| id |
caadria2019_104 |
| authors |
Johan, Ryan, Chernyavsky, Michael, Fabbri, Alessandra, Gardner, Nicole, Haeusler, M. Hank and Zavoleas, Yannis |
| year |
2019 |
| title |
Building Intelligence Through Generative Design - Structural analysis and optimisation informed by material performance |
| source |
M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 371-380 |
| doi |
https://doi.org/10.52842/conf.caadria.2019.1.371
|
| summary |
Generative design (GD) is the process of defining high-level goals and constraints and then using computation to automatically explore a range of solutions that meet the desired requirements. Generative processes are intelligent ways to fast-track early design stages. The outcomes are analyzed simultaneously to inform decisions for architects and engineers. Whilst material properties have been defined as a driving agent within generative systems to calculate structure, material performance or structural capacity are not linked with early decision-making. In response, this paper sets a constrained approach upon traditional and non-traditional materials to validate the feasibility of structures. A GD tool is developed within Grasshopper using C-sharp, Karamaba3D, Galapagos and various engineering formulas. The result is a script, which prioritizes the structural qualities of material as a driving factor within generative systems and facilitates communication across different expertise. |
| keywords |
Intelligent systems; generative design; material properties; structural analysis; evolutionary algorithms |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (2,813,667 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:52 |
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