Panel Clustering Using Machine Learning (Soumaya Museum)
Release date:2020
Author: Arie-Willem de Jongh
Skill level:Beginner
Language:English
Exercise files:Yes
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.
00 – Preview
01 – Introduction and creating the basic facade shape
02 – Populating the facade with hexagonal panels using Lunchbox
03 – Hexagon creation method explained using sphere-packing
04 – Creating similar shaped hexagons using Kangaroo – part 1
05 – Creating similar shaped hexagons using Kangaroo – part 2
06 – Implementing our Kangaroo forces onto our facade – part 1
07 – Implementing our Kangaroo forces onto our facade – part 2
08 – Running the simulation in Kangaroo and baking our facade geometry to Rhino
09 – Trimming the panels near the edges of the facade
10 – Machine Learning and SupervisedUnsupervised learning explained
11 – Our clustering method explained Gaussian Mixture Model
12 – Clustering some simple triangular shapes using the Gaussian Mixture Model
13 – Implementing the clustering rules on our facade panels – part 1
14 – Implementing the clustering rules on our facade panels – part 2
15 – Refining our clustering method
16 – Standardizing our facade by generating one panel per group
17 – Standardizing the spacing between the panels
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