论文标题
城市设计和快速解决方案探索的生成方法
Generative methods for Urban design and rapid solution space exploration
论文作者
论文摘要
人口的迅速增长和气候变化推动了大规模的城市更新和城市化。需要新的计算方法，以更好地支持城市设计师，以开发可持续，富有的和宜居的城市环境。大师图的城市设计空间探索和多目标优化可用于加快计划，同时考虑不同的利益相关者要求和基于模拟的绩效反馈，通过合并生成参数建模来实现更好的设计成果。但是，缺乏可以与模拟和各种设计性能分析相结合的城市形式产生的可推广和综合方法限制了工作流程的可扩展性。这项研究介绍了一种基于张量的生成城市建模工具包的实现，该工具包通过与犀牛/蚱hopper生态系统及其城市分析和环境性能模拟工具进行集成，从而促进了快速设计空间探索和多目标优化。我们的张量场建模方法为用户提供了一种通用的方式，用于编码上下文约束，例如滨水边缘，地形，视轴，现有街道，现有街道，地标，地标和非几何设计输入，例如网络方向性，诸如所需的街道，街道，设施，建筑物以及作为建模者可以称重的力量的人。这使用户可以生成许多不同的城市面料配置，这些配置类似于现实世界中的城市，但模型输入很少。我们提出了一个案例研究，以证明拟议框架的灵活性和适用性，并展示建模者如何识别设计和环境性能协同作用
Rapid population growth and climate change drive urban renewal and urbanization at massive scales. New computational methods are needed to better support urban designers in developing sustainable, resilient, and livable urban environments. Urban design space exploration and multi-objective optimization of masterplans can be used to expedite planning while achieving better design outcomes by incorporating generative parametric modeling considering different stakeholder requirements and simulation-based performance feedback. However, a lack of generalizable and integrative methods for urban form generation that can be coupled with simulation and various design performance analysis constrain the extensibility of workflows. This research introduces an implementation of a tensor-field-based generative urban modeling toolkit that facilitates rapid design space exploration and multi-objective optimization by integrating with Rhino/Grasshopper ecosystem and its urban analysis and environmental performance simulation tools. Our tensor-field modeling method provides users with a generalized way to encode contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, and non-geometric design inputs such as network directionality, desired densities of streets, amenities, buildings, and people as forces that modelers can weigh. This allows users to generate many, diverse urban fabric configurations that resemble real-world cities with very few model inputs. We present a case study to demonstrate the proposed framework's flexibility and applicability and show how modelers can identify design and environmental performance synergies that would be hard to find otherwise
