Peisen Zhao

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Design goals:

• Construction time: Minimize
• Product Quality Score: Maximize
• Sustainability: Maximize

Parallel Coordinates Graph:

The Parallel Coordinates Graph below indicates multiple relationships among three metrics: the construction time, the product quality score evaluation, and sustainability score evaluation. There are also two input: the BaseRadius and Height of the building. More specifically, each line is showing a special design combination with different tradeoffs considered. By observing and filtering these lines, we are enable to select a couple of design options that would minimize the construction time, while maximize the product quality score and sustainability score.

1. First Design Tradeoff Combination: Construction Time vs. Product Quality Score

Below is a couple of observations from above graph:

• The plot does not show a clear linear correlation between Product Quality and Construction Time.
• For higher product quality (right side), construction time seems to cluster around moderate to low values, suggesting that better product quality does not necessarily require longer construction time.
• Since larger dots corresponds to longer construction time, most of the largest dots are located on the left side (lower product quality), and these also rise higher on the Y-axis. This would imply that long construction time is more often associated with lower product quality in this generative design output.
• Since blue dots corresponds to higher construction time, they are clustered in the top left, indicating low product quality with high construction time as a worst-case scenario.
• Green/yellow dots, which represent lower construction time, are scattered toward the middle and right side of the plot where product quality is moderate to high.

Based on above observations, here are some insights and design implications:

• There are options with long construction times and low product quality (top-left, large blue dots). These should likely be avoided.
• Several design options achieve high product quality with relatively low construction time (right side with smaller green/yellow dots). These are ideal candidates for selection.
• Some mid-range quality solutions (middle x-axis) offer a balance of moderate quality and time, which are worth considering because they are ideal.

Conclusion:

The generative design output suggests that longer construction time does not guarantee better product quality. In fact, some of the worst-quality designs require the most of time. Therefore, for this special design, it is necessary to focus on the smaller green/yellow dots to the right, which represent efficient design alternatives of high product quality with lower construction time.

2. Second Design Tradeoff Combination: Construction Time vs. Product Quality Score

Below is a couple of observations from above graph:

• There is a strong inverse relationship between sustainability score and construction time where designs with low sustainability scores (left side) tend to have very high construction times (upper left corner).
• As sustainability scores increase (moving right on the x-axis), construction times consistently decrease, clustering lower on the Y-axis.
• Since larger dots represent longer construction time, these large dots appear on the left side, indicating that low-sustainability options tend to require more time.
• Dots become progressively smaller as you move right, indicating shorter construction times for more sustainable designs.
• Similarly, the left side is dominated by blue dots, indicating high construction time and low sustainability. And the right side shows orange/red dots, suggesting that more sustainable designs are also more time-efficient.

Based on above observations, here are some insights and design implications:

• Design options in the top-left (large, blue dots) have low sustainability scores and long construction times, indicating poor performance and should be eliminated.
• Design options in the bottom-right quadrant contains small, warm-colored dots, indicating short construction time and high sustainability: these are optimal solutions in terms of both environmental impact and construction efficiency.
• The graph suggests there is no significant tradeoff between sustainability and construction time since both of them improve together as moving rightward, which contradicts the common assumption that more sustainable options require longer time.

Conclusion:

The generative design output shows a clear and desirable trend: as sustainability improves, construction time decreases. The most efficient, sustainable, and time-effective solutions are found in the bottom-right (high sustainability, short time, small orange/red dots).

3. Third Design Tradeoff Combination: Product Quality Score vs. Sustainability Score

Below is a couple of observations from above graph:

• There is a negative correlation: as sustainability score increases, product quality decreases.
• High product quality scores (large, blue dots) are clustered on the left (low sustainability), while high sustainability scores (right) correspond with low product quality (small, red dots).
• Since larger dots correspond to higher product quality, these large dots are only seen at the low end of the sustainability scale (left side of the plot). Toward the right (high sustainability), all dots are small, indicating lower product quality.

Based on above observations, here are some insights and design implications:

• There appears to be a strong tradeoff between product quality and sustainability. In this design space, increasing sustainability tends to reduce product quality.
• Top-left quadrant (high quality, low sustainability) contains large blue/green dots, indicating design options that offer excellent quality but poor sustainability.
• Bottom-right quadrant (high sustainability, low quality) contains small red/orange dots, indicating design options that are sustainable but with compromised quality.

Conclusion:

This generative design output reveals a clear tradeoff: designs with higher product quality tend to have lower sustainability scores, and vice versa. Therefore, it is necessary to decide based on project priorities whether to optimize for quality, sustainability, or find a balance in the middle-left portion of the graph (moderate sustainability with great quality).

4. Parallel Coordinates Graph Filtering with a purpose to minimize Construction Time, maximize both Product Quality and Sustainability Score:

Below is a couple of observations from above graph:

1. Construction Time

I filtered it to the lower range (~110–125) to minimize this metric, which significantly narrows down the design options where only a subset of lines remain.

2. Product Quality Evaluation

I filtered it for selected options which span from ~200 to 800, where I observe a positive correlation between higher product quality and slightly longer construction times, but some design options still achieve relatively high product quality (~500) with low construction time.

3. Sustainability Evaluation

I set this metric across all span. However, there is only few acceptable design options that pass through this range of span with relative low sustainability score.

4. Height

Among the filtered selections, height varies from 6 to 8, indicating that this range height would be highly correlated with desirable outcomes.

5. Base Radius

The selected options mostly cluster around 2.0 to 3.0, where extreme value (e.g., 4.0) is excluded in the filtered results.

5. Dynamo Study Graph:

An image of your Dynamo Study Graph (showing all your nodes and the connecting logic) -- You can use the File > Export Workspace As Image... command in Dynamo to save a PNG image to upload with your posting.

Above is the detailed Dynamo visual code flow diagram: where inputs are defined, solid are extruded based on nodes with defined radius and height. Moreover, the intermediate variables are calcuated (the solid area, and solid volume). Furthermore, more intermediate variables (the basearea and material usage calcuation) are also defined in later group to assist the final caluations of three evaluators. Eventually, three evaluators are derived with defined formulas, and outputs are defined to be ready for futher processing in Generative Design Study.