Annie Helmes

1. Generative Design Framework:

I initially brainstormed some ideas of structures and spaces I would potentially want to create. Then, I used the Generative Design Framework to think more critically about the objectives of these designs in the input variables, evaluators and types of tradeoffs I would have to consider with each.

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DESIGN DECISION 1

Objective: How can I design a Green Roof that reduces the carbon footprint and is easily constructable but remains affordable?

Design Variables:

  • Roof Area
  • Number of Plant Trays
  • Thickness of Layers

Evaluators:

  • Carbon Sequestered
  • Construction Costs
  • Weight of Green Roof
  • Assembly Time

Tradeoffs:

  • Weight of Green Roof (want low) vs. Carbon Sequestered per SF (want high)
  • Assembly Time vs. Cost of Green Roof Plant Trays

DESIGN DECISION 2

Objective: How can I design a courtyard that maximizes human comfort and aesthetics but provides enough shading from daylight?

Design Variables:

  • Size of Courtyard
  • Number of Benches for People
  • Sunlight Exposure

Evaluators:

  • Appearance Quality
  • Human Comfort
  • Cost of Construction
  • Shading Availability

Tradeoffs:

  • Cost of Construction vs Appearance Quality
  • Sense of Comfort vs Size of Courtyard

DESIGN DECISION 3

Objective: How can I design a barn that functions as a garage to hold enough vehicles while remaining relatively affordable?

Design Variables:

  • Area of Barn
  • Number of Garage Doors
  • Volume of Barn

Evaluators:

  • Appearance Quality
  • Cost of Construction
  • Number of Cars it can hold

Tradeoffs:

  • Cost of Construction vs Number of Cars it can hold
  • Appearance of Barn vs Volume compared to attached home

2. Choose one of above Design Decisions and Study:

Chosen Design Decision: #1 - The Green Roof

Inspiration:

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For the generative process of my green roof design, I wanted to keep things simple. I started by defining the three study inputs as well as constants for the project. The study variables are: width of the roof, additional thickness of the roof (that is added to the thickness of the plant trays), and number of modular plant trays in each direction. I added some constraints for such inputs in terms of the range: for width (30ft - 70ft), for thickness (0.5-0.75ft), and for number of trays (4-50) as integers. For the plant trays, I chose to use the “Plant Tray Modular Green Roof” from TileTech Paver System. Some information regarding the trays themselves can be found below:

https://tiletechpavers.com/products/plant-tray-modular-green-roof/?gclid=CjwKCAjwgqejBhBAEiwAuWHioG6htpE2SKJEvTjgg9qbL3D-xkqnFendmqgUolF9JxqUrgDHipb5oRoCMGIQAvD_BwE

The constants are the length of the roof, cost of the plant tray per volume ($30 per CF), thickness of the tray itself at 3.25 inch, weight of tray (3lbs/ft^2), the total amount of carbon sequestered by the roof which was found to be around 1.5kg/m^2 or 0.3 lb/ft^2, and the labor hours (around 1/2 hr per SF). After setting up the inputs, both variable and constant, I produced the geometry for the roof system as seen below:

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I then set up the evaluators that could be calculated including the total cost for the plant trays, the total carbon sequestered, and the total assembly time. The volume and surface area of the roof were intermediate steps to get to these evaluators. These evaluators trade off with one another with some desired minimums and some desired maximums. Minimized cost for the green roof is desired but that may mean less area of the roof which would sequester less carbon, which is not favorable. An easy assembly time is desired but this may mean less time went into the modular staging of the plant trays and less carbon will be sequestered. A low fabrication cost may be traded with time to assemble.

I then produced outputs to watch the values for such evaluators. A full screenshot of my dynamo study graph can be seen below:

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3. Create and run a Generative Design Study using the Study Graph:

I ran the Green Roof Optimization Generative Design Scheme with the following inputs and outputs.

Create Study in Generative Design for Optimization of Green Roof
Create Study in Generative Design for Optimization of Green Roof
Inputs and Outputs for Generative Study
Inputs and Outputs for Generative Study

After generating several design options, I was able to sort through several different combinations of the inputs which varied the three input parameters that I left as variables.

  • Screenshot of Generative Design Results
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  • Explanation of Generative Design Results

Ultimately from the scatterplot, there are several conclusions I can make about the design process of this green roof. Assembly Time (Hrs) vs. Installation Costs ($) is being plotted with the evaluator of sequestered carbon as the size of the dots themselves. The colors correlate to the number of plant trays between 4 and 50 that are to be installed. There is a clear tradeoff here between cost and time as well as carbon sequestered. From the plot, more carbon is being sequestered (larger dots) but for a green roof with longer assembly times and at the sacrifice of higher costs. This tradeoff takes into consideration human bias and also what is prioritized in the design. For example, if this was for a project that had endless money and wanted simply a green building, then I would choose the design that maximizes the carbon sequestered by the plant trays. With this provided information, I chose the highlighted design to be seemingly optimal because it forms a nice middle ground. The amount of carbon sequestered is not as the maximum I would desire, however the installation costs are more reasonable and the assembly time is a lot less than for some of the other designs.

Sources used for Green Roof Research: