Image of My Model
Figure 1: Original Model
Figure 2: Optimal Model
Image of Your Results
Table 1: Output of Results
Table 2: Scoring Results
Description
The additional metrics I chose were a simple cost analysis, a metric to analyze the horizontal views not blocked by surrounding buildings, the building radiation, and a metric to evaluate the direct sunlight hours a building receives. The cost analysis was created similar to the example where the surface area of each floor was multiplied by a linear function of cost/SF.
The horizontal visibility metric uses a Grasshopper node “ViewPercent” that uses the building geometry and the surrounding buildings geometry (a collection of breps defined in Rhino) to analyze the amount of the horizontal view band that can be seen at each test point. I chose to use the 30 degree offset feature since this accounts for the range of human eye visibility. This produces the percentage of test vectors at each point that are not blocked by surrounding buildings. I then added these values to get a cumulative metric.
The building radiation metric uses Ladybug nodes in Grasshopper. I chose a weather file from San Francisco and used the .epw file to create a cumulative sky matrix. The node then uses the sky matrix and relevant building geometry to calculate the total solar incident radiation on the building. I used this cumulative value as my metric.
The other metric I thought was interesting used the Ladybug “DirectSunHours” to calculate the hours of direct sunlight the building receives at each test point. This uses vectors from a Sun Path (created from the .epw data) and the buildings in question. I wanted to ignore the top and bottom surfaces of the building, so I filtered the test points by their heights by excluding z-values less than 0’ and greater than the building height. I then summed the remaining values to produce the direct sun hours metric.
To evaluate these metrics, I normalized all the metrics by the maximum value of the data set. I then chose weights for each metric based on what I thought was the most important. I prioritized gross floor area and horizontal views since those would be important for both the developer (maximizing sellable space) and for the occupants (clear views of the city is important). I also gave positive weights to the volume (good use of building footprint) and building radiation. Since the building is in San Francisco, solar radiation would help with passive heating systems. I gave negative weights to the gross surface area since the assignment indicates lower GSA values are better. I also negatively weighted the cost and the direct sun metric. I thought about this metric in terms of glare potential, so higher direct sun values would increase the amount of glare occupants experience which is a negative aspect of the building envelope.
My top three buildings are highlighted in blue in the tables above. The best choice maximizes gross floor area and volume. While it doesn’t have the highest view metric, there is more mass at the top of the building so I would expect better views at the higher floors which is good. A more detailed study that weights the view percentages based on the floor heights could change this metric. This building also has good solar radiation and an average direct sun value.