Stephanie Chang

Stage 1: Create Two New Evaluator Nodes

I designed three new metrics to evaluate the 12 building form alternatives. To recommend the most promising building form to the developer, it is important to account for both the performance and economic evaluation perspectives.

As shown in Figure 1, I created node logic in Grasshopper Rhino to compute these evaluation metrics for a single instance and then implemented Anemone start and end loops as well as additional node logic to support evaluation of multiple instances. Climate data was imported through EPW weather file and the analysis in Grasshopper was conducted for the location of Dubai. This allows for evaluation of the 12 building forms (with combinations of height and scaling factor of the top ellipse as varied inputs) and retain the evaluation values computed for each of these 12 test cases. The compiled inputs and outputs are shown in Table 1. Below are the attributed files for Stage 1:

• 4units_Stephanie Chang_Module6_Stage1_Design Space for GH.xlsx: compilation of input values
• 4units_Stephanie Chang_Module6_Stage1_Design Space for GH_output.xlsx: compilation of output values
• 4units_Stephanie Chang_Module6_Stage1_Summarized Inputs & Outputs.xlsx: compiled input and output values for varied building forms
• 4units_Stephanie Chang_Module6_Stage1.gh: Grasshopper file with node logic

Point to Ponder: In addition to the metric calculated in Module 5 (gross floor area created / gross surface area of building envelope), the new evaluation metrics computed in Module 6 (average cost per square foot, solar efficiency, and direct sun hour efficiency) capture meaningful differences between the 12 building form alternatives, in particular those driven by changes in the building height and top ellipse scaling factor.

Accounting for an evaluation metric that is determined by the geometry of the building form, the first new metric, average cost per square foot, is computed. As per the criteria that the construction cost per square foot grows linearly from ground level to 750 ft above ground, this indicates that the lower floors are less expensive. This economic-attributed metric is important because it accounts for the penalty of taller or heavily tapered designs. This is used as a minimization metric (lower cost is more favorable from an economic perspective).

There are two new primary performance metrics that allow for evaluation beyond simple geometry-based values; they capture how the building envelope interacts with the specified environment over a year.  The direct sun hour efficiency measures the average annual exposure duration that points across the building envelope receive direct sunlight. This metric complements the solar efficiency metric and gives indication of the relationship for solar energy concentration and exposure duration. A higher direct sun hour efficiency would suggest greater consistency for sun-exposed envelope surface of the building geometry. The direct sun hour efficiency is independent of the radiation intensity and is sensitive to effects like self-shading or tapering near the top of the tower. Shadowing patterns can result from geometric variations. The solar efficiency was calculated as the total annual insolation per total envelope area which results in the measured averaged annual solar energy incident per unit of the envelope area. This is an important metric that allows for normalization of the total solar potential by envelope size and accounts for how well the envelope is used rather than solely relying on the size of the envelope. Achieving a higher solar efficiency indicates that the envelope is favorably oriented and/or less self-shaded such that there can be greater potential to use for solar powered applications.

Moreover, examples of other metrics that would be useful to compute for a finer analysis on narrowing down the optimal building alternatives are: economic metrics (e.g., construction time, net present value), environmental metrics (e.g., embodied carbon, material mass and type, equipment lifespan, reusability of structural material, daylight autonomy), structural and performance metrics (e.g., lateral drift, envelop U-value), as well as comfort and human metrics (e.g., overheating hours, sellable views, thermal comfort, acoustic privacy).

Stage 2: Develop a Single-Objective Optimization Scheme

This single-objective optimization scheme was conducted using Microsoft Excel spreadsheet (file name: “4units_Stephanie Chang_Module6_Stage2”). The results of each building form’s test case are reported in Figure 2; below is a breakdown of the reported contents:

• Table 2: Weights as well as minimum and maximum values for each performance and economic evaluation metric
• Table 3: Compilation of inputs and normalized outputs for varied building forms
• Table 4: Single-objective score for varied building forms

Point to Ponder: The overall strategy to best capture the relationship between the evaluation metrics is to provide a weighted composite score for each building form (test case). This allows for prioritization of normalized, efficiency-based metrics rather than solely based on absolute quantities while simultaneously accounting for economic realism through construction costs. In the single-objective optimization scheme, I approached this by combining the four computed evaluation metrics for each test case into a single metric by first normalizing these output values for each evaluation metric of the test cases and then determining a composite score for each test case.

Having a higher value for the building performance evaluation metrics (gross floor area created / gross surface area of building envelope, solar efficiency, and direct sun hour efficiency) which are considered maximization metrics resulted in an improved (higher) score. By comparison, having a lower value for the economic evaluation metric (average cost per square foot) which is considered a minimization metric resulted in an improved (higher) score.

To reflect their relative importance, I assigned weights of 0.25,0.25, 0.3, 0.2 to the respective evaluation metrics: gross floor area created / gross surface area of building envelope, average cost per square foot, solar efficiency, and direct sun hour efficiency. Solar efficiency was assigned with the highest weight to reflect the objective to have a building form with greater sustainable performance. Direct sun hour efficiency captures both the exposure duration and self-shading effects that aren’t directly included in the energy metrics. The envelope efficiency metric is important to favor building compactness and well-utilization of the façade. The economic metric using average construction cost per square foot helps to ensure that the analysis is contextualized with economic realism and prevent excessively expensive geometric building forms.

