Stephanie Chang

• Option 1 - Sustainability
• Design Variables
• Insulation thickness
• Envelope area
• Roof area
• Building volume
• Building height
• Evaluators
• Surface-to-volume ratio (envelope area / building volume)
• Embodied carbon (kgCO2e): carbon present in material, transport, and construction
• Material mass (kg): weight of structure and envelope (acts as a proxy for material cost and the embodied impact)
• Operational energy use (kWh/m^2/year): determined by the building form, glazing, orientation, and climate
• Solar gain on façade: cumulative kWh that reaches the envelope
• Solar gain on roof: photovoltaic opportunity using the root footprint to determine impact on annual yield
• Capital costs
• Annual operating costs
• Payback period
• Most Important Tradeoffs to Consider
• Material cost vs. carbon footprint (higher material costs for renewable system can result in lower carbon footprint)
• Capital vs operating cost (higher capital investment for the renewable system can result in reduced long-term energy costs but higher upfront costs)
• Option 2 - Architectural Design
• Design Variables
• Building height
• Building base width
• Window area
• Total façade area
• Usable area
• Gross area
• Evaluators
• Slenderness ratio (height / base width)
• Glazing ratio (window area / total façade area)
• Floor plate efficiency (usable area / gross area)
• Most Important Tradeoffs to Consider
• Aesthetic design and appearance quality vs occupant comfort and engineering expenses
• Too slender of a building design, although highly unique and aesthetically creative, can increase expenses in engineering and may pose discomfort to occupants due to higher swaying or vibration of the structure.
• High glazing ratio can allow for more direct sunlight but can lead to higher expenses with heat gain
• Greater use of HVAC system may be needed for air conditioning if too much heat gain is present in the building due to high window area
• Materials cost vs. appearance quality
• View quality vs. shading
• Option 3 - Construction Planning
• Design Variables
• Daily work hours (hours/day) and construction crew size (number of workers)
• Material quality level (scale from 0 to 1)
• Construction method (fabrication off-site vs. on-site) which can be presented as a prefabrication level (%)
• Evaluators
• Total construction time (days): timeframe from groundbreaking to occupancy events
• Total construction costs ($): expenses contributed to materials, labor, equipment, finance
• Quality score: function of both material quality and schedule pressure
• Rework risk: function of both product quality and construction time compression
• Most Important Tradeoffs to Consider
• Building assembly time vs. fabrication cost
• Product quality vs. construction time vs. total construction cost
• Larger crew size reduces construction and assembly time but can result in increased costs
• Faster schedules (greater time compression) can result in reduced product quality and higher rework risk
• Higher material quality can result in improved performance but lead to higher construction attributed costs
• Option 4 - Structural Design
• Design Variables
• Member cross-sectional area
• Span length
• Structural depth
• Material stiffness factor
• Evaluators
• Material cost (weight x unit cost)
• Structural efficiency (load capacity / weight)
• Structural weight (volume x density)
• Maximum deflection (span^3 / stiffness relationship)
• Most Important Tradeoffs to Consider
• Member weight vs. deformation or drift
• Larger members reduce deflection but can result in higher weight and costs
• Longer spans can reduce columns but result in greater deformation
• Greater stiffness can reduce deflection but result in higher costs
• Option 5 – Economic Architectural Design and Sustainability with Solar Renewable Energy
• Design Variables
• Scaling Factor of Top Ellipse
• Building Height
• Rotation Degree of Top Ellipse
• Rotation Degree of Middle Ellipse
• Evaluators
• Gross floor area created / Gross surface area of building envelope
• Average cost per square foot
• Total annual solar potential
• Solar efficiency
• Most Important Tradeoffs to Consider
• Greater solar potential and efficiency vs greater construction costs (due to higher building height)

Option 5 described in Step 1 is used in this Generative Design Study. I created a study graph with four design variables (study inputs) and four evaluators (study outputs) to explore the design decision with Generative Design. The four design variables are: Top Scaling Factor, Building Height, Rotation Degree of Top Ellipse, Rotation Degree of Middle Ellipse. The four evaluators that are calculated from the design variables and constants are: Gross floor area created / Gross surface area of building envelope, Average cost per square foot, Total annual solar potential, Solar efficiency. Regarding the four evaluators: Solar efficiency provides an assessment of having a building form with greater sustainable performance. Annual insolation potential accounts for the cumulative solar energy that reaches the building envelope annually. The envelope efficiency metric favors building compactness and is indicative of well-utilization of the façade. The average construction cost per square foot provides an economic assessment.  The study assumes that there is a linear growth in construction cost per square foot from $500/SF at ground level to $1000/SF at 750 ft above ground as well as climate and location in Dubai.

The primary objective for this Generative Design study is to maximize the Gross floor area created / Gross surface area of building envelope (also known as the envelope efficiency) using the single variable optimization strategy with Galapagos in Grasshopper Rhino. This evaluator was selected as the primary objective because it directly reflects building efficiency, thereby maximizing usable space while minimizing envelope surface, which is a key factor that impacts both construction cost and thermal performance. The node logic in the Grasshopper file does provide the option to perform Galapagos design study for each of the four evaluators.

