The MAA is a visionary master program with an innovative and open structure, mixing diverse disciplines, shaping professionals capable of producing theoretical & practical solutions towards responsive cities, architecture & technology.
Introduction to Optimization and Design Decision-making
Computational Design III (Level 2) advances data-driven design methodologies by integrating optimization, data analysis, and machine learning into architectural decision-making processes. Building on parametric workflows, the seminar explores how environmental simulation tools can inform iterative design through measurable performance metrics. Students engage with both single-objective and multi-objective optimization techniques to generate and navigate complex solution spaces. Expanding on this, the course introduces clustering methods and regression analysis to identify patterns and relationships within large datasets, supporting deeper insights into design performance. Through the application of multi-attribute decision-making techniques, participants develop transparent and reproducible strategies for evaluating and ranking design alternatives within complex architectural contexts.
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Abstract This project uses parametric design and multi-objective optimization to develop an interlocking masonry brick. A Grasshopper workflow generated brick geometries by varying sinusoidal width, depth, and cavity parameters. The designs were evaluated using structural and geometric performance criteria, including load transfer, web thickness, horizontal area, volume, and interlocking capacity. An evolutionary optimization process identified … Read more
Reciprocal Frames Reciprocal frames are self-supporting 3D structures. They consist of three or more sloping beams arranged in a closed circuit. Each beam rests on the preceding one and supports the next, creating a completely interdependent grid. This design eliminates the need for a central pillar or column. Traditionally these frames were made of wood, … Read more
Using climate clustering, vernacular precedent research, and evolutionary optimisation to create climate-responsive design typologies when no established local precedent exists Overview Lovingly named after the very first test location, Project Michigan is a tool that provides climate-responsive design guidelines when no local vernacular precedent exists. Through scraping climate data and global vernacular information, it helps … Read more
