(Adding this Image at the beginning just for the Journal Cover 😄)

2 Units: Create Two New Evaluator Nodes

The objective of this assignment was to recommend a proposed design to the developers of a new high-rise building project in Dubai based on my evaluation of potential building forms. To achieve this objective, I followed the approach outlined below:

1. I first defined the specific design goals I aimed to optimize.
2. I then identified two metrics that would be used to assess and optimize these goals.
3. I created node logic to compute the evaluation metrics for a single instance.
4. I developed custom nodes that could be used to evaluate multiple instances efficiently.
5. Finally, I evaluated the outcomes and discussed the results that led to the best design.

Defining the Design Goals

Defining the Design Metrics

To effectively achieve the defined design goals, I established specific metrics to measure and guide the optimization process. These metrics provided a clear framework for evaluating the building form in terms of both economics and performance.

Economic Metric: Total Construction Cost

This metric is the sum of the construction costs for all floors, providing a comprehensive measure of the project's financial feasibility. It takes 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 750 feet above ground. I calculated the cost per square foot at a given level as:
$$\text{CPSF}_\text{level} = \text{CPSF}_{0} + \left( \text{Elevation} \times \frac{\text{CPSF}_{750} - \text{CPSF}_{0}}{\text{MaxElevation}} \right)$$

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 $/SF
3. GPSF_750: Cost per square foot at the max elevation of 750 feet ($/SF) = 1000 $/SF
4. MaxElevation: Maximum specified elevation (Feet) = 750 ft
5. Elevation: Height in feet at which I want to calculate the cost (ft)

Solving the Equation:

$$\text{CPSF}_\text{level} = 500\$/SF + \left( \text{Elevation} \times \frac{1000\$/SF - 500\$/SF}{750 \text{ ft}} \right)$$
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.

Building Performance Metrics: Solar Efficiency Index

In order to solve the duplex goal of minimizing the surface area of the building envelope and maximizing the solar insolation potential as measured by cumulative insolation available on the building envelope surfaces throughout the year, I came up with an index that I named the Solar Efficiency Index (SEI) ☀️.

The solar efficiency index metric evaluates the building's ability to harness solar energy efficiently throughout the year, providing insight into the building’s potential for passive solar heating, daylighting, and photovoltaic energy generation.

I have calculated the SEI by measuring the cumulative solar insolation available on the building envelope surfaces and comparing (dividing) it to the total surface area of the envelope. This metric helps to optimize the design for maximum solar gain, enhancing energy efficiency and sustainability.

Where:

• SEI = Solar Efficiency Index
• SI_cumul = Cumulative solar insolation. This refers to the total amount of solar radiation (insolation) received by a surface over a specific period.
• SA_tot = Total surface area of the building envelope. This comprehends walls and roof, but excludes the ground floor!

Implementing the Nodes for the Individual Design Metrics

I have implemented three distinguished custom nodes, each used to calculate:

1. The original metrics, already caclulated in Assignment 5: Gross Floor Area (GFA), Gross Surface Area (GFA), Gross Volume (GV). I named the custom node “GFA_GSA_GV_Metrics_LaviniaCustomMetric.dyf”.
2. The new metric related to economics: total construction cost. I named the custom node “ConstructionCostLinear_LaviniaCustomMetric.dyf”.
3. The new metric related to building performance: solar efficiency index. I named the custom node “SolarEfficiencyIndex_LaviniaCustomMetric.dyf”.

The individual custom nodes schematics are illustrated below:

Node to Calculate the Original Metrics: GFA, GSA, and GV

Node to Calculate the Economic Metric: Total Construction Cost

Node to Calculate the Building Performance Metrics: Solar Efficiency Index

(continuation of the image above) ⤵️

Node to Evaluate All the Individual Design Metrics

Next, I have implemented a custom node where, given a series of inputs, it creates the parametric tower (another nested node) and calculates all the mentioned metrics. The node inputs and outputs are displayed in the image below (left), and its simplified schematic is illustrated in the image below (right).

Analysis and Results:

For this analysis, I have flexed the following parameters (and hence performed a grid search):

• Base Star Radius = 110, 120, 130 ft
• Number of Star’s Mountains = 7, 9, 11, 13, 15, 17

The analysis produced the following results. I have highlighted in red the results that are not compliant to the GFA requirements for which the GFA should be comprised between 2’500’000 and 3’000’000 SF. All the other requirements are satisfied, as the height is fixed at 742’ (less than 755’ as requested), and the largest radius value among all the radii (bottom, middle, top) employed is of 130’ (the top and base star radii), meaning a diameter of 260’ (less than 984’ wide x 328’ deep in plan view as requested).