The study graph in Grasshopper Rhino is demonstrated in Figure 1, capturing the inputs, calculations of metrics, and output evaluators assessed in this Generative Design Study. Upon generation of the design alternatives in the optimization study, the results were visualized by a collection of scatter plots, parallel coordinate graphs, and Pearson correlation plots as shown in Figures 2, 3, 4, 5, 6. The graphic of the optimized building structure for maximum envelope efficiency as shown in Figure 7. The study was repeated for optimized building structure for maximum solar efficiency as shown in Figure 8.

The scatter plot shown in Figure 2 displays design variables demonstrating the correlation between the primary objective evaluator, Gross floor area created / Gross surface area of building envelope, and the average cost per SF. Each data point represents one generated design option; the higher values on the y-axis demonstrate greater envelope-efficient massing (more usable floor area per unit envelope area), while the x-axis represents cost intensity. The color legend represents the building height to demonstrate how the height relates to the objective-cost relationship.

The scatter plot shown in Figure 3 compares the primary objective (Gross floor area created / Gross surface area of building envelope) against solar efficiency. Each data point is a generative design option; the plot highlights whether envelope-efficient solutions also achieve strong solar performance per unit area. The color legend corresponds to the Total Annual Insolation Potential [kWh], indicating how total captured solar energy varies across designs with different envelope efficiency and solar efficiency.

The scatter plot in Figure 4 illustrates the envelope efficiency versus total annual insolation potential. Each data point represents one design option tested through the Galapagos evaluation. The color legend corresponds to the average cost per SF, thereby allowing a three-way tradeoff evaluation among envelope efficiency (compactness), total solar energy potential, and cost.

The bar graph in Figure 5 demonstrates the Pearson correlation between the 4 design variables (Top Scaling Factor, Height, Rotation Degree of Top Ellipse, Rotation Degree of Middle Ellipse) and the primary evaluator that the Galapagos evaluation is optimizing (maximizing Gross floor area created / Gross surface area of building envelope). Positive values indicate design variables (inputs) that tend to increase envelope efficiency, while negative values indicate inputs that tend to reduce it, providing a first-pass indication of which variables have the strongest influence on the objective within the evaluated design space.

The parallel coordinates graph in Figure 6 compares all four evaluators simultaneously (normalized). Each polyline represents one design option. Solutions that remain high across multiple axes represent balanced designs, while lines that spike on one axis but dip on another may suggest strong tradeoffs (e.g., high insolation but high cost).

The scatter plots show how each generated design option performs relative to the primary objective and other evaluators, with each point representing a unique combination of design variables. In this study, a Galapagos single-objective optimization was performed to maximize gross floor area relative to envelope area. The plots illustrate that as envelope efficiency increases, there are corresponding tradeoffs with cost and solar performance, suggesting that improvements in compactness may lead to a reduction in solar exposure or increase costs depending on the design configuration.

Moreover, the parallel coordinates graph similarly shows how each optimized solution performs across all evaluators, highlighting that even with a single-objective optimization, the multiple design options achieve high envelope efficiency but differ in secondary metrics. This suggests that while Galapagos strategy identifies high-performing solutions for the primary objective, designers must still interpret tradeoffs among the cost and solar performance. This information would be used to select a final design in a further study from the high-performing solutions that best balances efficiency with acceptable cost and energy performance, rather than simply selecting the absolute maximum value of the objective.

Associated Files for Galapagos Design Study for Maximizing Envelope Efficiency:

• Grasshopper file: “4units_Stephanie Chang_Module7_Stage3.gh”
• Compiled inputs and outputs: “4units_Stephanie Chang_Module7_Stage3_Design Space for GH_max envelope efficiency_compiled inputs & outputs.xlsx”
• Excel writeout of input (design variables): “4units_Stephanie Chang_Module7_Stage3_Design Space for GH_input_max envelope efficiency.xlsx”
• Excel writeout of outputs & evaluation metrics: “4units_Stephanie Chang_Module7_Stage3_Design Space for GH_output_max envelope efficiency.xlsx”

Associated files for Galapagos single-variable based optimization studies for maximum solar efficiency:

• Grasshopper file: “4units_Stephanie Chang_Module7_Stage3.gh”
• Compiled inputs and outputs: “4units_Stephanie Chang_Module7_Stage3_Design Space for GH_max solar efficiency_compiled inputs & outputs.xlsx”
• Excel writeout of input (design variables): “4units_Stephanie Chang_Module7_Stage3_Design Space for GH_input_max solar efficiency.xlsx”
• Excel writeout of outputs & evaluation metrics: “4units_Stephanie Chang_Module7_Stage3_Design Space for GH_output_max solar efficiency.xlsx”