Performance Prediction of AI-generated Architectural Layout Design : Using Daylight Performance of Residential Floorplans as an Example
Research output: Conference Papers › RGC 32 - Refereed conference paper (without host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Number of pages | 12 |
Publication status | Published - 16 Nov 2024 |
Conference
Title | 2024 计算性设计学术论坛暨中国建筑学会计算性设计学术委员会年会 |
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Location | 同济大学 |
Place | China |
City | 上海 |
Period | 15 - 17 November 2024 |
Link(s)
Document Link | Links
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Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(6e246732-d61c-47d0-a4c9-cf90a3243253).html |
Abstract
The integration of artificial intelligence (AI) in architectural design, especially for generating floor plans, can greatly streamline the process. However, most AI-generated plans focus on form and spatial layout, often neglecting crucial performance evaluations because they are presented as images without the necessary geometric and physical properties for effective simulation. To address this limitation, we propose a novel approach that combines diffusion models with generative adversarial networks (GANs) for generating and evaluating floor plans. We fine-tuned a Low-Rank Adaptation (LoRA) model for creating residential floor plans, while a GAN quickly predicts daylighting performance. Our results show that the diffusion model generates a more varied set of floor plans compared to the training set. The GAN accurately assesses daylighting performance, with deviations from the ground truth not exceeding 5%, achieving a mean squared error (MSE) of 4.2 and a structural similarity index (SSIM) of 0.98. Additionally, it operates 267 times faster than traditional methods. This approach equips architects with a reliable tool for efficient early-stage design decisions, enhancing AI-driven workflows.
Research Area(s)
- Automated floor plan, Diffusion model, Generative design, Conditional generative adversarial network
Bibliographic Note
Research Unit(s) information for this publication is provided by the author(s) concerned.
Citation Format(s)
Performance Prediction of AI-generated Architectural Layout Design: Using Daylight Performance of Residential Floorplans as an Example. / Hu, Xiao; Zheng, Hao; Lai, Dayi .
2024. Paper presented at 2024 计算性设计学术论坛暨中国建筑学会计算性设计学术委员会年会, 上海, China.
2024. Paper presented at 2024 计算性设计学术论坛暨中国建筑学会计算性设计学术委员会年会, 上海, China.
Research output: Conference Papers › RGC 32 - Refereed conference paper (without host publication) › peer-review