Adding this image here just for visualization purposes.

STEP 1: OUTLINE 3 DIFFERENT DESIGN DECISIONS USING THE GENERATIVE DESIGN FRAMEWORK

Design Decision #1: Building Form Design and Placement on Site

Design Variables:

• Variable 1: Building Footprint Geometry
• Types: Rectangular, polygonal, cylindrical, irregular...
• Variable 2: Building Footprint Dimensions
• Unit of measure: assuming regular shapes, either measure length and width, or radius in feet
• Variable 3: Building Height
• Unit of measure: feet
• Variable 4: Building Orientation
• Unit of measure: degrees
• Variable 5: Building Position
• Unit of measure: Coordinates (latitude, longitude) or displacement from a defined origin point along x and y axes

Evaluators:

• Evaluator 1: Total Construction Cost
• Calculation: the sum of construction costs for all floors, considering the cost per square foot linearly increasing with increasing elevation.
• Evaluator 2: Cumulative Solar Insolation
• Calculation: the total amount of solar radiation (insolation) received by a surface over a specific period.
• Evaluator 3: Total Surface Area (Walls + Roof)
• Calculation: the total surface area of the building envelope. This comprehends walls and roof, but excludes the ground floor.
• Evaluator 4: Number of Sightlines (Rays)
• Calculation: maximize the number of sightlines (rays) that intersect the building from the designated viewpoint, considering fixed surrounding buildings that obstruct views.

Most Important Tradeoffs to Consider:

• Tradeoff 1: Cumulative Solar Insolation vs. Total Surface Area
• Increasing the total surface area of the building envelope, can increase the total cumulative solar insolation value by providing more surface area for solar radiation to be captured.
• Tradeoff 2: Construction Cost vs Performance
• Achieving better performance in terms of factors like solar insolation and view maximization usually comes with higher construction costs.
• Tradeoff 3: Building Height & Shape Complexity vs Performance
• Simpler geometries and lower heights generally reduce costs and surface areas but on the other hand reduce solar gain and minimize views.
• Tradeoff 4: Building Orientation vs. Energy Performance
• Optimal orientation can maximize solar gain and reduce energy consumption but might conflict with site constraints (building location).

Design Decision #2: Building Envelope Design

Design Variables:

• Envelope insulation properties
• Calculation: first, determine the types of all materials in the building envelope (e.g., exterior cladding, sheathing, insulation, interior finishes). Then measure the thickness of each layer. Finally find the R-value per inch for each material and multiply by its thickness. Add up the R-values of all layers to get the total R-value.
• Unit of measure: R-value (ft²·°F·hr/BTU)
• Window Characteristics - Window Type:
• Unit of Measure: type description (e.g., single-pane, double-pane, triple-pane)
• Window Characteristics - Window-to-Wall Ratio:
• Calculation: divide the total window area by the total wall area, then multiply by 100
• Unit of Measure: Percentage (%)
• Window Characteristics - Window Insulation:
• Calculation: get it from manufacturer specifications
• Unit of Measure: U-factor (BTU/hr·ft²·°F)

Evaluators:

• Energy Consumption
• Calculation: calculate the total energy consumption of the building using energy simulation software (for example, EnergyPlus, eQuest).
• Unit of measure: BTUs/year
• Thermal Comfort
• Calculation: measure the temperature distribution within the building and the percentage of time that indoor temperatures fall within a specified comfort range (e.g., 68-72°F).
• Unit of measure: Percentage of time within comfort range (%)
• Daylighting Quality
• Calculation: assess the amount of natural light in the building using daylighting simulation tools (e.g., Radiance).
• Unit of measure: average illuminance (lux)
• Envelope Materials Cost
• Calculation: estimate the total cost of materials for the building envelope using cost estimation software or industry benchmarks.
• Unit of Measure: USD

Design Decision #3: Structural System Selection

Design Variables:

• Material Type
• Unit of Measure: Material description (e.g., steel grade, concrete mix, wood type)
• Beam and Column Sizes
• Unit of Measure: Cross-sectional dimensions (inches)
• Floor-to-Floor Height
• Unit of Measure: Height (feet)
• Structural Grid Spacing
• Measurement Method: Measure the spacing between columns and beams in the structural grid.
• Unit of Measure: Spacing (feet)

Evaluators:

• Structural Integrity
• Calculation: perform structural analysis using software (e.g., SAP2000, ETABS) to ensure the system can withstand expected loads.
• Unit of measure: Safety factor or stress/strain ratios
• Material Cost
• Calculation: calculate the total cost of materials based on quantities and unit prices.
• Unit of measure: USD
• Construction Time
• Calculation: estimate the total construction time required for the structural system.
• Unit of Measure: time (hours)
• Embodied Carbon
• Calculation: calculate the total carbon emissions associated with the production and transportation of structural materials.
• Unit of Measure: kg CO2e (carbon dioxide equivalent)

Most Important Tradeoffs to Consider:

