Damage-Rheology Model for Predicting 3D Printed Concrete Buildability (2023-08)¶

10.1016/j.autcon.2023.105037

Wang Qing, Ren Xiaodan, Li Jie
Journal Article - Automation in Construction, Vol. 155, No. 105037

Abstract

Insufficient buildability during printing can result in structural instability of three-dimensional (3D) printed concrete. A damage-rheology model was developed to predict such structural failure by simulating the early-age behavior of 3D printed concrete. The model captures essential characteristics of early-age concrete, including structural build-up, softening damage, irreversible deformation, and creep effect. Based on continuum damage mechanics, the model integrates the damage-plasticity theory and the Burgers model. A time-dependent thixotropic model is used to characterize the structural build-up behavior, considering the evolution of material properties as functions of the structural parameter. The model is systematically validated by comparing its predictions with uniaxial creep, straight-wall printing, and hollow-cylinder printing tests. The study investigates the effect of early-age creep on structural responses. Numerical results demonstrate the ability of the model to accurately predict the buildability of early-age 3D printed concrete.

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22 References

11 Citations

BibTeX

@article{wang_ren_li.2023.DRMfP3PCB,
  author            = "Qing Wang and Xiaodan Ren and Jie Li",
  title             = "Damage-Rheology Model for Predicting 3D Printed Concrete Buildability",
  doi               = "10.1016/j.autcon.2023.105037",
  year              = "2023",
  journal           = "Automation in Construction",
  volume            = "155",
  pages             = "105037",
}

Formatted Citation

IEEE

Q. Wang, X. Ren and J. Li, “Damage-Rheology Model for Predicting 3D Printed Concrete Buildability”, Automation in Construction, vol. 155, p. 105037, 2023, doi: 10.1016/j.autcon.2023.105037.

CMOS

Wang, Qing, Xiaodan Ren, and Jie Li. “Damage-Rheology Model for Predicting 3D Printed Concrete Buildability”. Automation in Construction 155 (2023): 105037. https://doi.org/10.1016/j.autcon.2023.105037.