Treffer: 人工智能辅助的“基于案例设计” ——以深圳南头古城周边地区城市肌理织补为例.

"Case-based design" (CBD) is a design method developed in the 1980s. Unlike the first generation of rule-based design methods used in the 1960s and 1970s, "CBD" simulates the cognitive behaviors of people based on past empirical knowledge to address new challenges. First, CBD establishes a case database by systematically extracting historical case features. Next, it searches and matches appropriate solutions using structured features when facing fresh design problems. CBD proponents argue that addressing complicated design problems without optimal solutions is more suitable. 　　This paper reviews the development process of "CBD". Importantly, it shows the great potential of combining "CBD" with the latest artificial intelligence (AI) technology and summarizes the problems encountered while integrating the newest methods with the AI technology: 1) On an urban scale, most of the existing methods are "integral generation", which cannot effectively generate the repair 开放科学（资源服务） 标识码（OSID） and filling of specific types of urban forms; 2) Most of the present methods lack further evaluation and screening of the generation results; 3) It is challenging to mark data and operate with the current methods, which are also unfriendly to designers. 　　DeepCity, an AI-assisted design system, is presented in this study to address these issues. The primary features of this system are: 1) automatically recognizing and analyzing different types of urban morphology based on an image clustering algorithm and making the AI model learn specific kind of morphological modes, thus realizing the texture darning of specific urban morphological types; 2) The AI model is trained to output performance indicators according to urban morphological image features. The model can directly and quickly evaluate the physical performance indicators of the generated results and assist designers in screening, modifying, and deepening schemes; 3) realize the full-process automation from case scanning, data annotation, model training and generation, and design evaluation to data vectoring by combining the Python program and the grasshopper (GH) platform, which designers widely use. This action reduces the user threshold and time cost of designers. 　　The working process of cooperation between DeepCity and designers was elaborated from design cognition, design generation, and design evaluation based on a case study on urban texture darning surrounding the ancient city of Nantou in Shenzhen. The findings of DeepCity in urban morphological clustering evaluation and fast generative morphological thermal environment prediction were evaluated using quantitative analysis. DeepCity can effectively assist designers in conducting typological analysis on urban morphology and generating design prototypes with historical context features. Moreover, it can make faster evaluations of the physical properties of design schemes. Finally, the application potentials and shortcomings of the AI-assisted "CBD" system were discussed and summarized. The results are expected to enlighten field researchers and further promote AI-assisted design practices. [ABSTRACT FROM AUTHOR]

梳理了“基于案例设计”的发展历程, 针对最新的与人工智能 结合的基于案例设计系统, 数据标注困难, 操作难度大, 且大多数城 市生成的研究仅关注于整体式生成, 对于同一城市不同的肌理并未进 行细致区分等问题, 提出了一个与 Grasshopper 平台结合的人工智能 辅助设计系统 DeepCity. 该系统从设计认知, 设计生成, 设计评估三 个方面, 为设计师提供基于图像聚类的城市形态类型学分析, 基于生 成对抗网络的指定城市形态自动织补与室外热环境的快速评估。最后, 讨论总结了人工智能辅助基于案例设计的应用潜力与不足. [ABSTRACT FROM AUTHOR]

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)