• Material Cost vs. Structural Integrity
• Using higher strength materials will improve structural performance but may increase the bill for purchased materials because high-strength materials such as steel are often more expensive due to their manufacturing processes and raw material costs.
• Construction Time vs. Material Cost
• Using prefabricated materials like steel can reduce construction time compared to on-site cast concrete because prefabrication allows for parallel processing and faster assembly. However, this can increase material costs due to the additional expense of prefabrication processes and transportation.
• Embodied Carbon vs. Structural Integrity
• Selecting low-carbon materials like timber reduces environmental impact but may compromise structural strength compared to steel or concrete because timber generally has lower load-bearing capacity and durability, requiring careful engineering and possibly additional reinforcement for equivalent performance.
• Construction Time vs. Construction Cost
• Reducing construction time often increases costs due to the need for accelerated schedules, overtime labor, or expedited delivery of materials.
• Conversely, extending construction timelines may lead to lower costs but can result in delayed project completion, potential financial penalties, and increased carrying costs.
• Note that higher floor-to-floor heights can increase material use and construction time.

STEP 2 - GENERATIVE DESIGN STUDY

For this second step I have decided to pursue the first option where I have defined my opimization problem as follows:

To achieve this, I have first created an environment for my dynamo simulations:

• I created a limit area as a square of 300 ft x 300 ft (signed as a red square in my design). This will serve as a site constraint where I can place my building.
• I have created my building as a prism with a polygonal footprint (polygon inscribed in a circle). It is colored in blue in my project. I have set the maximum width/diameter of the prism as 200 ft (inspired from the square plan of the One World Trade Center, which has a square plan for its base measuring 200 ft on each side). I have also set the maximum building height as 120 ft.
• I have created three buildings to represent the obstacles between the scenic viewpoint and my building. These are represented in yellow and their position is fixed right outside the limit area.
• I have created a series of vertical points to represent the scenic view. This could be a beautiful landscape, like the ocean. The maximum height of the last point is equal to the maximum height of the building, which is 120 ft. Their (x, y) coordinate is fixed, and it is in front of the three buildings.

The initial environment is depicted below. Please note that the blue building (my proposed design) is just one random (not optimal yet) representation of the various shapes the final structure can take.

I have defined the following changeable inputs:

• The building height ranging from 20ft to 120ft.
• The building footprint radius that can vary from 50ft (diameter 100ft) to 100ft (diameter 200ft).
• The building footprint number of sides that can vary from 3 to 8
• The building orientation, from 0 to 360 degrees
• The building x and y position. Note these coordinates were constrained so that the largest building footprint (200ftx200ft) would still be within the red limit area.

Then, I have implemented the 4 Optimization Objectives as follows. Note that these objectives correspond to the four outputs of my dynamo code.

This was implemented by first calculating the rays departing from the black points that do not intersect the three yellow buildings / obstacles. Using the previous example, these rays would look like:

Note that from each viewpoint (represented as black dots), there is a specific amount of rays spreading radially on a plane parallel to the XY plane.

I show below another perspective. You can see how the middle building partially obstructs the view from the viewpoints to my building.

Finally I have counted these rays to produce the output.

The corresponding dynamo code is illustrated below:

This was calculated by summing the construction costs for all floors after taking into account the varying costs per square foot at different elevations and the respective areas of each floor. I calculated it as follows:

1. I determined the construction cost per square foot for each floor. The cost per square foot grows linearly from $500 per square foot at ground level to $1000 per square foot at 120 feet (maximum height) above ground. I calculated the cost per square foot at a given level as:
$$\text{CPSF}_\text{level} = \text{CPSF}_{0} + \left( \text{Elevation} \times \frac{\text{CPSF}_{120} - \text{CPSF}_{0}}{\text{MaxElevation}} \right)$$

Where:

1. CPSF_level: Cost per square foot at the given level ($/SF)
2. GPSF_0: Cost per square foot at the ground level at 0 feet ($/SF) = 500 $/SF
3. GPSF_120: Cost per square foot at the max elevation of 120 feet ($/SF) = 1000 $/SF
4. MaxElevation: Maximum specified elevation (Feet) = 120 ft
5. Elevation: Height in feet at which I want to calculate the cost (ft)

Solving the Equation:

$$\text{CPSF}_\text{level} = 500\$/SF + \left( \text{Elevation} \times \frac{1000\$/SF - 500\$/SF}{120 \text{ ft}} \right)$$
2. I calculated the total construction cost for each floor. I have multiplied the area of each floor by the corresponding cost per square foot.
3. Then I summed the construction costs for all floors.

The corresponding dynamo code is illustrated below:

This refers to the total amount of solar radiation (insolation) received by a surface over a specific period.

The corresponding dynamo code is illustrated below:

This is calculated by summing up the surface areas of the building envelope of my building. This comprehends walls and roof, but excludes the ground floor.

The corresponding dynamo code is illustrated below:

Step 3 - Generative Design Study Results

To optimize my building design, I conducted two studies using Dynamo Generative Design.

• The first study used the "Optimize" option to find the best solution balancing all objectives: maximizing view, minimizing construction cost, maximizing solar exposure, and minimizing surface area. For visualization purposes, I applied filters (shown in grey) to the parallel coordinate graph to align with the four optimization objectives mentioned earlier. For instance, to maximize the ray count, I selected only the upper range, from 200 to approximately 350 rays. This filtering helps to focus on the most relevant data, highlighting the solutions that best meet the optimization criteria. The resulting parallel coordinates graph is displayed below (filters applied). With this filtering, I have highlighted two potential solutions (the two blue lines in the graph below).


