Maddie Connelly - Module 7

As someone who passionate about the intersection of construction/sustainability/design, the most important design decisions I can think of from these perspectives include:

• Design Decision 1: Construction
• Design Variables
• Building rotation, Building dimensions (building height, mid profile height, number of floors, profile geometry), Site location
• Evaluators
• Construction cost
• Gross floor area/surface area
• Construction time
• Most Important Tradeoffs to Consider
• Construction cost v time (often times aligned, but sometimes faster schedule = higher cost)
• Gross floor area/surface area (maximizing size) vs construciton cost (minimizing cost)
• Design Decision 2: Sustainability
• Design Variables
• Cost of material, embodied carbon of material, gross quantity of variable needed (driven by gross floor area, surface area, building height, number of floors), building alignment (drives solar exposure)
• Evaluators
• Floor area and height for structural materials
• Use the above to calculate construction costs ($/sf) and embodied carbon (kg CO2/tonne of material)
• Roof area to maximize PV potential energy generation
• Solar insolation
• Most Important Tradeoffs to Consider
• Size v cost v embodied carbon (also driven by material choice)
• cost v solar insolation
• cost v PV potential
• Design Decision 3: Architecture
• Design Variables
• Building area, roof area, building alignment, material choice
• Evaluators
• Construction cost
• Building area
• Aesthetic appeal (more subjective and difficult to model objectively)
• Most Important Tradeoffs to Consider
• Mazimizing aesthetics v minimizing cost
• Maximizing size v minimizing cost

Since I am taking the class for 4 units, I designed a Generative Study with 4 inputs and 4 evaluators, combining construction and sustainability design decisions:

• Inputs:
• Building rotation
• Material
• Top geometry radius
• Building height
• Evaluators:
• Construction cost
• Surface envelope solar exposure
• Roof PV potential/payback period
• Embodied Carbon

I wanted to combine construction and sustainability considerations since these are the areas that I am most passionate about and I think that they will be driving the reality of the built environment as the challenges of climate change intensify the tradeoffs between their interests (scarcity in materials/energy while trying to invest in long-term, net positive designs).

To do so, I wanted to consider how underlying factors for both construciton and sustainability (building size, orientation, and material choice) could drive divergent (or convergent) outcomes for their shared/distinctive evaluators. For instance, increasing roof area will increase construction cost but also increase the potential to generate solar energy by allowing for more PV panels to be installed.

Inputs

Building rotation, height, and top geometry radius

This was relatively simple. I created sliders for each, and tested the bounds of logical options to make sure my designs made sense (i.e., making sure walls were continuous, roof was bounded).

First started modeling my materials as drop down boxes, but the Generative Study would not factor in those changes. I then adapted by making unique sliders for each material based on the values for single, double, and triple glazing. This did cause the study to consider unrealistic combinations (i.e., having single glazed cost with triple glazed benefits), but I just had to be mindful of those possibilities and narrow my ultimate choice to a scenario that had input values consistent across cost, embodied carbon, and solar heat gain reduction to a align with a singular material.

https://www.sciencedirect.com/science/article/pii/S2352710220336743?via%3Dihub

1.23 m × 1.48 m = 1.82 m^2 —> 19.6sf

Single: 22 kgCO2/19.6 sf = 1.12 kgCO2/sf

Double: 54 kgCO2/19.6 sf = 2.76 kgCO2/sf

Triple: 64 kgCO2/19.6 sf = 3.27 kgCO2/sf

Material: Cost of Glass

https://www.lancasterpaintandglass.com/custom-cut-glass-cost.php#:~:text=For common projects like single pane windows%2C,cuts required%2C the edgework%2C and other customizations.

