To begin with, I took my Dynamo script from Module 5, where I was flexing the height and base radius of the building. I modified my custom node by adding steps to compute mass floor areas. As a result, I got gross floor area, gross surface area, volume, and mass floor areas. My next steps were creating custom nodes for calculating the carbon footprint and estimating the total rent price that depends on the view from the building.

For the first mode, I focused on the carbon footprint of the building based on the amount of concrete needed for the slabs. The inputs for the nodes were chosen as slab thickness, carbon factor, and gross floor area, which was calculated earlier with the first custom node I had. As inputs, the node makes a list of the total carbon footprint for the building as well as by floor.

For the second node, I decided to build a logic that would calculate the rental price per square foot based on the floor (higher levels will be more expensive) and the view. I started by inputting parameters t flex and getting a new building shape ID.

Then I used a new shape to identify its wall surfaces which were then used to calculate the values which would represent the directness to the desired object. To identify a desired object, I created a Revit shape and selected its location as a Golden Gate Bridge, and an ID of this element was one of the inputs for the node.

After that, I finally made a logic that would be used to calculate the price per square foot. I made inputs such as the total height of the building, inter-story height, mass floor areas, and an average price per square foot in San Francisco. I made assumptions for the top and bottom floor prices based on the average price and calculated the price for every floor. Then I multiplied the price by the directness coefficient and the area of the floor to get a price. An output gave me a list of the total prices (the sum of all the floor prices) for every tested case.

After testing the nodes, I combined the results into a single list that contained height, base radius, gross floor area, gross surface area, volume, carbon footprint, and price.

I decided that the best design alternative will be chosen based on space efficiency, carbon footprint, and price. My ranking might not seem realistic since I chose space efficiency as the top-ranked, carbon footprint as second, and rental price as third. Making the cost a top priority would make more sense since that's what the investors would care about the most, but I decided to go with efficiency and sustainability parameters. Before using a costume node, I found minimum and maximum values from every parameter column. Then I edited a node available for us In the shared library and got the score for each of the testing cases.

I highlighted the top three highest-score design options.

Final design and parameters:

Top height: 300 ft

Top Rotation: 90 degrees

Top Radius: 30 degrees

Mid Rotation:45 degrees

Base Rotation: 22.5 degrees

Base Radius: 60 degrees