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Building morphology generative design : a knowledge-driven paradigm considering comfort, context, and cost

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Název: Building morphology generative design : a knowledge-driven paradigm considering comfort, context, and cost
Autoři: Peng, Ziyu, 彭子禹
Přispěvatelé: Lu, WW, Webster, CJ
Informace o vydavateli: The University of Hong Kong (Pokfulam, Hong Kong)
Rok vydání: 2025
Sbírka: University of Hong Kong: HKU Scholars Hub
Témata: Architecture - Composition, proportion, etc, Machine learning, Mathematical optimization, Object-oriented programming (Computer science)
Popis: Artificial intelligence (AI) is reshaping the creative professions in an unprecedented way. Machines equipped with AI can not only perform repetitive tasks swiftly and accurately but also learn the tacit knowledge hidden inside data and make sensible decisions. Architecture is no exception. As an old profession that is dedicated to creating shelters for human beings across millennia, experience has been passed through generations of designers. The experience can be found both explicitly in technical treatises such as Yingzao Fashi and De Architectura as well as implicitly in building morphologies, structures, and materials. So, to enable machines to understand knowledge has become a research frontier. Generative design refers to a to-and-fro process in which designers deploy algorithms to produce and analyze design possibilities given user inputs. A twin of generative design paradigms arises. The rule-based paradigm uses relationships among design elements to generate variants, whereas the knowledge-driven paradigm applies machine learning to decode knowledge from past works into models and use them for design generation, evaluation, and optimization. While the former is popular, its reliance on one or several designers may lead to limited possibilities. In contrast, the knowledge-driven paradigm broadens the design frontiers by integrating the knowledge of past works. Nonetheless, the relevant literature is few. The thesis aims to advance the generative design field built upon the knowledge-driven paradigm. It does so by applying the paradigm in building morphology generative design and considering performances in terms of comfort, context, and cost. Building morphology allows designers to focus on forms and functions over the external ornaments. A three-step workflow: generation, evaluation, and optimization, is implemented. The first trains a machine learning model that can map input design variables to morphological outcomes. The ground-truth data is collected from Hong Kong, with the multivariate Random ...
Druh dokumentu: doctoral or postdoctoral thesis
Jazyk: English
Relation: HKU Theses Online (HKUTO); 991044923892203414; https://hub.hku.hk/handle/10722/354766
Dostupnost: https://hub.hku.hk/handle/10722/354766
Rights: The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Přístupové číslo: edsbas.F582B3D4
Databáze: BASE
Popis
Abstrakt:Artificial intelligence (AI) is reshaping the creative professions in an unprecedented way. Machines equipped with AI can not only perform repetitive tasks swiftly and accurately but also learn the tacit knowledge hidden inside data and make sensible decisions. Architecture is no exception. As an old profession that is dedicated to creating shelters for human beings across millennia, experience has been passed through generations of designers. The experience can be found both explicitly in technical treatises such as Yingzao Fashi and De Architectura as well as implicitly in building morphologies, structures, and materials. So, to enable machines to understand knowledge has become a research frontier. Generative design refers to a to-and-fro process in which designers deploy algorithms to produce and analyze design possibilities given user inputs. A twin of generative design paradigms arises. The rule-based paradigm uses relationships among design elements to generate variants, whereas the knowledge-driven paradigm applies machine learning to decode knowledge from past works into models and use them for design generation, evaluation, and optimization. While the former is popular, its reliance on one or several designers may lead to limited possibilities. In contrast, the knowledge-driven paradigm broadens the design frontiers by integrating the knowledge of past works. Nonetheless, the relevant literature is few. The thesis aims to advance the generative design field built upon the knowledge-driven paradigm. It does so by applying the paradigm in building morphology generative design and considering performances in terms of comfort, context, and cost. Building morphology allows designers to focus on forms and functions over the external ornaments. A three-step workflow: generation, evaluation, and optimization, is implemented. The first trains a machine learning model that can map input design variables to morphological outcomes. The ground-truth data is collected from Hong Kong, with the multivariate Random ...