren, “Buildability of Complex 3D Printed Concrete Geometries Using Peridynamics”, Automation in Construction, vol. 165, p. 105575, 2024, doi: 10.1016/j.autcon.2024.105575.

Zhu, Jinggao, Miguel Cervera, and Xiaodan Ren. “Buildability of Complex 3D Printed Concrete Geometries Using Peridynamics”. Automation in Construction 165 (2024): 105575. https://doi.org/10.1016/j.autcon.2024.105575.