algorithmic modeling for Rhino
Playing the Game: Data-Driven Stadium Design Beyond the develop of complex forms and their rationalization, computation affords us the opportunity to employ data-rich approaches to design exploration. Data-driven approaches to design enable you to control the flow of information, directing a feedback loop centered on evaluating the impact of each design decision. This workshop will focus on this design approach for a large and complex typology, the Stadium.
Sports, in general, is increasingly affected by data. The rise of accessible computational tools has given baseball front offices the power to collect new knowledge from an old game (think Moneyball). This new knowledge, built from the statistical analysis of tens of thousands of individual data points, lets managers make better informed decisions about how best to win games.
Stadium design is well suited to employing similar methods in the design process because we can treat each seat as a data point. This means a typical stadium has tens of thousands of sample points to build an analytical model from. Each seat can be analysed for:
- Sightlines (C value, horizontal and vertical viewing angles, view distance, environmental obstructions)
- Environmental exposure (to sun and bad weather)
- Safety (exiting distances & widths)
- Comfort (prestige coefficient due to proximity to desirable areas of seating eg; front row at the lakers, distance to concessions and amenities)
The analytical model can assign these modes of analysis different weightings in the aggregate assessment of each seat. These weightings could be different between sports, allowing a proper assessment of multi-use facilities. But in the end, each seat gets a score which allows us to place a value on our design decisions. It allows us to measure the answer to the question - does this change to design make it better or worse?
Led by members of the Woods Bagot Sports team and Design Technology Community, participants will learn the basics of Stadium bowl design through parametric techniques in Rhino Grasshopper. A simple envelope design, driven by the stadium layout, will be evaluated in Diva for solar exposure at each seat. The "value" of each seat will then be analysed.
Finally, to balance analytic and empirical evaluation, participants will be taught the basics of the Unity game engine to experience a first-person experiential view from the seat as the final validation for the design. This will be followed by a short demonstration of the Oculus Rift.
This workshop will provide an introduction to data-rich workflows that focus on adding value through various programming and form decisions, verified through analysis and game-engine visualization.
Pre-requisites: Basic Rhino and Grasshopper knowledge