Step 1 - Generative Design Framework
- Design Decision 1
- Design Variables
- Variables 1: Cross-section width
- Variables 2: Height
- Evaluators
- Evaluators 1: Construction Cost
- Evaluators 2: View Quality(linearly distributed by the height of the building)
- Most Important Tradeoffs to Consider
- Increasing the building height improves the view quality but also increases the embodied carbon. The critical tradeoff is finding the optimal balance between view quality and construction cost.
- Design Decision 2
- Design Variables
- Variables 1: Roof Area
- Variables 2: Height
- Evaluators
- Evaluators 1: Total amount of energy that is generated by the solar panel installed on the roof
- Evaluators 2: View Quality(linearly distributed by the height of the building)
- Most Important Tradeoffs to Consider
- The primary tradeoff lies in striking a balance between view quality and energy generation. As the building height increases, the available roof area for installing solar panels decreases, potentially impacting the overall energy generation capacity.
- Design Decision 3
- Design Variables
- Variables 1: the total material amount for timber and steel (assuming that building is timber and steel hybrid structure)
- Variables 2: height
- Evaluators
- Evaluators 1: Construction Cost
- Evaluators 2: embodied carbon
- Most Important Tradeoffs to Consider
- The fundamental tradeoff in this design decision revolves around the proportion of timber and steel used. The selection of materials affects both the embodied carbon and construction cost, as timber tends to have higher costs but lower embodied carbon compared to steel. Striking the right balance is crucial in managing these tradeoffs effectively.
Step 2 - Generative Design Study
Objective: The objective of the model is to analyze the optimization between construction cost, embodied carbon, and view score for three different buildings constructed with mass timber, concrete, and steel.
Model: The model consists of three buildings, each constructed with a different material. The variables and constants within the model are used to evaluate the trade-offs between construction cost, embodied carbon, and view score.
Design Variables:
- Building Material: Mass timber, concrete, steel
- Number of Floors: Represents the height of the building
Constants:
- Construction Cost per Square Foot: Specific to each material (mass timber, concrete, steel)
- Embodied Carbon per Square Foot: Specific to each material (mass timber, concrete, steel)
Evaluators:
- Construction Cost: Assesses the total cost of construction based on the building material and number of floors.
- Embodied Carbon: Measures the total carbon footprint associated with the construction material and building height.
- View Score: Assigns a numerical value to represent the view quality based on the height of the building.
Interpretation: The model allows for the evaluation of trade-offs between construction cost, embodied carbon, and view score for each building material. By varying the number of floors and considering the specific construction costs, embodied carbon values, and assigned view scores, it is possible to analyze the optimal balance between these factors. The interpretation of the results will provide insights into which material, building height, and associated costs and carbon footprint provide the most favorable combination of construction cost, embodied carbon, and view score.
Step 3 - Generative Design Study Results
The analysis includes the visualization of the relationship between view score, construction cost, and embodied carbon using scatterplots and parallel coordinates graphs. The findings demonstrate that as the height of the building increases, there is a corresponding increase in the view score, but with noticeable escalations in construction cost and embodied carbon. Moreover, the model considers the use of different construction materials, presenting trade-offs associated with their selection. For instance, opting for concrete or steel can yield cost savings, albeit at the expense of higher embodied carbon emissions. The model focuses on showcasing combinations that offer a high likelihood of optimal outcomes considering material choices.
In practical applications, it is important to note that buildings can be divided into distinct sections based on the materials used, particularly in hybrid structures. Additionally, aesthetics can play a significant role, given the current prominence of mass timber buildings renowned for their visual appeal. The results generated by the model empower users to make informed decisions regarding the optimal building height based on their specific floor area requirements. Furthermore, by incorporating a maximum limit for embodied carbon, buildings can adhere to future regulations governing carbon emissions.
The model serves as a valuable tool for stakeholders in the design and construction industry, facilitating the exploration of different design scenarios while balancing considerations of view score, construction cost, and embodied carbon. It enables decision-makers to strike an optimal balance between functional requirements, environmental sustainability, and aesthetic preferences in their building projects.
Each building within the model is designed to have the flexibility of adjusting its location and applying distinct colors for visualization purposes. In future iterations, an advanced version of the model can incorporate additional features such as adjusting the view score based on potential blockages caused by neighboring buildings. This enhancement will provide a more comprehensive analysis of the view quality and further refine the decision-making process for optimal building placement.
The initial phase of the model involves the calculation of construction costs for each building. Each building within the model is assigned a unique construction cost, which is linearly distributed based on the number of floors for the sake of simplification. This approach allows for a systematic evaluation of the financial implications associated with varying building heights and facilitates the optimization process for cost-effective design decisions.
The subsequent stage of the model focuses on assessing the view quality of each building. To streamline the analysis, a simplified approach is adopted where higher floors are assigned higher view scores in a linear fashion. This approximation allows for a straightforward evaluation of the relationship between building height and the perceived view quality. By incorporating this aspect into the model, it becomes possible to quantify and compare the visual appeal of different building designs, aiding in the decision-making process for optimal building configurations.
The final aspect of the model pertains to the assessment of embodied carbon in the building designs. Specifically, the model assigns the following values to represent the embodied carbon for each construction material: steel with a value of 900 kgCO2e per square foot, Mass Timber with a value of 450 kgCO2e per square foot, and Reinforced Concrete (RC) with a value of 800 kgCO2e per square foot. These values allow for a quantitative analysis of the environmental impact associated with the choice of construction materials. By considering the embodied carbon, the model provides insights into the carbon footprint of each building design, facilitating informed decisions regarding sustainability and environmental considerations.
