Algorithmic Design: Office Illumination AI

Office Illumination

Fabian Danisch, Eric Esch

Chair Architectural University of

Office Illumination AI

Chair of Architectural Informatics

Prof. Dr.-Ing. Frank Petzold

Algorithmic Design

Ivan Bratoev, Frank Petzold

Fabian Danisch, Eric Esch 03683295, 03701534

Table of Contents

TopicNorms and values are as important as ever before, which leads to a lot of technical difficulties for architects and engineers. Meeting these conditions is often a tremendous amount of work, due to the fact, that some norms need simulations to be checked for correctness.

Such a norm is the illuminance norm for office buildings. Every workplace has a normed value for the illuminance that has to be met at specific locations where employees are working. For example, an office that does paperwork needs to provide 500 Lux on top of the working desk. However, in this booklet, we will focus on an office that does computer work, which means that it only has to provide 300 Lux for a standardized working place. [1] Doing these tests or simulations is time-intensive, as models have to be built, matched onto a correct simulation environment, and then calculated.

This project focuses on relieving the architect of some of his work, by replacing the simulation with an AI-generated segmentation, which tells the user, which spots need more lighting such as artificial lighting or a new set of windows. The booklet will feature some storyboards which led to the idea of using an AI for illuminance simulation as well as explanations of how it was implemented.

Storyboards

The first storyboard was born from the idea of sound isolation. Especially in old buildings, sound isolation isn‘t always as sophisticated as residents would like it to be. With the technology that we had in mind people would be able to improve the sound isolation of their house by themselves or have certain spots renovated. This can be useful for anybody, from students who want to spare their elderly neighbors from the noise that‘s coming from their party to home office workers that want to be able to concentrate despite traffic outside their apartment.

The idea would consist of using a smartphone to collect data on repelling sound waves to calculate or predict how much sound is being transmitted through the wall. Our AI would then detect where there are gaps in the sound isolation so that the user can see the areas that need improvement. This idea could also be spun further to help not just with existing buildings, but also to improve sound isolation when constructing new buildings.

However, this is a very delicate task and extremely hard to do because there are a lot of factors that influence the flow of soundwaves. Additionally, the technical side of how collecting information about incoming and outgoing soundwaves could prove difficult to realize. Moreover, the training data for such a neural network would probably need to be created manually which is impossible without the required technology. Resolving this by generating the data randomly would deliver more unrealistic results in return. Therefore, we decided not to pursue this topic any further.

Storyboards c.

The second idea came from the idea of rooms that are badly lit. Often rooms do not offer enough lighting to do some tasks at home and thus require cellphones and flashlights to enhance the lighting. This evolved then into the idea of creating a heatmap of a floor plan which tells the user, where he might need to add light sources to have a properly lit room.

To be able to recreate a real-life lighting scenario, one needs a simulation. Such a simulation can be made using Rhino, a framework that allows the creation of models. Those models can then be connected to another framework that simulates scenarios on that particular model. A good example of a lighting framework would be Honeybee, which runs in a grasshopper container.

These simulations can be created and made into ground truth images for an AI. In the end, the AI can predict the incoming light from the outside based on a very basic floorplan.

Because we saw that data could be generated comparatively easily and knew that this project was realizable concerning the technical part, we decided to further explore this idea.

Concept Dev.

The development of the concept was quite simple. The storyboard gave already a good overall view of what was achievable. The concept only had to be refined and structured. Those steps took their fair share of time, but eventually, the concept was finished.

What has changed from the original idea was that we restricted this application to the design of office buildings. This is because „good lighting“ can be very subjective. In the workspace, however, there exist clear definitions of how much light is required for different tasks. The AI can then be trained to distinguish between areas with „sufficient lighting“ and „insufficient lighting“. Also, we were aiming at giving recommendations as to how problems with lighting could be fixed. This is why we also wanted to include a category that denotes that the area does not receive enough light with the current layout but that it could be fixed by adding another window. Due to the complexity of creating data for this category, we decided not to include it in our prototype.

The core of the concept is simplicity, as it should be something anyone can use daily without needing a large background in architecture or informatics. This led to the use of a basic floor plan, which is fed as an image into a neural network. This step can be made automatically, such that the user only has to drag and drop the image. The result will again be a color-coded segmented image, which is easy to read.

As there are an infinite amount of possibilities to build floorplans, it was unavoidable to limit the AI to a specific subset of such plans. However, this booklet is more of a guidance to develop tools that facilitate the everyday use of AI than a perfect solution to a problem. That said, with enough time and knowledge it is without question extensible to much more difficult tasks or rather a larger variety of floor plans.

The concept was made thinking about tools for architects, but it turned out to be a tool that can be extended to meet other expectations as well. For example a interior designer might be interested on the already existing natural light sources, to make decisions on the design, or the property owner might want to have a rough idea about the lighting in the space he bought. Especially for those people this tool might come in handy, due to no needing any prior knowledge.

