Floorplan Efficiency: A GNN-based evaluator

Project Workflow:

Conclusion and future work

Model verification and testing

Model training

GML Model selection

Define Parameters to encode on node, edge and graph levels

Dataset preparation

Problem definition & objective formulation

Project Workflow: Problem definition & objective formulation

With the growing pressure on cities available spaces for vertical or horizontal expansion, it's crucial to make the most efficient use of the available spaces without compromising the space qualities.

Conventional work flow of floor plan design is rigid regarding evaluating the designed plans as most of times they are evaluated at the end of the design process.

It will be more interesting and time saving to get to evaluate the designs while designing the floor plans, to provide a continuous feedback loop that helps designers enhance and modify their designs.

Project Workflow: Problem definition & objective formulation

The objective of this project is to develop an AI model that can instantly show floor plan performance metrics “Efficiency and quality” during the design process to help the designer in tackling the design problem

Project Workflow: Dataset preparation

Using magnetizing rooms plugin we generated around 4000 floor plans with different number of rooms and a circulation area. we ensured good variation within our dataset Through variening:

rooms count

corridor type “ one sided or Two sided “

plot size

Plot proportions entrance position

Some Samples

FACULTY: David Andrés León, FACULTY Assistant : Dai Kandil

Ziqi Cui & Mostafa Ahmed

Project Workflow: Dataset preparation

4000 Samples Dataset

Corridor= 0 rooms= 1

Limitation due to the plugin used

is always fixed

Project Workflow: Model training

Graph Samples “ Classified by quality ”

Project Workflow: Model training

16h:

64h:

FACULTY: David Andrés León, FACULTY Assistant : Dai Kandil

Ziqi Cui & Mostafa Ahmed

Project Workflow: Model training

192h:

Model Accuracy = 98%

Project Workflow: Model verification

and testing

Class D

Class A

Class C

Class B

Project Workflow: Model verification and testing

Plans within training Domain

Actuall Classification = Class A

Predicted Classification = Class A

Actuall Classification = Class B

Predicted Classification = Class B

Actuall Classification = Class C

Predicted Classification = Class C

Project Workflow: Model verification and testing

Plans out of training Domain

Actuall Classification = Class A

Predicted Classification = Class A

Actuall Classification = Class B

Predicted Classification = Class B

Actuall Classification = Class B

Predicted Classification = Class B

Project Workflow: Model verification and testing

Plans out of training Domain

Actuall Classification = Class A

Predicted Classification = Class B

Error margin = 0.02

Actuall Classification = Class B

Predicted Classification = Class B

Here we tried different scales and the result is the same

“ model is not affected by plan area”

Actuall Classification = Class B

Predicted Classification = Class A

Error margin = 0.01

Project Workflow: Model verification and testing

Plans out of training Domain

Actuall Classification = Class D

Predicted Classification = Class C

Error margin = 0.1

Actuall Classification = Class D

Predicted Classification = Class C

Error margin = 0.15

Project Workflow: Conclusion and future work

Conclusion:

The model performed in a very goodway regarding predicting plans quality within training domain, also it’s performance was good for predicting unseen out of training domain data with a minor margin of error ( only one class up or down in some plans ), overall graph SAGE classification showed a very good potential for our particular problem with acceptable ability to generalize.

Future work:

Our current research is based on 2D plan exploration. In the future, we hope to use similar methods to train prediction models suitable for 3D building layout quality.

Moreover the plugin we used to generate the dataset is limiting our possibilities to only train for quality maybe we can use another approach in plan generation to include more measures like efficiency.