Panel Clustering Using Machine Learning: Soumaya Museum Facade Design Course
Acquire expertise in facade panel clustering with machine learning and parametric modeling through this comprehensive course. Learn techniques for standardization and optimization using Grasshopper.
Arie-Willem de Jongh
About this course
We’re going to design and rationalize the facade of the Soumaya Museum in Mexico. Opened in 2011 and one of the most visited museums in Mexico. Gehry Technologies did the whole facade design to fabrication process and we’re going to look into some of the techniques they used to populate and standardize the panels.
In the first part of the course we’ll design the facade and populate it with panels. We’re going to use a variety of tools which include Lunchbox for the panel generation and Kangaroo, a physics engine plugin for Grasshopper, to populate the facade and standardize the panels.
The second part and main part of the course will focus on clustering our panels using Machine Learning. We’ll first look at some example exercises to explain what clustering algorithm we’re going to use, how it works, and why we’ll use it. When we’ve understood the workflow and how the algorithm will cluster our dataset, we’ll move to our facade and extract the necessary information from our panels to feed into the algorithm.
In the last part we’ll analyze the various groups of panels created by the algorithm to see the variation of the panels. Based on that we’ll create a standardized panel per group and repopulate the facade. In the last step we’ll analyze our standardized facade and optimize the distances between the panels.
- Learn about the different Machine Learning methods and there applications.
- Use Rhinoceros 3d to design and create the basic facade shape.
- Use Grasshopper for the population of the facade with panels.
- Use Kangaroo to relax the panels on the facade to get them to have similar shapes.
- Use Grasshopper to organize the panels.
- Use the Grasshopper Lunchbox plugin to cluster and standardize the panels with the help of Machine Learning.
- Use the evolutionary solver Galapagos to optimize the family types of panels.
1.- Introduction and creating the basic facade shape08min 51seg
2.- Populating the facade with hexagonal panels using Lunchbox10min 32seg
3.- Hexagon creation method explained using sphere-packing09min 21seg
4.- Creating similar shaped hexagons using Kangaroo - part 114min 50seg
5.- Creating similar shaped hexagons using Kangaroo - part 210min 05seg
6.- Implementing our Kangaroo forces onto our facade - part 108min 02seg
7.- Implementing our Kangaroo forces onto our facade - part 220min 49seg
8.- Running the simulation in Kangaroo and baking our facade geometry to Rhino14min 15seg
9.- Trimming the panels near the edges of the facade09min 21seg
10.- Machine Learning and Supervised/Unsupervised learning explained09min 07seg
11.- Our clustering method explained: Gaussian Mixture Model15min 30seg
12.- Clustering some simple triangular shapes using the Gaussian Mixture Model12min 53seg
13.- Implementing the clustering rules on our facade panels - part 121min 24seg
14.- Implementing the clustering rules on our facade panels - part 212min 55seg
15.- Refining our clustering method08min 02seg
16.- Standardizing our facade by generating one panel per group16min 37seg
17.- Standardizing the spacing between the panels20min 33seg