Tailoring 3D printed concrete through explainable artificial intelligence-Reference-Cited by-同舟云学术

Tailoring 3D printed concrete through explainable artificial intelligence

Published:2023-10 Issue: Volume:56 Page:104850
ISSN:2352-0124
Container-title:Structures
language:en
Short-container-title:Structures

Author:

Ghasemi Alireza,Naser M.Z.^ORCID

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference126 articles.

1. A review of 3D printed concrete: Performance requirements, testing measurements and mix design;Hou;Constr Build Mater,2021

2. Mechanical properties of layered geopolymer structures applicable in concrete 3D-printing;Al-Qutaifi;Constr Build Mater,2018

3. Kreiger, Megan A., Bruce A. MacAllister, Juliana M. Wilhoit, Michael P. Case, The current state of 3D printing for use in construction, In The Proceedings of the 2015 Conference on Autonomous and Robotic Construction of Infrastructure. (2015) 149–158.

4. https://www.3printr.com/3d-concrete-printing-market-reach-56-4-million-2021- 123966/, (n.d.).

5. Additive manufacturing of concrete in construction: potentials and challenges of 3D concrete printing;Bos;Virtual Phys Prototyp,2016

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