Overall, these well-distributed weights allow for a composite score that balances building performance, compactness, and cost more effectively under the given constraints. The composite score is determined by the sum of individual products; each product pertains to the weight assigned to the evaluation metric and the normalized output value of the corresponding evaluation metric. This normalization and weighting scheme ensures that no single metric will unfairly dominate simply due to scale or units; this single-objective score allows for a thoughtful compromise among the presented metrics to determine a good baseline for a favorable building form.

Point to Ponder: From the top 3 recommended design alternatives highlighted in Table 4, the “best” recommended design has a composite score of 0.63, using a building height of 720 ft and scaling factor of the top ellipse of 0.6. This was the top ranked design alternative, demonstrating the greatest balanced performance across the 12 building forms evaluated. It allows for strong solar efficiency without requiring excessive envelope area, achieves good envelope efficiency (efficient use of façade area), and is economically reasonable for attributed construction cost penalties. A tradeoff however is that this building form has the second worst direct sun hour efficiency. By comparison, the lesser ranked designs may have higher façade surface area, floor area, or building volume, but these gains are offset by either higher costs or reduced envelope efficiency. More compact forms are more favorable from the economic standpoint but can lead to underperforming building designs for solar-attributed metrics.

It is important to consider that there are nuances and tradeoffs that are lost in this single evaluation, such as not accounting for material reusability and equipment lifespan. The distribution of the solar exposure (for instance at the roof versus façade) is also not directly addressed in the overall composite score. Another limitation is that the cost model is quite simplified based on the given assumptions 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. In reality, there are other economic factors to account for including construction time, net present value, cost per usable square foot, etc.

The evaluation presented allows for a good baseline recommendation on an appropriate building form in Dubai for an early-stage recommendation that can be further refined with additional assessment. The envelope efficiency accounts for good use of façade area, cost metric accounts for economic feasibility, solar efficiency accounts for energy potential, and the direct sun hour efficiency captures the exposure duration; each of these metrics addresses a different aspect of the design. The normalization of each metric allows for a fair comparison among the different test cases.

From the results shown in Figure 2, there are key interactions demonstrating tradeoffs between solar efficiency and direct sun hours, cost versus performance, and envelope efficiency versus direct sun hours. For instance, the solar efficiency peaks for a building height of 720 and top scaling factor of 0.6, but the direct sun hour efficiency is near the minimum value. The direct sun hour efficiency peaks at a building height of 700 ft and top scaling factor of 0.9 but the envelope efficiency is the worst and there is mediocre solar efficiency. Moreover, the taller building forms are penalized by growing construction costs. While the best score from an economic cost perspective occurs for a combination of the building height of 700 ft and top scaling factor of 0.9, we see the solar efficiency peaks near the 720 ft and 0.6 combination and envelope efficiency peaks near the 750 ft and 0.6 combination. Furthermore, from the dataset, the building forms with better envelope efficiency usually have lower direct sun hour efficiency. Accounting for the discussed tradeoffs, the proposed optimization strategy provides a weighted balance between performance, efficiency, and cost to compute the optimal combination with the highest-ranking composite score.

Stage 3: Visualize the Recommended Alternative

Using Grasshopper, I illustrated the best-case building form (building height: 720 ft, top scaling factor: 0.6) as shown in Figure 3. I used LunchBox Panel nodes to divide the wall surfaces into UV grids. To provide visual feedback, the panel color appearance was varied independently by (1) incident radiation and (2) direct sun hour; a color gradient scheme was implemented to reflect the corresponding values across the building form’s model geometry. As shown in Figure 3, the top region of the best-case building form has the highest incident radiation and direct sun hours. Mapping the panel color to these metrics helps to communicate how the building form performs differently. It provides a visual to better understand the parameter optimization study in the represented model geometry. The attributed files for this stage are as follows:

• 4units_Stephanie Chang_Module6_Stage3_Design Space for GH_output.xlsx: compilation of output values
• 4units_Stephanie Chang_Module6_Stage3.gh: node logic adapted for data visualization of the best case building form (height: 720 ft, top scaling factor: 0.6)

Creative Bonus:

• Designed three new metrics for evaluating the building forms to cover both building performance and economic perspectives (rather than the required minimum of 2 metrics): average cost per SF, solar efficiency, direct sun hour efficiency
• Refer to node logic captured in Figure 1 and single-objective score analysis accounting for the four total metrics in Figure 2
• Created visualizations for adaptive panel color analysis of both the incident radiation and direct sun hour for the evaluated building forms
• Refer to data visualization captured in Figure 3 for the best-case building form
• The data visualization is also built into the following file to illustrate the incident radiation and direct sun hour for all 12 alternative building forms: “4 units_Stephanie Chang_Module6_Stage1.gh”