In this week’s assignment, I wanted to look at three different evaluation factors:
- The total cumulative floor value of the building
- The potential overturning moment magnitude on the building (using ASCE 7-22 design code)
- The space efficiency of the building
- Induced cost from cooling the building (1 - solar insulation value)
I chose these four evaluation criteria since they are somewhat contradictory in nature.
In theory, we want to make the higher floors larger from an aesthetic and comfort point of view since the higher floors provide the best views and are most “valuable” as tenants would be more likely to pay more for a penthouse rather than a 2nd story room.
However, from a structural engineering point of view, having a larger mass at a higher elevation would result in higher seismic weight there and induce a higher overturning moment (a function of weight and height).
The last factor I wanted to consider was the amount of AC cooling required to heat the building. For a tall building, a large part of its operating costs come from the HVAC system. Here in California, with hot weather all year round, a large portion of that comes from cooling the building to comfortable temperatures. The amount of sun directiveness would increase indoor temperatures and increase AC costs. This would be inversely proportional to the solar insulation of the building.
I created a custom node for evaluating overturning moments and incorporated two custom nodes with slight modifications (floor area value and insulation value).
The custom node uses the Equivalent Lateral Force (ELF) procedure as described in ASCE 7-22.
Considering the energy efficiency of cooling the building, the more sun hitting the building surface, the more cooling would be required. As such, I took the custom node for solar insulation and subtracted it from 1.
Similarly, the custom node for area value was modified to suit my situation.
Input Values:
Custom Node for Optimizing all Parameters:
Custom Node for Evaluating Overturning Moment:
To weigh these factors, I gave them equal weighting (2.5 each), summing to a total of 10 for easy comparison between options (0 being the worst, and 10 being a perfect building). Below is the logic for the evaluator node.
Evaluator Node:
Resulting Table:
Bolded are the optimized building input parameters and results
