Immanuel Koh 嘅作品 AI Sampling Singapore 住宅建築

由設計師 Immanuel Koh 創作嘅 AI Sampling Singapore 住宅建築係備受推崇嘅 2025 A' Design Award 鐵獎得獎作品，獲獎類別為生成式設計。

鐵生成式· 2025

AI Sampling Singapore 住宅建築

由 Immanuel Koh

客戶: Immanuel Koh

生成式類別嘅 A' Design Award 鐵獎得獎者

60,960

經驗證嘅獲獎者

驗證設計 #170593 嘅獲獎狀態

訪問網站喺 A' Design Award 查看新聞稿獎項徽章

色彩

設計

關於 AI Sampling Singapore 住宅建築

新加坡係一座人工城市。透過大量新加坡高層建築嘅3D數碼模型數據集訓練,呢個度身訂造嘅AI模型唔單止能夠生成形態合理、語義連貫嘅配置,仲開始想像出新穎而又奇異嘅建築形態,喺板式、點式同組團式等標準高層住宅類型之間進行插值同外推。

榮譽認可

關於A' Design Award 鐵獎

AI Sampling Singapore 住宅建築是 A' Design Award 鐵獎得主，這是授予滿足人們對設計精良的創作所期待的專業與工業要求的優良設計的聲望認可。鐵獎得主展現出其創作者的高超技藝、高度專業化、專家級的理解力和創造能力。預計AI Sampling Singapore 住宅建築將成為一件備受推崇、令人欽佩且被珍愛的創作，既實用又創新。這件贏得 A' Design Award 鐵獎的設計整合了行業最佳實踐，並擁有與專業知識和才華相結合的稱職技術特性。AI Sampling Singapore 住宅建築因其嚴謹而受到尊重，旨在提供有助於讓世界變得更美好的生活品質改善。

設計背後

新聞資料包

由設計師提供嘅製作人員名單、團隊成員同技術詳情。

獨特屬性: 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.
尺寸同包裝: 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.
標籤: AI
生產技術: 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.
設計挑戰: 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.
靈感: 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.
項目時長: 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.
專利: Currently no patent.
團隊成員: Artificial-Architecture
圖片來源: All credits: Artificial-Architecture