Single: $7/sf

Double: $12/sf

Triple: $40/sf

Material: Solar Exposure

To model exposure, I ultimately took the average of the solar directness/solar heat gain reduction (decided by glazing) to get a rough proxy of how well the design could keep radiation out. Varying the glazing thus changed the Solar Heat Gain Reduction since more glazing = less radiation.

https://www.nature.com/articles/s41598-025-92600-w

Single: 0

Double: 20%

Triple: 30%

Evaluators

Some of these evaluators were relatively straight froward, such as construction cost and embodied carbon. However, even these were complicated by the fact that material choice had rippling downstream effects in their logic. For instance, choosing single glazed glass for the exterior presents new values for construction cost as well as embodied carbon.

It was more complex to attempt to model solar exposure and PV potential. I first tried to use the Ladybug/Honeybee extentions, but ran into issues with connecting my Dynamo graph rather than an element directly in Revit. I also tried the Solary Analysis custom node from the shared drive, but there were several unresolved issues with trying to process weather files/the Dynamo graph. Thus, I tried to increase the complexity of the measures with the skills that I’ve learned so far in the class.

Construction Cost

 For this evaluator, I used a basic assumption that each sf of floor area would cost $200. On top of this, I added the cost of the material * surface area (see inputs) used and the cost of solar installation.

I found that the average cost of solar installation in San Diego is $2.34/W.

https://www.energysage.com/local-data/solar-panel-cost/ca/san-diego-county/san-diego/

I then used this value to get a gross annual output of Watts to calculate what the installation cost would be, and then added it to the other totals.

Embodied Carbon

For embodied carbon, I multipled the kg CO2 eq rate for each respevtive material with the surface area of the design.

PV Potential

For the PV potential, I first tried to model it in terms of payback period since this is important for arguing the business case of sustainability. Thus, the node takes in the roof area from the inputs, and calculates the cost of installing PVs as well as the potential for annual energy generation. Then, it considers the cost of buying energy from the grid and divides the cost of installation by the $ saved annually to calculate a payback period. Originally I wanted to calculate solar potential using the previously mentioned methods, but considering the unresolved challenges, this was a way to make the evaluator less directly tied to the inputs themselves (i.e., increased roof area = increased solar potential). However, when I created a formula for this calculation, the simplified assumptions I was making created a constant payback period since the costs of installations and savings always were proportional.

Thus, I pivoted to look at maximum PV potential, which was calculated by multiplying the average PV output per sf of panels in San Diego (project location) by the total roof area (which is the maximum sf of panels possible).

5.26kWh/day*m2 —> 0.49kWh/day*ft2 —> 178.42kWh/year*sf.

https://www.solarenergylocal.com/states/california/san-diego/

This is when I moved the complexity of increasing solar capacity v increasing cost to the construction cost evaluator so that the tradeoff could still be considered, just not within the PV potential evaluator alone.

Solar Exposure

For solar exposure, I wanted to consider how the orientaiton of the building would interact with material choice and determine how much solar radiation the building’s envelope would recieve. Since I couldn’t calculate this directly (bc of challenges described above), I decided that I would use the solar directness vectors based on building orientation for the peak solar exposure (summer solstice, 12pm) and divide them based on the savings garnered by single v double v triple glazed glass for the exterior material.

The parallel coordinates graph shows how each of the designs performs on the desired outputs of cost, embodied carbon, PV potential, and envelope solar exposure. We can see key tradeoffs between embodied carbon and avg solar exposure, which is driven by these divergent behaviors within material choice (more glazing = less exposure but more embodied carbon). Further, there is a similar tradeoff between construction cost and annual solar energy generation since it costs more to create the roof area required to boost PV installation potential.

To narrow this down, I applied filters to show the lowest embodied carbon, cost, and avg solar exposure and the highest annual solar generation. I then evaluated the remaining designs to make sure they were logical (i.e., consistent input material qualities), and throught about which options had minimized tradeoffs (i.e., fewer costs for the greatest benefits). From this thought process, I think that this is the most ideal design:

In this case, it was relatively simple since only one of the narrowed options had a consistent range of material inputs (corresponding to triple glazing).