Stage 1: Create Two New Evaluator Nodes
To create new node logic to evaluate the building, I wanted to think about the metrics that would best illuminate performance according to the project brief’s goals.
From last week’s module, the code already evaluates floor area, surface area, their relationship, and volume. Of the other aspects of the brief, the construction cost and solar insolation were not encapsulated. I wanted to prioritize these areas as much as possible in my approach.
To evaluate construction cost, I first started working from the example code given as a resource. However, since I was working with the Dynamo graph custom building from Module 5’s stage 3, I needed to create my own logic to take in my inputs from there rather than a Revit template form. I did this by generating a list of indices corresponding to the level of each floor area. This way, I could assign each area a construction cost corresponding to its respective elevation (which is modeled by a linear relationship starting with $500/sf at the base to $1000/sf at 750’. This is similar to the example, but my logic flows differently to accommodate the differing structure of my inputs (rather than selecting elements within the Revit model).
Construction Cost Logic:
To account for solar insolation, I initially wanted to track how building rotation would impact solar directness based off of the logic we used in Module 4. However, I struggled to panelize the building with my current knowledge-level/skill in Dynamo. Since I was working from a custom building, Dynamo struggled to place U and V points on sharp edges and severe curves caused by the ellipse in the center of my building geometry.
I also tried to create logic that would compute the average distance from a window within the building. I ran into similar problems, though, of developing logic that would account for the building profile’s custom geometry without just spitting out the give radii of length/width of each floor’s footprint.
While experimenting with the window distance logic, I found that I could use the methodology to create floors in Dynamo to extrapolate the perimeters of each floor’s footprint. I thought about the ways this could tell us solar-relevant information about the building, and I thought about how it would shape the the material needed for sustainable, solar shading implementations on the building’s envelope to better protect users from direct sunlight in the bright Dubai skies.
Thus, I decided to create a node that would measure how changing perimeter contributes to embodied carbon of adding overhangs to floors. This would alert stakeholders to the tradeoff between adding an energy saving design feature and increasing the building’s carbon footprint by increasing the material necessary. To do so, I created the following built-in assumptions within my OverhangEmbodiedCarbon node:
- thickness of steel: 4 in
- density of steel: 490 lb/ft3 —> 0.22226 tonnes/ft3
- embodied carbon of steel: 2800 kgCO₂e/tonne.
Then, I used the total perimeter area and an additional input called an overhang ratio to assume which percentage of floors would install overhangs (which I assigned 50% for now). For such a large building, there could be tradeoffs between how many overhangs are installed vs. the benefits of increased shading. Although my code does not weigh all of these factors currently, it provides a rough overview of how changing parameters could optimize the given overhang assumptions, which can be used as a baseline for solar analysis of overhang performance.
Overhang Embodied Carbon Logic:
Using these nodes, the spreadsheet results below show how the design alternatives perform alongside the new cost and overhang embodied carbon metrics.
Report Spreadsheet Results:
Points to Ponder: Do the new evaluation metrics that you’ve designed capture the meaningful differences between the building form alternatives?
- I think that the cost metric is very meaningful in selecting design alternatives because it expresses the tradeoff between optimizing the floor:surface area ratio versus the increasing cost of a greater floor area constructed at higher elevations. In other words, a narrow building is good for minimizing surface area, but can be more expensive. I think the overhang embodied carbon metric is helpful specifically if the owners are considering this as an option to increase solar insolation. This also helps determine which geometry could minimize the embodied carbon to make this addition more attractive.
Points to Ponder: What other metrics would be useful to compute to help understand and make the case for which alternatives are truly better than others?
- I think that getting solar directness as well as size of shadows casted would be very helpful for both the consideration of the individual building outcomes as well as the impact on the site/site area. This would help decide what “value” is most important to add to the building. . . what would the tradeoff be between more shade vs potential higher cost?
Stage 2: Develop a Single-Objective Optimization Scheme
Points to Ponder: What overall strategy do you feel best captures the relationship between the evaluation metrics? Clearly articulating your design strategy is the key aspect of this task. Before you dive into implementing your scheme, briefly describe your thinking and strategy in a paragraph that outlines your thinking and approach.
