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

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Izvleček
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.

Jezik:Angleški jezik
Ključne besede:computational design, generative design, machine learning, optimization, surrogate modelling
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FGG - Fakulteta za gradbeništvo in geodezijo
Status publikacije:Objavljeno
Različica publikacije:Objavljena publikacija
Leto izida:2024
Št. strani:16 str.
Številčenje:Vol. 159, art. 105284
PID:20.500.12556/RUL-156200 Povezava se odpre v novem oknu
UDK:004:624
ISSN pri članku:0926-5805
DOI:10.1016/j.autcon.2024.105284 Povezava se odpre v novem oknu
COBISS.SI-ID:180736259 Povezava se odpre v novem oknu
Datum objave v RUL:14.05.2024
Število ogledov:418
Število prenosov:66
Metapodatki:XML DC-XML DC-RDF
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Gradivo je del revije

Naslov:Automation in construction
Skrajšan naslov:Autom. constr.
Založnik:Elsevier
ISSN:0926-5805
COBISS.SI-ID:14103045 Povezava se odpre v novem oknu

Licence

Licenca:CC BY-NC 4.0, Creative Commons Priznanje avtorstva-Nekomercialno 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by-nc/4.0/deed.sl
Opis:Licenca Creative Commons, ki prepoveduje komercialno uporabo, vendar uporabniki ne rabijo upravljati materialnih avtorskih pravic na izpeljanih delih z enako licenco.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:računsko načrtovanje, generativno načrtovanje, strojno učenje, optimizacija, nadomestno modeliranje

Projekti

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Program financ.:Young researchers

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P2-0210
Naslov:E-gradbeništvo

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