Current performance-driven building design methods are not widely adopted outside the research field for several reasons that make them difficult to integrate into a typical design process. In the early design phase, in particular, the time-intensity and the cognitive load associated with optimization and form parametrization are incompatible with design exploration, which requires quick iteration. This research introduces a novel method for performance-driven geometry generation that can afford interaction directly in the 3d modeling environment, eliminating the need for explicit parametrization, and is multiple orders faster than the equivalent form optimization. The method uses Machine Learning techniques to train a generative model offline. The generative model learns a distribution of optimal performing geometries and their simulation contexts based on a dataset that addresses the performance(s) of interest. By navigating the generative model's latent space, geometries with the desired characteristics can be quickly generated. A case study is presented, demonstrating the generation of a synthetic dataset and the use of a Variational Autoencoder (VAE) as a generative model for geometries with optimal solar gain. The results show that the VAE-generated geometries perform on average at least as well as the optimized ones, suggesting that the introduced method shows a feasible path towards more intuitive and interactive early-phase performance-driven design assistance.
翻译:目前由性能驱动的建筑设计方法在研究领域之外没有被广泛采用,原因有几个,因此难以将其纳入典型的设计过程。在早期设计阶段,尤其是与优化和形式对称相关的时间密集度和认知负荷与设计探索不相容,而设计探索需要快速迭代。这一研究为以性能驱动的几何生成引入了一种新的方法,该方法能够直接在3D模型环境中进行互动,消除了对明确的对称化的需要,并且是比同等形式优化更快的多个订单。该方法使用机器学习技术来培训离线的基因化模型。该方法利用机器学习技术来培训一个离线的基因化模型。该基因化模型学习根据一套能够反映利益性能的数据集,对最佳性能和模拟环境进行分布。通过浏览基因化模型的潜在空间,可以迅速产生符合理想特征的几何形状。介绍了案例研究,展示了合成数据集的生成,并使用了一种蒸汽自动电解码(VAE),作为在最不理想的太阳能收益情况下的基因化模型。结果显示,在最接近于一种平均设计方法上展示了一种最可行的方法。