This is a representation of how the input should look like in theory. It is a very simple floor plan which indicates spots where windows are located and optionally also where doors are located.

This is the representation of an output image, where green indicates enough lighting. Orange indicates that there is insufficient lighting, but it can be fixed by adding windows, while red depicts, that only artificial lighting can solve the issue.

Data Structure

Probably the hardest part of this whole project is the creation of data points. As there is no similar data needed for the cause of this project, a whole new dataset had to be created.

For this, definitions had to be made, such as a fixed norm of how a floor plan should look like. This dataset was built while keeping other norms such as building heights in mind. We distinguish between our definitions to facilitate the process and the definitions based on norms in Germany. This means that the dataset does not address any other norms than the ones being handled in Germany, and is thus obsolete in other regions. However, this does not mean that it‘s impossible to build a similar dataset with norms from another region.

Personal definitions:

• No interior.

• White walls and grey carpet floors.

• Honeybee base definition for windows.

• Office building of 300 square meters with the same rectangular outer walls shape.

• No surroundings.

• Thin walls based on Honeybee Room from Solid feature.

• Weather data from Munich through EPW files.

Definitions based on norms and common values:

• Ceiling height of 3m, due to the floorplan having an area greater or equal to 300m². [5]

• Window size of 1,23m x 1,48m at a height of 0,95m. The window size is not standardized, but is a standard size often used in buildings in Germany. [3]

• Office table height at around 1m, which is again based on average values of office tables. [2]

• Grey carpet floor reflection of 0.06% and roughness of 0.1. [4]

To be more specific, no surroundings means, that the dataset does not take other than its location into account. That means that the floor plans are on large single planes, with no trees or buildings in the vicinity.

Measurements of the windows and their respective height to the floor.

Area of the floor plans that are used for this project. The inner walls are customizable.

3D Floor model built in Rhino.

3D Floor model with simulated illuminance data.

Calculation scheme for the si mulation in Grasshopper using an annotated image.

The workflow consists of 3 major steps.

First one needs to create a 3D model in any modeling software. For this project, Rhino was used. The floor plan was built with solid squares and extended by rectangular surfaces on the ceiling to simulate a thicker wall, which is needed for the training data. The top view will be used as input for the network.

The windows and doors are rectangular planes matched onto the solid walls. Overall the doors can be ignored as they do not affect the calculation, as they are solid non-transparent, but were still added into the design process, to make the rooms look more realistic. Such a model can be seen in the first

Thispicture.model

will be extended with calculations that output the simulated heatmap. Those calculations are made in Grasshopper with the Honeybee toolset. The produced results of the dataset use annual daylight studies from honeybee radiance. This process also sets some definitions for the simulation, such as the material used for walls and floors as well as the height of the simulation. The calculation can be seen in the second image on the next side.

The simulated data can then be seen in the next image. As the office building has to have 300 lux on working spots, the simulation is capped at that value, meaning that every higher value will have the same color condition as 300 Lux. This leads to the heatmap as seen in the picture. Everything red has a Lux value equal to or greater than 300 Lux, and can be seen as sufficient lighting for our classification later. Everything else does not suffice as office lighting and has to be corrected.

Lastly, the simulation can be segmented by hand, which can be seen in the last image on the next page. This is done using an open source annotation tool called CVat. [6] For annotating we decided to use 5 different classes:

• Wall (purple)

• Window (blue)

• Background (black)

• Sufficient Lighting (green)

• Insufficient Lighting (red)

The color mapping can be seen in the last image. The annotation will be used as ground truth for training the AI.

This concludes the creation of a single model for the dataset.

As time was limited and research on producing such a model had to be made, only 64 different models were made using this method. Each model went through the same process and was handcrafted.

AI Research

Our goal was to get an illuminance prediction from a floor plan. This suggests that we should use image classification, which directly receives the floorplan as input. Furthermore, it should then detect where the windows are and give an accurate prediction of the illuminance based on the direction of the window and the surrounding windows. To be able to predict each pixel separately we need to use the idea of image segmentation instead of classification, which normally assigns a class to a whole image. Image classification is a more refined version of image classification, as it assigns each pixel to a class it thinks it belongs. [9]

Concerning image segmentation, the most well-known architecture is the U-Net architecture. U-Net is a network that can be represented in a layered model which resembles the features of the letter U. First we go down a slope and downsample our image to smaller and smaller sizes. While doing that, we increase the number of features/filters and hope to catch the important information we want the network to detect. In our case, an example would be to detect the windows.

After we reach the bottom of the character U, we want to sample our image back up into a readable format. That means we want to decrease the features back to 3 features, which represent the RGB colormap of our output image, as well as increase the image size back to the input size. That can be seen as climbing the slope back up at the end of the letter U and representing the actual prediction. For us, this means that we produce the prediction of the actual illuminance.

The output image then needs to undergo a last step, in which the the best prediction is chosen for each pixel in the image. This is done using the well-known Softmax function.

A classic representation of a U-Net ar chitecture.[10]

The image above shows an example of a U-Net model. One can also see the actual form of the U, where the image is first downsampled and then upsampled again.

Prototype

After the concept phase, the actual model was built. First, we wanted to use the pre-built U-Net model from Tensorflow. However, because of compatibility issues, it was not available for training. Because of that, we utilize code from [11] which builds the U-Net from scratch. We then adjusted the code so that it is compatible with our dataset. The model is a 35-layered network with over 8 million trainable parameters. It doubles the filter size every time it downsamples the image and halves it when upsampling. It also uses dropout in the last two downsample layers.

The input images are padded so that they have a fixed size of 512x512. The pixels that are consequently added as padding are denoted as background. Using ReLU activation and a softmax activation at the end of the network we conclude the actual model.

The model is then fitted using the Adam optimizer and the sparse categorical cross-entropy loss function, over a hundred epochs and a batch size of 8. The batch size is quite small, as we did not have a lot of training data. Thus we also had a high split rate, as the model used a 90/10 train/validation split.

After fitting, the model concluded with an accuracy of 97.44%. The model was then tested on unseen images, where some resembled training data and some had completely new room layouts.

On the following pages, a few results will be shown and explained.

The first example is an empty room layout with no windows at all, where the input image can be seen on the left. The network should thus only detect the following classes: background, insufficient lighting, and walls. As seen on the right image the network managed to perfectly predict the room layout.

A second example would be another room layout with some windows. Again the left image is our input image. The middle image depicts the ground truth mask and the right image is the prediction. It can be seen, that the network did a very good job on predicting the lighting. It even kept the direction in mind, where the top windows let less light through then the bottom windows. The only problems are some boundaries on the inner walls, where the light shines through the walls. This is due to some training data having inner windows to make the predictions a little harder.

A more complicated example can be seen here. Larger lighting regions are now considered, due to more windows intersecting with each other. It can be seen, that the network still did a very good job of predicting the lighting. The clusters are a little off when it goes deeper into the room, but overall it resulted in a pretty good prediction.

The last example is a whole new room layout, which the network has never seen. The model had some problems predicting this image, as one can see that some windows are not matched or are missing. However, the actual lighting was predicted quite nicely. Naturally, it is the worst prediction the network has made, but it still managed to get most features correct.

Reflection

Simulating lighting takes time as models need to be built and simulated. These steps can take very long and most people cannot do this step without knowledge in the field of architecture or informatics. However using AI is a simple task for everyone, especially when embedded into a UI.

With the current technology, it is quite easy to build new models and train them quickly to test out different methods. Still though, for a real use case, one has to train much longer on much more data than what was used in this Lookingproject.

back at the results it can be seen, that the network was performing quite nicely. This means that we achieved a good basis for further development and signifies a step in the right direction. However, the dataset is very restricted and contains only a few layouts. For it to be used in a working environment, a lot more data is necessary. On top of that, some of the restrictions that we made could be loosened and integrated into the training. That way, the predictions would better reflect what the lighting conditions look like with other houses and trees surrounding the building. Additionally, parameters like wall colors, floor materials, interior, etc. could be integrated into the data and the model architecture. Consequently, our technology could be utilized in a more general way.

References

[1] Recommended Lighting Levels in Buildings, 2021, [2]https://www.archtoolbox.com/recommended-lighting-levels/Archtoolboxm,AVERAGEHEIGHTS/DIMENSIONSOFPERSONSITTING,2014, First

in Architecture, https://www.firstinarchitecture.co.uk/average-heightsdimensions-of-person-sitting/ [3] Fenstergrößen, Fensterversand.com, https://www.fensterversand.com/ fenstergroessen.php

[4] Light Reflectance, 2011, CIAL, Haufe[5]content/uploads/2015/06/Light-Reflectance-Fact-Sheet-FINAL.pdfhttps://www.carpetinstitute.com.au/wp-ArbeitsstättenverordnungBüro:MindestgrößeundRaumhöhe,2022,OnlineRedaktion,https://www.haufe.de/arbeitsschutz/sicherheit/ arbeitsstaetten-regel-wie-viel-platz-muss-im-buero-sein_96_224924.html

[6] CVAT, https://app.cvat.ai/tasks?page=1

[7] Cover image, 2022, Black Swan Studio, [9]professional/lighting-solutions/future-office[8]post/designer-office-lighting-the-latest-trends/https://blackswanstudio.eu/blog-Topicimage,2022,LEDVANCE,https://www.myledvance.com/TensorflowImagesegmentation,https://www.tensorflow.org/tutorials/ images/segmentation

[10] U-Net: Convolutional Networks for Biomedical Image Segmentation, Uni Freiburg, https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ [11] Deep Learning for Semantic Image Segmentation: A Worked Example

in TensorFlow: image-segmentation-a-worked-example-in-tensorflow/https://programmathically.com/deep-learning-for-semantic-