typology-aware generative masterplanning

In computer science, artificial intelligence, and mathematical optimization, a heuristic (from Greek εὑρίσκω "I find, discover") is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution.

How can we propose urban configurations and optimize them in function of performance criteria ? How can we optimize a design intention ?

Parametric and generative models can explore the design-space of high-density massing. By producing,  we can evaluate and optimize rule-based typologies through the prism of comfort and energy indicators and provides appropriate decision making for further design development


Massing heuristics allows urban designers and architects to explore all the posible forms of a building in order to optimize his environmental quality. By fixing the domains where the project can change, the application developpes a large number of posibilities to be evaluated where designers can easily choose the one that suits to their requirements.

In this research, we've developed definition for three different massing typologies, and then evaluating and optimizing them by eight metrics. After optimizing all the typologies, we merged all the data together so its very convenient for us to compare each of them.






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