Towards machine learned generative design

Gradišar, Luka (avtor), Dolenc, Matevž (avtor), Klinc, Robert (avtor)

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
• UDK: 004:624
• ISSN pri članku: 0926-5805
• DOI: 10.1016/j.autcon.2024.105284
• COBISS.SI-ID: 180736259
• Datum objave v RUL: 14.05.2024

Gradivo je del revije

• Naslov: Automation in construction
• Skrajšan naslov: Autom. constr.
• Založnik: Elsevier
• ISSN: 0926-5805
• COBISS.SI-ID: 14103045

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