Form to Performance (F2P): A Generative Design Tool for Building Optimization
Intended users
Architectural designers often rely on limited iterations or intuition when shaping building forms, making it difficult to understand how geometric decisions (e.g., scaling, rotation, and massing) affect performance outcomes like efficiency, cost, and solar exposure. This generative design tool is intended to aid conceptual and early-stage building design for urban designers, engineers, and architects.
Need you’re trying to provide a solution or support for
The objective for developing this easily transferable, robust, and reliable tool is to address the needs in architectural and engineering design for early-stage building form generation and optimization. Parametric and generative design approaches are particularly valuable in this domain because they enable rapid exploration of complex geometric configurations and their impact on performance metrics such as envelope efficiency, cost, and solar exposure. This generative design approach addresses the challenge of navigating high-dimensional design spaces and provides high customizability to analyze single-objective or multi-objective optimization workflows for their early design decision-making process.
- This tool provides users with the following capabilities:
- Enables systematic exploration of multiple design options
- Provides clear, visual feedback to support decision-making to aid tradeoff analysis among competing performance metrics
- Allows for analysis of high-dimensional geometric variability while maintaining clear connections between design inputs and performance outputs
Inputs
These variables control the shape, tapering, and rotation of a multi-level lofted building form, enabling exploration of complex geometries.
- Form scaling variables
- Ellipse scaling factor at N x overall building height (where N = 0.15, 0.35, 0.55, 0.85, 1)
- Rotation variables
- Rotation degree of ellipse at N x overall building height (where N = 0.15, 0.35, 0.55, 0.85, 1)
- Base geometry
- Radius 1 and 2 (x and y) of the base ellipse
- Building, story, floor heights
- Weather URL of site location for solar analysis
Underlying logic of the model you’ll implement
The model generates a building form by lofting a series of ellipses distributed along the building height. Each ellipse is parametrically controlled through scaling and rotation inputs. From this geometry, key performance metrics such as envelope area, gross floor area, and solar exposure are computed. These are then used to derive higher-level evaluators including envelope efficiency, cost, and solar performance.
The tool incorporates both Galapagos and Octopus optimization frameworks to support different phases of the design process. Galapagos is used to generate a single optimized solution enabling decision-driven optimization. By comparison, Octopus enables multi-objective optimization, generating a set of Pareto-optimal solutions that reveal tradeoffs between competing performance metrics. Together, these approaches allow users to both explore the design space and make informed decisions, enhancing the flexibility and usefulness of the tool.
- Tool provides the following:
- 3D visualization of generated building forms
- Numeric output values for performance metrics
- Optimization results, including best-performing solutions for single variable objectives (Galapagos) or Pareto fronts for multi-variable objectives (Octopus)
- This allows users to compare alternative design solutions for the targeted objective(s) and identify tradeoffs between envelope efficiency, cost, and solar performance.
Outputs
- Building Volume [CF]
- Facade Surface Area [SF]
- Floor Area [SF]
- Gross floor area created / Gross surface area of building envelope
- Average Cost per SF [$/SF]
- Solar Efficiency [kWh/m^2]
- Total Annual Insolation Potential [kWh]
Proposed Timeframe/Timeline
3 hours: Review criteria for project submission and map out the design and milestones needed to achieve the deliverables.
12 hours: Draft the overall node logic flow in Rhino’s Grasshopper and then run initial test cases to validate nodes and wirings. Determine appropriate flex range and constraints for sliders of variables. Develop understanding and establish correlation between building form & performance variation with flexing of variables. Refine logic as necessary.
- “Nice to have” features: Conduct evaluations for varied weather conditions and compile results (e.g., weather at UAE, Santa Monica, Washington for showcased examples).
15 hours: Run test cases for Galapagos single variable optimization and Octopus multi-variable optimization.
- Due to the in-depth studies with a high combination of customizable input variables and evaluators, there can be up to several hours of runtime for each of the optimization workflows. Extensive time has been allocated for running multiple simulations, such as case studies performed at various weather locations and varied objective optimization targets (e.g., maximize GFA/GSA, maximize solar efficiency, etc.).
5 hours: Compile and analyze results for the evaluations conducted. Return to refining node logic if necessary.
8 hours: Complete submission of deliverables: write-up summary and Grasshopper snapshots, recorded demo, and file uploads to ACC.
Idea & Creativity: Snapshot of New Functionalities Beyond Previous Module Assignments
Module 9 expands the tool’s capabilities by integrating both Galapagos and Octopus, enabling not only single-objective optimization but also multi-objective Pareto exploration. The parametric model has been significantly enhanced, increasing from 4 to 10 input variables, which broadens the design space and allows for more complex form exploration. This supports simultaneous evaluation of multiple performance metrics, including solar performance, cost, and geometric efficiency. Additionally, the framework is tested across three different geographic/climate contexts, introducing cross-location comparison. The outputs are also more comprehensive, combining quantitative performance metrics with 3D visualizations of solar behavior to provide deeper insight into optimization results.