20.500.12556/RUL-156200 Towards machine learned generative design 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. computational design generative design machine learning optimization surrogate modelling računsko načrtovanje generativno načrtovanje strojno učenje optimizacija nadomestno modeliranje true false true Angleški jezik Slovenski jezik Članek v reviji 2024-05-14 09:59:46 2024-05-14 09:59:51 2024-05-15 03:45:23 0000-00-00 00:00:00 2024 0 0 16 str. Vol. 159, art. 105284 Mar. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004:624 0926-5805 10.1016/j.autcon.2024.105284 180736259 1-s2.0-S0926580524000207-main.pdf 1-s2.0-S0926580524000207-main.pdf 1 9297F1A5A0F5A09DFDF015F203129CBE a2b4edea9209bdd87e604df7c5f31b0459eded24eaa33587de5b11a3968d1f69 51c8de9d-11c8-11ef-a487-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=183524 https://www.sciencedirect.com/science/article/pii/S0926580524000207 1 f089c4c0-11c7-11ef-a487-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=183523 Fakulteta za gradbeništvo in geodezijo 0 0 0