Immanuel Koh 的作品 AI Sampling Singapore 住宅建筑

设计师 Immanuel Koh 的作品 AI Sampling Singapore 住宅建筑荣获了极具声望的 2025 A' Design Award 铁奖，竞赛类别为生成式、算法、参数化与人工智能辅助设计。

铁生成式设计· 2025

AI Sampling Singapore 住宅建筑

设计师： Immanuel Koh

客户: Immanuel Koh

生成式设计类别A' Design Award 铁奖获奖者

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关于 AI Sampling Singapore 住宅建筑

新加坡是一座人造城市。通过对新加坡高层建筑的大量3D数字模型数据集进行训练，这一定制设计的AI模型不仅生成形式上合理、语义上连贯的配置，还开始构想新颖而奇异的建筑形态，在板式、点式和组团式等标准高层住宅类型之间进行内插与外推。

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关于A' Design Award 铁奖

AI Sampling Singapore 住宅建筑是 A' Design Award 铁奖得主，这是授予满足人们对设计精良的创作所期待的专业与工业要求的优良设计的声望认可。铁奖得主展现出其创作者的高超技艺、高度专业化、专家级的理解力和创造能力。预计AI Sampling Singapore 住宅建筑将成为一件备受推崇、令人钦佩且被珍爱的创作，既实用又创新。这件赢得 A' Design Award 铁奖的设计整合了行业最佳实践，并拥有与专业知识和才华相结合的称职技术特性。AI Sampling Singapore 住宅建筑因其严谨而受到尊重，旨在提供有助于让世界变得更美好的生活品质改善。

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由设计师提供的致谢、团队成员及技术详情。

独特属性: Is it possible to sample at scale and at high-resolution every single building ever built in a country? If so, can such a dataset be used to train an AI model in generating new yet locally compliant buildings without any explicit regulatory control inputs? The project explores the design agency of deep generative neural networks in learning three-dimensional exteriority and interiority with a redesigned 3D generative adversarial network (3D-GAN) AI model. Winner of WAFX2024, IDA2024 and BLT2024.
尺寸与包装: This is a digital AI x Architecture project. Each generated output is a new high-rise high-density residential public housing of up to 30 floors.
标签: AI
生产技术: A custom generative AI model was trained from scratch using proprietary code base and dataset. Specifically, a 3D generative adversarial network (3D-GAN) AI model was trained to learn semantic and spatial configuration of high-rise high-density residential public housing in Singapore.
设计挑战: The non-existence of 3D models and their corresponding floor plans was the first challenge. New workflow and tools had to be developed to not only collect (i.e., digitally and physically), but create (annotate) and curate (i.e., exploratory data analysis) such high quality dataset to train an AI model from scratch. Technically difficult due to the scalability of similar CAD representation which was overcome via a novel AI latent representation of 3D.
研究: A custom generative AI model was trained from scratch using proprietary code base and dataset. Specifically, a 3D generative adversarial network (3D-GAN) AI model was trained to learn semantic and spatial configuration of high-rise high-density residential public housing in Singapore.
灵感: Singapore is an artificial city! Trained with a large dataset of 3D digital models of high-rise buildings found in Singapore, the custom-designed AI model generates not only formally plausible and semantically coherent configurations, but begins to also imagine novel and uncanny architectural forms, interpolating and extrapolating among standard high-rise housing typologies such as the slab, point, and cluster blocks. Project exhibited at Venice Architecture Biennale and Singapore's Arts House.
项目周期: This is an on-going and longer-term AI x Architecture research being conducted at the university design lab since 2021. It was however only published much later as an official architectural design award entry from 2024 onwards.
操作流程: This is a digital AI x Architecture generative research project, while the dataset was digitally collected from existing physical buildings in Singapore.
专利: Currently no patent.
团队成员: Artificial-Architecture
图片版权: All credits: Artificial-Architecture