The corresponding Scatter Plot is displayed below (no filters applied). For better visualization, I have not visualized the points by size - total surface area.


• The second study used the "Distribute Evenly" option to generate a diverse set of solutions across the design space. This approach provided a comprehensive understanding of feasible designs and their performance, helping to identify the most effective configuration. The resulting parallel coordinates graph is displayed below (filters applied):


The resulting Scatter Plot is displayed below (no filters applied). For better visualization, I have not visualized the points by size - total surface area.

From the graphs above, it is clear that the "Distribute Evenly" option explores the design possibilities more thoroughly. This method generates a wide range of solutions across the entire design space, capturing various combinations of building height, footprint radius, number of sides, orientation, and position. It provides a comprehensive view of all feasible options, helping to identify optimal solutions and understand trade-offs.

For this reason, I will analyze the results obtained with the “Distribute Evenly” option.

Tradeoffs that can be noticed from the previous scatter plot are the following:

• Bigger building shapes (larger radius, higher number of polygon sides, larger height) can increases the total construction costs.
• Taller buildings can obstruct less of the view from lower elevations but come with higher costs.
• Buildings with more sides (e.g., a polygon with 8 sides vs. a triangle) can provide better views and potentially more surface area for solar exposure but increase construction complexity and cost.
• Positioning the building between existing structures towards the scenic viewpoint within the 300ft x 300ft limit area can maximize the view, but this strategic placement may compromise opportunities for optimizing solar exposure.

Based on my analysis, all inputs seem to be valuable, however, building orientation can be constrained. In the case of regular prisms with polygonal footprints, changing the orientation adds unnecessary complexity and provides minimal benefit due to the symmetrical nature of these shapes. Hence I suggest constraining the orientation, for example to 0 degrees, to simplify the optimization process while still achieving effective design outcomes.

Filter #1 - fixing the building orientation at 0:

This image shows how by constraining the building orientation at 0, all design outcomes remain still accessible.

Filter #2 - keeping only the upper half of total cumulative insolation to maximize it:

This image shows that high sun exposure can be achieved only with larger building sizes. Hence, with this filter, all the options with small footprint radii (e.g. 50ft) are excluded.

Filter #3 - maximize rays count, minimize total construction cost, and total area

With these last filters, I have selected the inputs for the optimal design (bold blue line above): radius 100 ft, building orientation zero degrees, triangular shape, height 100ft, x position 50 ft, y position -50 ft as a Pareto optimal decision and a compromise between the four optimization objectives.

Below there is an illustration of:

• Some of the 5250 design options explored in this study (top-left part of the image)
• The scatter plot (bottom-left part of the image) representing these design options in terms of:
• Total construction cost (x axis)
• Total cumulative solar insolation (y axis)
• Rays count (size)
• Total surface area (color)

Highlighted in the scatter plot there is the selected best design choice

• The selected best design choice, its outputs, and input variables (right part of the image):

Given the inputs for the optimal design (radius 100 ft, triangular shape, height 100 ft, x position 50 ft, y position -50 ft), I explain the objectives and tradeoffs that impact the design decision as follows:

• Maximizing the view:
• Height: A 100 ft height ensures a great view above obstacles or nearby buildings compared to lower heights where my building would be shielded by the three buildings in front.
• Position: The specific position (x=50 ft, y=-50 ft) is ideal for the building to have clear sightlines. This is because at that position my building is placed behind and between the left and middle obstacle buildings.
• Footprint Size (Radius): A larger radius can enhance the view by providing more vantage points. However, it must be balanced with other factors like construction cost and solar exposure.
• Minimizing the construction cost:
• Footprint Shape and Size (Radius): The triangular shape can reduce material costs compared to more complex shapes while keeping a fairly large radius. A large radius (100 ft) may can increase construction costs but it is necessary to have a high value of cumulative insolation.
• Height: While beneficial for maximizing the amount of rays (enhancing the view), the 100 ft height increases construction costs.
• Minimizing Total Surface Area
• Footprint Shape: The triangular shape helps minimize the total surface area even when using a large footprint radius.
• Height and Footprint Size (Radius): Increasing height and radius generally increases surface area. Balancing these dimensions is necessary to keep the surface area minimal while achieving other the goals like maximizing the sun exposure.
• Maximizing the Cumulative Solar Insolation
• Height: A height of 100 ft helps minimize shading from nearby structures, allowing for higher solar exposure on the building surfaces.
• Radius: A larger radius provides more surface area exposed to sunlight, increasing the building's cumulative solar insolation.
• Shape: The triangular shape may not be the best to maximizes the potential surface area for solar panels. However, other tradeoffs were considered and the triangle was considered the best choice when combined with the other selected inputs.

Finally, here is a picture of my Dynamo Code. Please open the corresponding file in Dynamo, to be able to properly read each node.

And a corresponding explanation…