SUPERVISED LEARNING // ROOF FORM RECOGNITION

Aims:

The project aims to train the neural network to distinguish between four different roof forms, including, shed roof, hip roof, barrel vault and flat roof.

Learning Objectives:

• Understand a range of machine learning methods
• Be able to couple parametric models with machine learning tools
• Setup supervised learning using Grasshopper
• Be aware of data preprocessing necessary for successful training

Strategies:

• The number of faces
• The sum of XY components of the normal vector of the roof surface(s)
• The Z component of the unitized normal vector of the roof surface(s)
• The volume difference between the mass and the bounding box of the mass
• The change in tangent in the sectional profile

Roof Form Recognition is a project of IAAC, Institute for Advanced Architecture of Catalonia developed in the Master in Advanced Architecture 2019/20 by Timothy Ka Kui Lam and Faculty: Mateusz Zwierzycki