Simulating environmental impacts on building envelopes using machine learning and advanced data analytics

工程师们在很大程度上依赖于模型建筑的风洞测量来评估包层和结构荷载、建筑物周围的局部分散以及行人水平的风。自20世纪70年代初以来，风洞测试是一种广泛使用的工业标准方法。然而，风洞测试既昂贵又耗时。该项目将研究挖掘和利用历史、物理尺度风洞测量数据的可行性，以开发和训练能够生成合成、真实结果的机器学习算法，而无需定制风洞测试。这种能力将允许合格的风力工程师使用“合成”（即机器学习衍生）风洞类型的结果，快速且经济有效地提供早期设计咨询。

Engineers rely heavily on the use of wind tunnel measurements of model buildings to assess cladding and structural loads, local dispersion around buildings, and pedestrian level winds. Wind tunnel testing is an industry-standard approach that has been widely used since the early 1970's. However, wind tunnel testing can be both costly and time consuming. This project will investigate the feasibility of mining and leveraging data from historical, physical scale wind tunnel measurements to develop and train machine learning algorithms capable of generating synthetic, realistic results without the need for bespoke wind tunnel testing. This capability will allow qualified wind engineers to use 'synthetic' (i.e., machine learning derived) wind tunnel type results to provide early design consultation both quickly and cost-effectively.