1 day late pass used
2 Units: Create Two New Evaluator Nodes
Evaluator Node 1: Sunset View Index (SVI)
Especially for high-rise buildings, potential tenants will often prioritize the angle of a potential unit towards where sunsets may be. Given that we are evaluating a twisting building form, it will be important to evaluate the structure for how many of these units have a good view of sunsets; more units with a good view means more money for the developer.
I implemented this custom node by first choosing a range of dates and times over which to test. I opted to do a test of sunsets in the winter months, as this is when most people will be outside in their building and enjoying the sunsets; climate during the summer seasons is far too hot for people to be outside and enjoy it. I set the range of times to be during 4PM-6PM, as this is the “golden hour” window. Then, using the directness custom node as a template, I modified it so my custom node calculates the directness between the structure and the sunset during these times. I changed the subdivisions of the surfaces so that the v divisions resulted in 1 per floor, and I chose to divide the u by about 10 divisions, or approximately the dimensions of one standard unit. This way, I could take the normal as a simulation of what the view of the sunset might be like. The end product of this was a singular score of the SVI for any particular configuration; a higher value meaning that that particular configuration had better sunset view potential, which could inform a developer’s choice since they might want the alternative that has better overall view potential.
I created two nodes to do this; one called PanelsComputeDirectnessToSunset, and one called BuildingFormEvaluateSVIwithTwoInputs.
My results are as follows:
Evaluator Node 2: Slab Construction Cost Index (SCCI)
High-rise buildings like this typically use post-tensioned decking for their floor plate system. However, not only do the cost of these systems add up, construction cost gets exponentially higher as you go up the building, primarily due to the increased difficulty of construction. This metric aims to estimate this cost.
For this purpose, I created BuildingFormEvaluateSlabCostTwoInputs, which, as the name implies, calculates the estimated cost of the concrete slab at each floor. It takes in a specified slab thickness (I opted for 8”, but this is a parameter that can be changed) as well as a concrete cost per cubic yard (It assumes a standard cost of $130/CY, but is also parametric). It then calculates the cost per square foot, and assumes a 1.0 multiplier at the base, and a 1.3 multiplier at the top floor (i.e. a 30% surcharge at the top compared to the bottom).
My results are as follows:
3 Units:
For this portion, the scheme that I developed engineers a weighted scheme of all the metrics based on what a developer might find most important. My scheme is as follows:
30% SCCI + 25% SVI + 20% GSF + 15% GSA + 10% GV
For metrics we want to MAXIMIZE (GSF, Volume, Sunset Index), we normalize the values such that the top-performing case is a 1.0 and the worst case a 0.0. For metrics we want to MINIMIZE (Surface Area, Structural Cost), we flip the math so that the cheapest, most efficient structure gets a 1.0, and the most expensive gets a 0.0.
My idea with this approach is that it allows anyone, at glance, to clearly understand what values are the worst (0) and what are the best (1). This is important because the score needs to be translatable as well as easy to understand.
Point to Ponder: What overall strategy do you feel best captures the relationship between the evaluation metrics?
I think it makes sense to do a weighted average like this, as it allows the evaluator to think about what they prioritize the most in terms of each individual characteristic. It allows them to develop a single score that is easy to present to others, as it is normalized and a scale from 0-1 means something to those who aren’t as familiar with the building.
I opted to implement this logic in Excel, as this was the most practical environment in which to analyze my results.
My results are as follows:
As seen in the results image, the best results are ones with a shorter mid height, and a 60 degree mid rotation. This is likely because more extreme rotation results in more warped shapes that are higher to construct and more polarizing in terms of which units have most attractive views.
Point 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 best alternative is the mid height = 400 ft and 60 degrees mid rotation. This alternative ended up at the top of the list because it had the highest normalized score. I consider it to be the best option because, as my original explanation of my developed scheme implied, it has the best balance of all the characteristics that I want a building to have for the purposes of developing a new site. Obviously there are some nuances that get lost when you are developing a metric like this - reducing a building to nothing more than quantitative statistics can be disadvantageous, as it loses the human aspect that you get from looking at a building and deciding if it looks pleasing or not. A more ideal scenario would be to use these metrics as a tool and not as the singular mechanism through which to make a decision.