THE POTENTIAL OF GENERATIVE DESIGN IN THE ADAPTIVE REUSE OF INDUSTRIAL ARCHITECTURE
Article Sidebar
Main Article Content
Abstract
This article examines the use of generative design for the functional zoning of industrial heritage sites for adaptive reuse. A method for parameterizing an existing building and the requirements for its functions is proposed using Rhino, Grasshopper, Ladybug, and Galapagos. A comparison of manual and generative approaches is performed using a hall-type industrial building as an example.
Subject of the study: Functional zoning of industrial heritage sites using generative design.
Materials and methods: Digital model of a hall-type industrial building, parametric modeling in Rhino and Grasshopper, illumination analysis in Ladybug, and optimization of zoning options using Galapagos.
Results: A method for generative functional zoning was developed and tested. The generative approach demonstrated higher cost effectiveness compared to manual design and allowed for the identification of additional space use scenarios.
Conclusions: Generative design can be effectively applied at the pre-project analysis stage during the reconstruction of industrial heritage sites, reducing labor costs and improving the quality of decisions made.
Article Details
References
Егоров М. В. Этапы развития промышленной архитектуры. Кризис типологии промышленной архитектуры // Инновационная наука. 2022. №5-2. URL: https://cyberleninka.ru/article/n/etapy-razvitiya-promyshlennoy-arhitektury-krizis-tipologii-promyshlennoy-arhitektury (дата обращения: 11.05.2025).
Reconstruction of the gray belt objects based on energy efficiency clusters / L. Talipova, E. Shonina, K. Strelets, S. Lapteva // E3S Web of Conferences : 2018 International Science Conference on Business Technologies for Sustainable Urban Development, spbwosce 2018, St. Petersburg, 10–12 декабря 2018 года. Vol. 110. – St. Petersburg: EDP Sciences, 2019. – P. 01021. – DOI 10.1051/e3sconf/201911001021
Strategies for redevelopment of gray belt objects on the basis of neural networks / E. D. Kosyakov, L. V. Talipova, M. A. Romanovich [et al.] // Construction of Unique Buildings and Structures. – 2018. – No. 7(70). – P. 31-42. – DOI 10.18720/CUBS.70.3
Talipova, L. Methods for converting industrial zones / L. Talipova, E. Kosyakov, I. Polyakova // IOP Conference Series: Earth and Environmental Science, Khabarovsk, 10–13 апреля 2017 года. Vol. 90. – Khabarovsk: Institute of Physics Publishing, 2017
Салех Мария Сальвановна Внедрение цифровых методов на различных этапах архитектурного проектирования // AMIT. 2021. №1 (54). URL: https://cyberleninka.ru/article/n/vnedrenie-tsifrovyh-metodov-na-razlichnyh-etapah-arhitekturnogo-proektirovaniya (дата обращения: 05.05.2025).
Лаушкина Анастасия Александровна, Басов Олег Олегович Применение методов генеративного дизайна с использованием мультимодальных данных в сфере архитектуры и градостроительства // Научный результат. Информационные технологии. 2021. №3. URL: https://cyberleninka.ru/article/n/primenenie-metodov-generativnogo-dizayna-s-ispolzovaniem-multimodalnyh-dannyh-v-sfere-arhitektury-i-gradostroitelstva (дата обращения: 05.05.2025).
Serdar S.E., Kaya M.E. Generative landscape modeling in urban open space design: an experimental approach // Proceedings of Digital Landscape Architecture Conference 2019. – 2019. – P. 150–157. – URL: https://www.dla-conference.com/wp-content/uploads/2019/06/DLA19_05-24_1500_Serdar_Modelling-Urban-Open-Space.pdf (дата обращения: 04.05.2025).
Комарова А. А., Пыхтюк С. В., Чернышов Д. А., Дымченко М. Е. Образование архитектурной формы с применением алгоритмических методов // ИВД. 2019. №8 (59). URL: https://cyberleninka.ru/article/n/obrazovanie-arhitekturnoy-formy-s-primeneniem-algoritmicheskih-metodov (дата обращения: 05.05.2025).
