An overview of the design decision I chose to study and the process through which I reached this decision can be seen through the steps below.
Step 1 - Generative Design Framework
Starting off, we will take a look at the 3 different design frameworks that I explored:
- The placement and form of a multi-towered building in a dense city environment
- Design Variables
- The x and y placement of multiple rectangular towers (say 3)
- The height of these rectangular towers
- Evaluators
- Maximizing the amount of direct line of sight to a desired point (say a lake front) from the building
- Maximizing the functional volume of the building
- Maximize SA:V ratio
- Minimizing the amount of cost for insulation required
- Most Important Tradeoffs to Consider
- Maximizing Direct Line of Sight
- To maximize direct line of sight we will want more exterior surface area that is not blocked by other buildings, however this also means more surface that is not shaded by surrounding buildings which leads to higher cost of insulation.
- Maximize Functional Volume vs Maximizing SA:V Ratio
- These two evaluators are inversely related. If we increase the functional space we will reduce the ratio of SA:V which reduces the percentage of the floor space that has good views to the exterior.
- Minimizing Cost for Insulation
- If we want to reduce cost for insulation, we will need to reduce the SA. This means that we will have a smaller SA:V ratio, which also could correlate to a reduction in the direct line of sight to the desired view.
- Considering the design of optimized column design and for an open rectangular office building floor plate
- Design Variables
- Number of columns in the X-direction
- Number of columns in the Y-direction
- Concrete Strength
- Evaluators
- Maximizing the open space between column grid (x span + y span)
- Maximizing the uniformity of the grid (or minimizing the difference between the x and y spans)
- Minimizing the percentage of room occupied by columns
- Minimizing the construction costs (for simplicity cost can be related to the amount of concrete used for the columns)
- Minimizing force carried by columns (increase in structural redundancy)
- Most Important Tradeoffs to Consider
- Maximizing open space
- To increase the open space between columns we directly increase the force that is carried in the columns. There also is can be a correlation between the uniformity of the grid. If we increase open space, we also reduce percentage of of column space occupying the room and potential impacts on construction costs, which will be explained.
- Minimizing percentage of room occupied by columns
- This directly relates to the total volume of the columns. Since the volume of the columns will be reduced, this will impact the construction costs (less volume less cost).
- Minimizing Construction Cost
- To minimize costs, we want the least volume of concrete using the lowest strength concrete as it is cheaper. Lower strength mean lower allowable force that can be carried by the column.
- Minimizing Force
- To minimize force, we want a denser column spacing which negatively impact open space. Additionally, the spacing of the columns linked with the strength of the concrete impact the total volume of concrete required. Which impact construction costs and the percentage of the room occupied by columns.
- The design of glazing and shading system for a standard office
- Design Variables
- Size of window (as a percent of window area to wall area)
- U-value of the window
- Length of extension of an exterior shading element
- Evaluators
- Maximizing views from the office
- Maximize daylight in office space
- Minimizing energy consumption for heating and cooling (due to heat transfer)
- Minimizing construction costs
- Most Important Tradeoffs to Consider
- Maximize Views
- To maximize views, we would want larger windows. Good views also mean better daylighting for the office space. However, this also impacts the energy consumption of the room. If we have larger windows, likely we will have more heat transfer and more energy consumption. However, energy consumption also depends on the U-value of the window and extension of shading element.
- Maximize Daylighting
- This can be done through a bigger window and a smaller shading element. Which impacts heat transfer in the room negatively and has an impact on construction costs (bigger window more cost, smaller shading element decreased cost).
- Minimize Energy Consumption
- This can be changed by the factors explained in the point above as well as changes in the U-value of the window. If we increase the U-value we will have less heat transfer, however, this negatively affects construction costs as U-value increases so do construction costs.
- Minimize Construction Costs
- The trade-offs for cost are as described above.
Step 2 - Generative Design Study
For my generative design study, I decided to explore my second design decision: the selection of a column layout design for an open office space.
To describe the generative design tool created we will go through the different components of the detailed design framework (Objective, Model, Design Variables, Constant Variables, Evaluators, and Interpretation).
Objective:
The objective of this design tool is to help create an optimized column grid for an open office space. This optimization process, has many trade-offs relating to architectural, construction, and structural preferences. From an architectural perspective, we want a comfortable, open work space which requires an increase of open space between the grid layout, uniformity between the grids, as well as a reduction in the volume occupied by columns. From a construction standpoint, we want a reduction of the construction costs. Lastly, from a structural perspective it is important to have increased redundancy of columns by minimizing the amount of load being applied to each column.
Model:
To model this complex trade-off, the following script was created (Figure 1).
