For 2 or More Units: Create Two New Evaluator Nodes
- For my two evaluator nodes, I decided to focus on both ‘sustainable design’ and ‘construction’ facets. However, I wanted to combine them into one custom node, along with the evaluating factors found in Module 5, to improve the user-interface and reduce the number of nodes they’d have to deal with. I also wanted to change two different parameters - the height of the building and the top depth, to ensure that I checked a variety of permutations.
- Node Feature 1: From a sustainability perspective, I wanted to find the solar insolation and determine how much power from solar panels could be gained given the surface area of the building and the solar insolation value. This was my first evaluator node. I let the user input the size of a solar panel, and used industry standards for solar panel losses (15%), efficiency (18%) and found cost using the provided size and industry standards ($0.70/W), to determine how much it would cost. I found the solar insolation, following logic shown in the examples, and then used that to find out how much solar energy could be generated.
- Node Feature 2: My second evaluating node was to determine construction costs, this is often a decisive factor in construction, and thus, important to capture. I used the given cost values per square foot to calculate these costs. I edited the given logic for mass floors and computing cost by floor levels in a new node. From there, I decided that the cost would only change linearly with the addition of the outer facade since they would be linearly related to the area of the floor: the changing size of the building is captured in the floor area and the change in cost with respect to building height is captured as well.
Combined Node:
Combined Node Logic
Solar Panel and Solar Energy logic:
Construction Cost Node Logic:
Evaluation Values - Summary Table (Excel)
I do believe that the evaluation metrics capture meaningful differences, again, cost is one of the most important factors when building a project, and both the values I’m flexing and the evaluation metric of construction cost capture many permutations to optimize cost between alternatives. Similarly, given the location in sunny Dubai, harnessing solar power is important for sustainable development and net-zero buildings (aligning with net-zero goals in the UAE and globally), thus, capturing not only how much solar power can be harnessed but also the initial cost of installing the panels is a good way to measure the difference in this capability amongst different alternatives.
Other metrics that could be useful would be more precise cost values, estimating the different materials separately given that the different designs would have nuanced cost differences for facades, concrete etc. Another metric that would be useful would be a structural analysis to determine which alternative mitigates risk from natural disasters such as earthquakes the best. Knowing the energy consumption of the different buildings would also be useful to know how the solar panels would be used, and if that much energy is even needed/consumed.
For 3 or More Units: Develop a Single-Objective Optimization Scheme
- My Single-Objective Optimization scheme focused on the following criteria:
- minimizing construction and solar panel costs
- maximizing solar energy gain (thus maximizing solar insolation)
- minimize surface area while maximizing volume (usable area)
- Meeting the project deliverables:
- Provide between 2,500,000 and 3,000,000 SF of new floor area.
- Stay within the site development limits:
- Up to 300m (984 feet) wide x 100 meters (328 feet) deep in plan view
- No taller than the site’s height limitation of 230 meters (755 feet)
- Since my script was exporting all the information to excel, I decided to use that capability to create my scheme. I followed the following evaluation process:
- I got rid of any values that didn’t reach the project deliverables in terms of square foot of floor area.
- From there, I normalized each value using the formula ((value - minimum)/maximum-minimum)), this assigned a value between 0 to 1 to each of the calculated values. As expected, all values increase as the size of the building increases since all the metrics used are directly related to the building size. However, the key point to note is that for some of these values, we want the maximum whereas for others, we want the minimum.
- I ranked the values (1st, 2nd, 3rd) based on their normalized value, choosing the minimum (0) value for values that needed to be minimized (construction cost, solar cost, surface area), and taking the maximum for those that needed to be maximized (solar gain, volume).
- I also ranked the different metrics in importance given the context of the project. For example, I deemed construction cost most important so it got a weight of 1, and the other four categories got an equal portion of that (1,0.8.0.6,0.4 and 0.2).
Using these weighted values, the following results were found, where the pairs of top choices are given in the format (height, top depth):
I think the best strategy is a combination of normalization and weighting. This math-based approach allows us to assign empirical value to non-empirical metrics. Normalizing values compares them against only each other rather than against other values, which may be infeasible, and weighing them allows for the project’s specific objectives or most pressing deliverables (often cost-related) to be prioritized. Normalizing them and keeping in mind that some values are best at a maximum (such as solar energy and volume), while others are at a minimum (surface area, solar cost, construction costs), allows for a strategy that fully captures the relationship between the metrics.
Top Design Alternatives
My top three design alternatives include the following flexed parameters…
1) 650 feet tall, 300 foot depth at top of building
2) 650 feet tall, 200 foot depth at top of building
3) 750 feet tall, 300 foot depth at top of building
…along with the following (unflexed) parameters:
The top design alternative for the building (650 feet tall, 300 foot depth) provides the lowest surface area, as requested by the project objectives, which leads to the second lowest overall construction cost and lowest solar panel cost, while providing the second highest solar energy values.As well, it meets all the other height and depth restrictions, and the floor area requirements.
The balance between cost and sustainability are what propelled the alternative to the top of the list. Using the weighing system ensured that values that were more important, such as cost, were given a higher weighting, and more consideration. However, by assigning values to other metrics as well, we found that the best alternative is not necessarily the cheapest option, since we have a more holistic understanding of all the different factors at play.
There are nuances that get lost, given that there are only a few metrics considered here, many other ones are not considered, and may be directly correlated with the values that are considered. For example, a reduced surface area may lead to less fenestrations/windows, which affects the way the building gets lights and heating and cooling. As well, although a taller building may be favored in this evaluation, it may not be favored by the neighboring buildings and tenants, may be at higher risk of structural failure in the event of an earthquake or other natural disaster, may have high maintenance costs that grow exponentially (not linearly as we had it) with higher levels.
- For 4 Units: Visualize the Recommended Alternative - Option 2
- Per the instruction, I used my building form node from Module 5 and exported it to Forma. Since I had made part 1 using conceptual masses, and this one using Dynamo, the forms are different, however the logic and parameters are similar.
- Challenges: It seems Forma interprets the input values as meters, per the Slack discussion, and so I changed the values from feet to meters in my inputs
Daylight Analysis
Wind Analysis
Solar Energy