Gunagama M. G. Generative algorithms in alternative design exploration // SHS Web of Conferences. – 2018. – Vol. 41. – Art. 05003. – Proceedings of eduarchsia 2017. – DOI: 10.1051/shsconf/20184105003. – URL: https://doi.org/10.1051/shsconf/20184105003 (дата обращения: 11.05.2025)
Nagy D., Lau D., Locke J., Lee B., Weber N., Schlueter A. Project Discover: an application of generative design for architectural space planning // Autodesk Research. – 2017. – 14 p. – URL: https://damassets.autodesk.net/content/dam/autodesk/research/publications-assets/pdf/project-discover-an-application.pdf (дата обращения: 11.05.2025)
Li Z., Li S., Hinchcliffe G., Maitless N., Birbilis N. Automated architectural space layout planning using a physics-inspired generative design framework // arxiv preprint arxiv:2406.14840. – 2024. – 25 p. – URL: https://arxiv.org/abs/2406.14840 (дата обращения: 11.05.2025)
Johan R., Chernyavsky M., Fabbri A., Madrazo L., Melenhorst M. Building intelligence through generative design: structural analysis and optimisation informed by material performance // Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2019). – 2019. – P. 895–904. – URL: https://www.researchgate.net/publication/333104509 (дата обращения: 04.05.2025).
Голикова, Я. А. Применение генеративного дизайна при расчете параметров солнечного воздействия / Я. А. Голикова, С. В. Киселев, Л. В. Талипова // Вестник гражданских инженеров. – 2023. – № 3(98). – С. 77-87. – DOI 10.23968/1999-5571-2023-20-3-77-87
Dorta T., Kinayoglu G., Hoffmann M. Performance-based generative design: an investigation of the integration of performance simulation and parametric design tools // Proceedings of the 100th ACSA Annual Meeting. – 2012. – P. 257–265. – URL: https://www.acsa-arch.org/proceedings/Annual%20Meeting%20Proceedings/ACSA.AM.100/ACSA.AM.100.33.pdf (дата обращения: 04.05.2025).
Бжахов М. И., Ефимова М. М., Журтов А. В. Алгоритмическое проектирование в архитектуре // ИВД. 2018. №2 (49). URL: https://cyberleninka.ru/article/n/algoritmicheskoe-proektirovanie-v-arhitekture (дата обращения: 05.05.2025).
Lee Ch., Shin S., Issa R. R. A. Multi-objective optimization of a free-form surface based on generative designs // Proceedings of the 18th International Conference on Computing in Civil and Building Engineering (ICCCBE 2020) / eds. Eduardo Toledo Santos, Sergio Scheer. – Lecture Notes in Civil Engineering, vol. 127. – Springer, Cham, 2021. – P. 1252–1261. – DOI: 10.1007/978-3-030-51295-8_90
Boon C., Griffin C., Papaefthimious N., Ross J., Storey K. Optimizing spatial adjacencies using evolutionary parametric tools: using Grasshopper and Galapagos to analyze, visualize, and improve complex architectural programming // Perkins+Will Research Journal. – 2015. – Vol. 07(02). – P. 25–37. – URL: https://www.brikbase.org/sites/default/files/PWRJ_Vol0702_02_Optimizing_Spatial_Adjacencies_Using_Evolutionary_Parametric_Tools.pdf (дата обращения: 11.05.2025)
Birkemo A. S., Samarakoon S. M. K. Application of generative design for structural optimization at the conceptual design phase // Building Information Modelling (BIM) in Design, Construction and Operations IV. – 2021. – P. 139–150. – URL: https://uis.brage.unit.no/uis-xmlui/bitstream/handle/11250/3057274/BIM21012FU1.pdf?Isallowed=y&sequence=1 (дата обращения: 11.05.2025)