We will first need to model the base room geometry (Figure 2). The room will be created as a rectangle with the constant variables for room length and width. This rectangle will be shifted to have the corner at the 0-origin, patched to create a floor, and then made into a solid.
Then, the columns will be modeled as rectangular prisms that are placed on a grid that is determined by the users input of the number of columns to be placed in the x and y direction (Figure 3). To create the column grid, we will first create a list of x and y points based on the inputed number of columns in each direction and the room dimensions. These points will then be laced the together. Then, the points will be translated to up based on the room height, and rectangles will be created on each point.
The dimension of the column will be determined by the load placed on the column as well as the selected concrete compressive strength (Figure 4). The dimension of the column = sqrt(Force/Stress), a detailed look at these calculations can be seen through. The load that is placed on the column is determined by the tributary area of the column multiplied by the distributed load, which is passed in as a constant variable.
Based on these inputs, the user will be able to evaluate the trade-offs mentioned above. Now, we will take a closer look at the details of the design framework.
Design Variables:
This includes the input variables which can be altered by the user, as well as the constants (these are not changeable with the generative tool, but they can be updated if needed to fit your particular design situation.)
Input Variables (Figure 5)
- Number of columns in the X-direction: This value ranges from 6 to 17 columns, which is the range of columns that allow for reasonable spans.
- Number of columns in the Y-direction: This value ranges from 4 to 10 columns, which is the range of columns that allow for reasonable spans.
- Concrete Strength: This value ranges from 2000 to 6000 psi and steps by 500 psi.
Constant Variables (Figure 6)
- Room Length: The length of the room is set to 400 ft
- Room Width: The width of the room is set to 250 ft
- Factor of Safety: For the capacity of the compressive strength of the column we will design for a factor of 1.5
- Factored Load: This value was calculated based on a uniform DL of 80 PSF and a the Lord of 60 PSF, under a load combination of 1.2DL + 1.6LL. Additionally, we have set up this design tool for the bottom floor of a 10 floor office building. Therefore, the the distributed load is set to 1920 PSF (10 x (1.2DL + 1.6LL)).
- Lower Limit Cost: This value represents the lower cost of the low strength concrete based on average concrete costs of $125/yd^3.
- Upper Limit Cost: This value represents the upper cost of the high strength concrete based an estimated concrete costs of $175/yd^3.
Evaluators:
- Open Space Evaluator: The open space evaluator is calculated for simplicity as the addition of the x span length and y span length between columns (Figure 7).
- Uniformity Evaluator: The uniformity evaluator is calculated as the absolute value of the difference between the x span length and y span length between columns (Figure 8).
- Column Occupied Space Evaluator: The column occupied space evaluator is calculated by taking the ratio between the total volume of all the columns together over the volume of the room.
- Column Construction Cost Evaluator: The column construction cost evaluator is calculated by using linear interpolation between the lower and upper limit concrete costs (constant variables) and the strength of concrete selected (Figure). This is then multiplied by the total volume of concrete to determine the cost of concrete. (Figure).
- Column Force Evaluator: The column force evaluator is calculated based on the logic explained above in Figure 4 and the final value is re-displayed in Figure 11.
The final results from the evaluators are rounded and output to be used in the Revit Generative Design Tool (Figure 12).
Interpretation:
This section will follow in the next step.
Step 3 - Generative Design Study Results
For the generative study, the following variables and goals were selected (Figure 13). For this case, I have decided to ignore the evaluation of column to room ratio, focusing primarily on the other 4 evaluators and their trade-offs as seen in Figure 14.
Through the scatterplot, the first observation is the approximate parabolic trend between the cost of concrete used and the amount of open space between columns. On the two extremes of low cost and high cost, we see that in both cases we have a less desirable evaluation of the open space of the column grid. Therefore, to have a more desirable architectural space, we should focus on the lower central portion of the graph (to the left of the parabola peak).
In addition to the x-y relationship seen, we also can see a relationship between the two remaining evaluators. For size, we have the difference in spans, and for color, we have the force in the column. The smaller the point, the more uniformity we will have in our grid layout. As the colors move from red to blue, we have higher forces in our columns, which means less redundancy which is less ideal). Therefore, we should focus on smaller to medium points that are closer to a yellow color. It is also good to note that as open space increases so does force in the columns, which is what we would expect.
Based on the evaluation of this scatterplot, I would suggest that we select one of the options that fall in the region shown in Figure 15. Based on a collaborative effort between the architect, engineer, and construction manager, one of the points in this region should act as a starting point for them to select the optimal solution that fit each of their preferences.
Once again, the final dynamo script is shown below.