To better vidualize these results, I have removed the combinations in red, and plotted each valid combination - represented by (Base Star Radius, Number Star Mountains) - in the scatter plot below. I highlighted in red, green, and blue the points corresponding to base radius of 110, 120, and 130, respectively.

Trends:

• Increasing the number of star mountains given a fixed base radius, leads to higher SEI and higher TCC
• Increasing base radius given a fixed the number of star mountains, leads to higher SEI and higher TCC

The current evaluation metrics, Solar Efficiency Index (SEI) and Total Construction Cost (TCC), are already great at capturing key aspects of the building form alternatives. Specifically:

• Higher SEI indicates better solar performance, which is crucial for energy sustainability.
• Lower TCC indicates cost efficiency, which is important for budget constraints.

Despite these metrics provide a fundamental understanding of the trade-offs between solar efficiency and construction cost, it is challenging to determine which design is best due to the trends, as maximizing SEI and minimizing TCC present conflicting goals.

As a solution, a single-objective optimization scheme/metric that combines all computed metrics into a single metric would simplify the evaluation process and provide a clearer indication of the overall performance of each building form alternative. This streamlined approach would then facilitate easier comparison between alternatives and help to identify the optimal solution that balances economic feasibility with performance excellence.

(Note that I have implemented this metric in the next stage.) Finally, another useful metric could be one that takes into account sustainability indicators in the score, including:

1. The solar efficiency index to measure the effectiveness of the building form in harnessing solar energy.
2. The net energy production to quantify the amount of surplus energy generated by the building, considering both energy generation and consumption.
3. The environmental impact to evaluate the environmental footprint of the building form, considering factors such as carbon emissions, resource utilization, and waste generation.
4. The life cycle cost that accounts for the total cost of ownership over the building's lifespan, including construction, operation, and maintenance costs.

The alternative metric could then be named as the “Sustainability Performance Score”. This score can be computed by aggregating the normalized scores of these indicators, with appropriate weighting based on the design/project's priorities and objectives.

A higher SPS would indicate a greater sustainability performance for building form alternatives. This metric could be of great importance for Dubai, a city that places significant emphasis on sustainability and environmental stewardship.

3 Units: Develop a Single-Objective Optimization Scheme

In the following description I have answered to these questions in detail:

To effectively recommend the “best case” alternatives to the owner / client, I have developed a single-objective optimization scheme that combines all computed metrics into a single metric. This metric encapsulates both economic and performance aspects of the building form, that I have already tackled individually in the previous module.

Design Strategy:

1. Identify and Understand Metrics: I started by identifying and understanding the two primary metrics for evaluating the building forms: the Total Construction Cost (TCC) and the Solar Efficiency Index (SEI).
• Total Construction Cost (TCC): As already mentioned, this metric accounts for the financial feasibility of the project by summing up the construction costs of all floors, considering the varying cost per square foot at different elevations.
• Solar Efficiency Index (SEI): As already mentioned, this metric evaluates the building's potential to harness solar energy effectively. It is calculated by dividing the cumulative solar insolation available on the building envelope by the total surface area of the envelope.
2. Interaction of Metrics: I analyzed how these metrics interact with each other:
• Aligned Metrics: Upon analysis, I observed that TCC and SEI do not directly align. That is, they do not necessarily move in the same direction as the input values vary.
• Tradeoff Metrics: TCC and SEI often represent tradeoffs. Minimizing the TCC might reduce the building’s height and surface area, potentially reducing the SEI. Conversely, maximizing SEI might increase the surface area and height, thus increasing TCC.
3. Relative Importance of Metrics: Next, I determined the relative importance of each metric based on the project’s design goals. Given that we are in Dubai where economic considerations are less critical, I assigned a higher weight to the performance metric (SEI). Therefore, I suggest:
• Economic Metric (TCC): 40% importance
• Performance Metric (SEI): 60% importance

These weights reflect a higher importance on solar efficiency while still considering cost minimization to some extent!

