基于CGAN的居住区强排方案生成设计方法

Generative design method of forced layout in residential area based on CGAN

摘要:

居住区强排方案设计有助于提高项目容积率,是达成集约化建设的重要途径.既有强排设计多由设计者基于日照模拟分析结果,主观制定强排设计决策,设计效率较低.旨在立足深度学习技术语境,提出基于条件生成对抗网络（CGAN）的居住区强排方案生成设计方法,应用pix2pix算法,构建基于CGAN的居住区强排方案生成设计模型,通过学习低层、多层、高层居住区轮廓与强排设计方案总平面图的对应关系,生成任意居住区轮廓条件下的居住区强排设计方案,提高居住区强排设计精度与效率,推动城市土地的高效率利用.以中纬度地区的3个居住区为例,验证所提方法的应用效果,评价所生成方案的日照性能.结果表明:所生成低层方案可满足大寒日2 h日照要求,多层方案中96%的房间可满足日照要求,高层方案中84%的房间可满足日照要求,高层容积率＞3.0、多层容积率＞1.5、低层容积率＞0.5,说明所生成方案有效利用了城市用地,且应用所建立模型可在3 s内生成居住区强排设计方案,显著降低了强排设计耗时,提高了设计效率.

Abstract:

Forced layout design in residential area is beneficial for increasing plot ratio, which is an important approach to achieve intensive construction. Existing residential forced layouts are mostly made by designers subjectively based on the results of sunshine simulation analysis, with low design efficiency. In the context of deep learning technology, a generative design method for residential forced layout based on conditional generative adversarial network (CGAN) was proposed, applying pix2pix algorithm to construct residential forced layout generative model. By learning the corresponding relationship between the outline and the general forced layout of low-rise, multi-story, and high-rise residential areas, the model could generate residential forced layout under any outline conditions, which improves the precision and efficiency of residential forced layout design and promotes the efficient use of urban land. Three residential areas in mid-latitude region were taken as examples to verify the application effect of the proposed method and evaluate the sunshine performance of the generated scheme. Results show that the generated low-rise scheme could meet the sunshine requirement of 2 h in Great Cold day (around January 20); 96% of rooms in multi-story scheme and 84% of rooms in high-rise scheme could meet the sunshine requirement. The plot ratio of the high-rise scheme was more than 3.0, that of the multi-story scheme was more than 1.5, and that of the low-rise scheme was more than 0.5, indicating that the generated schemes make effective use of urban land. The constructed model could generate residential forced layout within 3 s, which significantly reduces the design time of forced layout and improves the design efficiency.