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Towards machine learned generative design
ID Gradišar, Luka (Author), ID Dolenc, Matevž (Author), ID Klinc, Robert (Author)

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Abstract
Machine learned generative design is an extension of the generative design process, addressing its inherent limitations, particularly those of interoperability. The proposed approach uses machine learning-based surrogate models, trained on computational model data, to replicate design evaluations and integrate them into a common environment. In this way, design alternatives can be generated and tested that satisfy all design requirements and considerations. The effectiveness of this approach is demonstrated by the design and optimisation of the enclosure structure for the New Robotic Telescope. Its complexity is characterised by multiple operating states that the enclosure can assume, in particular the closed state and the opening/closing state, each of which has a different structural behaviour. Using our approach, the results from each state were replicated with machine learning models and combined into a single evaluation model. This resulted in finding multiple solutions that outperformed the benchmark design. The results demonstrate not only the success of our method over conventional strategies, but also highlight its potential to redefine future design optimisation processes.

Language:English
Keywords:computational design, generative design, machine learning, optimization, surrogate modelling
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FGG - Faculty of Civil and Geodetic Engineering
Publication status:Published
Publication version:Version of Record
Year:2024
Number of pages:16 str.
Numbering:Vol. 159, art. 105284
PID:20.500.12556/RUL-156200 This link opens in a new window
UDC:004:624
ISSN on article:0926-5805
DOI:10.1016/j.autcon.2024.105284 This link opens in a new window
COBISS.SI-ID:180736259 This link opens in a new window
Publication date in RUL:14.05.2024
Views:200
Downloads:44
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Record is a part of a journal

Title:Automation in construction
Shortened title:Autom. constr.
Publisher:Elsevier
ISSN:0926-5805
COBISS.SI-ID:14103045 This link opens in a new window

Licences

License:CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International
Link:http://creativecommons.org/licenses/by-nc/4.0/
Description:A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.

Secondary language

Language:Slovenian
Keywords:računsko načrtovanje, generativno načrtovanje, strojno učenje, optimizacija, nadomestno modeliranje

Projects

Funder:ARRS - Slovenian Research Agency
Funding programme:Young researchers

Funder:ARRS - Slovenian Research Agency
Project number:P2-0210
Name:E-gradbeništvo

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