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

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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.