4. Normalization of Metrics: To combine these metrics into a single objective, I normalized them to ensure comparability. Normalization was done using the min-max method:

5. Combine Metrics into Single Objective: I then combined the normalized metrics using their assigned weights to create a single composite score:
$$\text{Single Obj. Value} = W_{SEI}\times\text{NormalizedSEI} + W_{TCC} \times (1-\text{Normalized TCC})$$

With W_SEI and W_TCC being the importance (weights) given to the individual metrics. I hence propose to give more importance to SEI as follows:

$$\text{Single Obj. Value} = 0.6\times\text{NormalizedSEI} + 0.4 \times (1-\text{Normalized TCC})$$
6. Rank Alternatives:The building forms were finally ranked based on their single objective value. The alternative with the highest value is recommended as the best case.

I have implemented this approach in a custom node, as illustrated below:

Analysis and Results:

For this analysis, I have flexed the base star radius and the number of star mountains as in the previous stage. The results:

To better vidualize these results, I have removed the combinations in red, and sorted the combinations so that the single optimization objective scores are in descending order. I have also highlighted the top three combinations in the table.

Based on the provided data, the top three recommended design alternatives for maximizing the single optimization objective score are, in order:

1. Base Star Radius: 110, Number Star Mountains: 17, Single Optimization Objective: 0.645
2. Base Star Radius: 120, Number Star Mountains: 17, Single Optimization Objective: 0.596
3. Base Star Radius: 110, Number Star Mountains: 15, Single Optimization Objective: 0.548

The recommended building form of a base star radius of 110 and 17 star mountains is then the best choice because it consistently achieves the highest single optimization objective scores in the dataset.

The single-objective approach offers a streamlined method for integrating multiple metrics into a unified evaluation criterion. However, to implement this approach, I had to normalize the metrics, leading to the loss of individual dimensions (and hence maybe overlooking nuanced interactions between design parameters).

To mitigate these limitations, one should consider contextual factors, tradeoffs, uncertainties, and stakeholder preferences, so that the designer can achieve a more comprehensive understanding of the design space and hence make better informed decisions.

4 Units: Visualize the Recommended Alternative

For this task, I have decided to visualize the recommended building form in Autodesk Forma. Below you can see an image of the best design selected in the previous stage (3 Units) and the parameters selected to create it.

As clearly visible from the map, the design stays well within the site development limits, adhering to the following restrictions:

• A maximum width of 300 meters (984 ft) and a depth of 100 meters (328 ft) in plan view.
• A height that does not exceed the site’s limitation of 230 meters (755 ft), with the design height being 742 ft.

Using this design, I have conducted three analyses: Daylight, Wind, and Solar Energy analysis.

☀️ Solar Energy Analysis ☀️

For this analysis, i configured the solar panels to cover 100% of the available roof area, with each panel operating at an efficiency of 25%.Results:

• The total amount of solar energy generated is an impressive 142,000,000 kWh.
• On average, the solar energy yield is 1890 kWh per square meter.

These results highlight the effectiveness of maximizing roof space for solar panel installation and the high efficiency of the panels I selected. This analysis demonstrates the feasibility being able to make a significant impact on energy savings and environmental sustainability.

🌬️ Wind Analysis 🌬️

I conducted a wind analysis using Autodesk Forma to assess the wind comfort levels around my project site. The analysis, visualized image below, shows the distribution of wind comfort zones around the proposed design (tower).

• The yellow zones represent areas comfortable for strolling. These regions are immediately surrounding the central tower in an oval shape, where wind speeds are moderate and conducive to walking. The placement of these strolling areas suggests that wind conditions are managed well enough to allow for comfortable movement close to the tower.
• The green areas indicate zones where wind conditions are comfortable for standing. These regions form an outer ring around the tower, providing a suitable environment for people to stand comfortably without being affected by strong winds.

The analysis highlights that the majority of the site is well-suited for standing and strolling activities. The standing zones ensure that people can comfortably remain stationary in most of the outer areas, while the strolling zones around the tower provide a pleasant environment for walking.

💡Daylight Potential Analysis 💡

The daylight analysis conducted in Forma Autodesk reveals that the building's design generally meets or exceeds the 27% Vertical Sky Component (VSC) threshold on all facades, ensuring satisfactory natural light conditions throughout the year.

• For 3 or More Units: Develop a Single-Objective Optimization Scheme

• Your answers to the Points to Ponder questions for each stage of the assignment that you completed.