A learning automated 3D architecture synthesis model: demonstrating a computer governed design of minimal apartment units based on human perceptual and physical needs
A learning automated 3D architecture synthesis model: demonstrating a computer governed design of minimal apartment units based on human perceptual and physical needs
复制标题
学习自动化 3D 建筑综合模型：演示基于人类感知和物理需求的计算机控制的最小公寓单元设计
作者：
D. Fisher;Nir Polak
本文提出了一种用于自动生成建筑环境的学习模型，并通过在密集的城市环境中创建最小的公寓来证明。该研究利用参数化建模、多标准优化和监督机器学习技术，提供最小公寓的3D配置，从重要的角度提高可视性，并通过“人群的智慧”定义功能布局。该模型旨在最大限度地提高生成单元的测量3D可见性——这是一种与低感知密度相关的属性，被认为对居民的福祉有积极影响。同时，该模型参与了一个学习过程，通过“人群的智慧”对每间公寓的适用性进行分类，通过开放的网络模拟收集。这种自动化的设计模型，考虑到感知和物理需求，展示了它在未来更大规模的密集城市环境发展中的潜力。
ABSTRACT This paper presents a learning model for the automated generation of built environments, demonstrated by the creation of minimal apartments situated in dense urban settings. The research utilizes the techniques of parametric modelling, multi-criteria optimization and supervised machine learning to provide 3D configurations of minimal apartments with improved visibility from significant viewpoints, and with a functional layout defined by ‘the wisdom of the crowd’. The model seeks to maximize the measured 3D visibility in generated units – an attribute associated with low perceived density, recognized as having a positive effect on the well-being of dwellers. Simultaneously, the model engages a learning process, through which the classification of the suitability of each apartment is refined through ‘the wisdom of the crowd’, collected through an open web-simulation. This automated design model, regarding both perceptual and physical needs, demonstrates its potential for future use in the development of larger-scale densified urban environments.