To develop my single-objective optimization scheme, I first brain stormed the meaning/importance/targets behind each of my existing objectives.
Objective | Target | Level of Importance |
Gross Surface Area | minimize | High |
Gross Floor Area | stay within site limits (984’ wide x 328’ deep x 750’ tall), total of 2.5-3 million sf | Medium |
Floor Surface Area Ratio | high numerator / low denominator = want to maximize | High |
Gross Volume | maximize space while minimizing cost; mostly encapsulated by other metrics | Low |
Construction Cost | minimize; incentivized to have greater floor area at lower elevations of the building | High |
Overhang Embodied Carbon | overhang helps solar insolation, but increases carbon footprint. want to minimize footprint while still incorporating the feature | Medium |
To rank the importance of the score, I wanted to normalize the values based on the respective minimums and maximums of the alternatives. This would allow each of these measures to be on a scale of 0-1 so that they could be combined. To incorporate the varying levels of importance, I assigned the high importance metrics x2 and eliminated the metrics at low levels of importance (volume). Medium level metrics stayed at a x1 ranking (overhang embodied carbon). I also decided that, since the Floor Surface Area Ratio already weighed both respective metrics, that I would only include the ratio in the combined score. I also didn’t want to double count the importance of those metrics, even at the cost of eliminating some level of complexity.
Thus a maximum score (most desirable) for an alternative pre-weighting would be:
- Floor Surface Area = 1
- Construction Cost = 1 (since we want this to be minimized, I would use 1-the actual score)
- Overhang Embodied Carbon = 1 (since we want this to be minimized, I would use 1-the actual score)
- Total = 3
A weighted maximum score would be:
- Floor Surface Area = 1 x 2 = 2
- Construction Cost = 1 x 2 = 2 (since we want this to be minimized, I would use 1-the actual score)
- Overhang Embodied Carbon = 1 (since we want this to be minimized, I would use 1-the actual score)
- Total = 5
With this same logic, the minimum score (least desirable) would be 0. The code to execute this logic is shown below:
This resulted in the following scores:
Points to Ponder: What propelled the recommended alternative to the top of the list? Explain your reasoning -- include a brief analysis of why this alternative rose to the top of the list and why you consider it to be the best option. Are there important nuances or tradeoffs that got lost is the single evaluation?
The top scores are:
- #1) 3.49: Top Height 500’, Base Width 500’
- This alternative has the 2nd best floor:surface area ratio, the 2nd lowest overhang embodied carbon, and the 2nd lowest cost amongst the top 3. This demonstrates how the score accounts for performance across all categories, even if it isn’t the top performer in every category.
- #2) 3.413: Top Height 600’, Base Width 500’
- This alternative has the highest floor:surface area ratio, highest overhang embodied carbon, and highest construction cost of the top 3. This similarly shows the associated costs to the benefit of being the top floor:surface area ratio, demonstrating how multiple objectives are represented in the single metric.
- #3) 3.411: Top Height 400’, Base Width 500’
- Finally, this alternative has the lowest floor:surface area ratio, lowest overhang embodied carbon, and lowest construction cost. Although it has the lowest cost and overhang embodied carbon, it preforms the worst with floor area maximization/surface area minimization.
However, all 3 of these alternatives do not meet the minimum sf of 2.5 million (though they are very close), so I would be compelled to choose the next best preforming on the single-metric (3.25) optimization, which is the 700’ height and 500’ width design alternative (screenshot below).
Stage 3: Visualize the Recommended Alternative
In order to panelize my building, I needed to replace my middle ellipse with a rectangle that had roughly the same dimension. This is because Dynamo could not comprehend the intense curves caused by the complex geometric lofting.
Then, I applied adaptive panels that would respond to the sun settings for Dubai based on the sun directness, both with opening and color.
I visualized the inputs determined by my single-objective optimization results.
Ideal Design Alternative @ 9:15AM
Ideal Design Alternative @ 12:15PM
Ideal Design Alternative @ 3:15PM
