| id |
ecaade2024_180 |
| authors |
Licen, Jurij; Chen, Taole |
| year |
2024 |
| title |
Use of Genetic Optimisation Algorithms in the Design of 3D Concrete Printed Shell Structures |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.213
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 213–222 |
| summary |
A transition from disparate data to interconnected and contextually integrated data is currently causing a paradigm shift in the architecture industry. The need for fabrication-aware architectural representation models, that enable designers to interface with today's data-intensive manufacturing technologies, is a direct consequence of new concepts such as smart fabrication, automation and vertical integration. Compared to conventional concrete casting methods, 3D Concrete Printing (3DCP) offers a wide range of advantages, particularly the ability to create complex geometry. A lack of computational modelling techniques that link design and production for 3DCP is currently making it difficult to predict the printability of designs. This research presents a unified design-to-fabrication framework using machine learning (ML) that is customized for freeform steel-reinforced 3DCP shell structures. 3DCP is used to create incrementally cast sacrificial formwork. In particular, the segmentation process is fed back into the design process using genetic optimization for a fabrication-aware design model. The framework is validated with a series of physical experiments. |
| keywords |
Additive Manufacturing, 3D Concrete Printing, Architectural Design, Integrated Workflow, Fabrication-Aware Modelling, Conceptual Design, Concrete Shells |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,551,718 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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