Lauren Kercheval

Selection of building forms produced in this Generative Design Study:

image

Step 1 - Generative Design Framework

Below is a list of various three building design decisions and their variables, evaluators, and most important tradeoffs:

Design Decision 1: Building form

  • Design variables:
    • Building height
    • Building footprint
  • Evaluators:
    • Cost to construct
    • Potential revenue
    • Payback period
    • Worker safety
    • User views
    • Modularity potential
    • Daylight potential
    • Operational costs
    • Maintenance costs
    • User wellbeing
  • Tradeoffs:
    • Constructing more floorspace will cost more but also has the potential to earn additional revenue
    • Increasing the number of number of stories creates more daylight potential and good views at the higher stories, but it will cost more to run and maintain the building

Design Decision 2: Building orientation

  • Design variables:
    • Rotation of structure on site
    • Location of structure on site
  • Evaluators:
    • Solar radiation
    • Operational costs
    • Excavation costs
    • Proximity to roads
    • Proximity to places of interest
    • Daily energy usage requirements
  • Tradeoffs:
    • A building that gets lots of sunlight will require more energy to cool the interior
    • A building right in the heart of town might be a nightmare to create a construction plan if the site is not large enough for trucks to turn around or for housing equipment at the end of the day
    • The location with the least excavation cost may be at a less-desirable end of the site

Design Decision 3: Structural material

  • Design variables:
    • Material selection
    • Material producers
    • Gravity system design
    • Lateral system design
    • Project site location
  • Evaluators:
    • Cost to construct
    • Time to construct
    • Ability to achieve architectural intent
    • Carbon dioxide output
  • Tradeoffs:
    • The architect’s vision might challenge the ability to minimize construction costs
    • Materials that embed carbon might not be locally accessible

Step 2 - Generative Design Study

In this Module, I conducted a Generative Design Study with Design Decision 1: Building form. The design variables alter the building’s form by altering its footprint (maximum width and number of polygon sides) and height. Other variables, listed below, are held constant throughout the analysis. These values were obtained from research of existing market conditions. The Generative Design Study reports and compares the Wellness Potential, Payback Period, and Modularity Potential resulting from each building form.

image

To produce these outputs, first a solid is constructed by lofting three geometric shape profiles. The dynamic inputs control the size of these profiles and the vertical locations to which they are lofted.

image

The solid is evaluated by isolating the surfaces and recording the measures of interest: surface areas and the total volume. Note that at this point, the color in Dynamo can also be selected for the roof and sides.

image

The desired outputs are then computed using the recorded solid geometry measures. The wellness potential compares the interior volume to the wall surface area, implying that the more windows a structure has, the more likely building users are to benefit from the view of the outdoors. Payback period is computed by estimating costs and revenues of each building form. Modularity potential creates a metric for constructing the building out of a uniform kit of parts. This is estimated by dividing the building’s total volume by the number of sides of the lofted polygons to represent the number of connections that would be required to construct the shape. A lower number of sides would result in a greater modularity potential.

image

Step 3 - Generative Design Study Results

Upon completing the Generative Design Study, an interesting pattern is revealed. Below, I’ve included the resulting scatterplot from the Study. The Y-Axis plots the Payback Period, which the client aims to minimize. The building’s Modularity Potential is plotted on the X-Axis, which is to be maximized. The Wellness Potential of the building forms is represented by the color of the data points: blue is a high score and red is a low score. To visually compare how the number of polygon sides influences the ability to achieve each of these optimization metrics, the size of the points is proportional to the number of sides. As seen in the plot below, only two design alternatives achieve significantly higher Wellness Potential scores but to the detriment of the other metrics. The lower the number of sides, the greater Wellness Potential, in general. The buildings with greater numbers of sides have greater Modularity Potential, but not to the extent which I expected as some shapes with only three sides score relatively poorly on the Modularity Potential metric. For the most part, the Payback Period is less than two years for all building forms considered in this Study.

image

Given the information presented in the Scatterplot, I would recommend the building form at the node highlighted below. As it appears to be a good compromise of all three measures.

image

The recommendation corresponds to the building form illustrated below in an isometric view and the Parallel Coordinates Graph. If the client has any concerns about this design option, we could either choose another one of the alternatives or work to optimize this building form. For example, if the building developer wants to shorten the payback period, we could work to optimize the construction costs to reduce the overall cost of the building.

image
image

Below, reference an image of my Dynamo Study Graph to completely detail the nodes and connecting logic.

image