Algorithms-aided design: Case studies 4-7

Last week we looked at the first 3 studies we developed for exhibition “All I Ever Had Was an Idea” in September 2019. Now it’s time to expand our brains a bit more and look for the number 4,5,6 and 7.

If you missed the basics about AAD, let’s head back to this article. If you know what’s what, than you may proceed and read the rest of this article.

Scenario 4 – Energy Edge

One of the important parts of indoor climate is the thermal comfort. The regulations dictate how much heat is allowed to escape through the building envelope. This pushes designers to make buildings that are energy efficient, which in turn lowers the load on the resources needed for a building to operate. On the other hand, this has also its negative side. Tight rectangular buildings are energy efficient, however they lack many social aspects.

The aim of this scenario is to find a balance between energy efficiency and opening up the building. On one side, we have an introverted, closed volume. On the other side, we have completely separated units, with free space in-between, forming terraces and paths through.

Aim is to find the exact point where we achieve the required thermal properties, while maintaining as much porosity as possible. In order to do that, we divide our building volumes into walls, roofs and slabs. Or as many different assemblies we might expect. And we make the software calculate only the areas which are exposed to the outside. If we start to pull the boxes apart, more and more areas will be exposed to the surrounding environment. And we want to stop at the exact point where we get as much openness as possible while stile fulfilling the requirements.

Scenario 5 – Boundary Bash

Scenario number six is all about analysis. The goal is to determine whether it will be worth the effort to develop a plot of land, based on the setbacks and calculating areas that can be rented.

The setbacks we have to deal with is the distance from the boundary of the plot and shading of the street.

Based on these setbacks, the maximum possible volume of the building mass emerges. In the next step, floors that are too small are eliminated. After that, preliminary plan is drafted in order to determine how much space is needed for circulation. From this set of inputs, data about floor areas are collected. We can now clearly evaluate the saleable and non-saleable areas and the ratio between them, which allows us to re-evaluate the floor plan and we can attempt to make it more efficient or decide that based on the current parameters, the development would not be profitable.

The form of the sixth scenario is tightly bound to the maximum allowed volume on a given plot. The form is therefore pre-defined by the authorities who imposed the rules. Yet we see that the resulting form is quite exciting despite its origin.

Scenario 6 – Futuropolis

How will the cities of tomorrow be designed? And can algorithmic tools help us? There are so many parameters to explore in an endeavour as complex as creating cities.

In this scenario, we first divided the area into reasonably sized units ready to be developed. These were then grouped into zones, each represented by slightly different shape of the building. The area is then connected to the existing road network, creating corridors in-between. The buildings then adjust their height according to the proximity to the main road. Finally, part of the land is reserved for a park and local landmarks are set up on edges of different zones.

In this way, the citizens can orient easily in the grid framework as well as locate their destination in relation to the landmarks. The vast park area allows the city to breathe and provides space for outside activities.

The first important point to observe is the height of the buildings, which dynamically changes depending on the distance from the main road, which forms two main axes. The highest buildings are located at the junction of the roads, since they are easily accessible and the further we go from the main artery, the more suburban character the neighbourhood has.

Scenario 7 – Soil Redistribution

The final scenario focuses on landscape. After soil for foundation and basement is excavated there are often many cubic meters of soil that would normally need to be stored and later used somewhere else. Now we have the opportunity to precisely count how much soil will become available and redistribute it in the vicinity of the excavation instead of transporting it far away.

The script starts with counting the volume of the soil which will be excavated due to the construction. As a second step, we define parameters of the desired shape and size of the landscaping elements. At the same time, we define the possible positions for these elements. The position is pseudorandom placement, based on the proximity of points. The script will then return sizes and positions based on the cubic meters of soil it has available.

Analysis of the result shows scattered hills and foliage across the whole site.
That creates many partially hidden spaces to explore and get lost among. Foot path connecting the surroundings slices through the site and winds around the building sitting in the upper third of the park.

The beauty of this definition lies in the control of the designer. The architect is still in control, the algorithm only checks and optimizes the result based on the available material. The shape and position is for the architect to decide. It could have been spheres, hexagons, line based hills or anything else they could imagine.

And there goes the second part of the case studies? Which one do you find the most useful? Can you imagine a scenario where AAD could be used? Let us know!

Cheers!