Develop a parametric design that advances your understanding of Rhino/Grasshopper and related tools through algorithms (including genetic algorithms and optimization), scripting, external data, simulation, or other computational methods studied in the course. Identify a design question and show how your chosen method helps you investigate it. The final model must support interactive parametric changes.

You may build on Project 1 by improving its model and adding new design ideas, data, content, or methods. Alternatively, use another design of your own with comparable complexity and workload, including a building, landscape, structure, or product design.

Generative AI may be used for conceptualization and visualization. RhinoMCP and other AI tools connected through the Model Context Protocol (MCP) may also assist directly with the modeling process, including creating or editing geometry, Grasshopper definitions, and scripts, where supported by your tools. Use this assistance to deepen your learning. You are responsible for understanding, checking, and presenting everything you submit. AI/MCP use is optional but encouraged.

Your tasks include:

1. Homework and Tutorials  (15 pts)

Complete all the exercises assigned for the second phase. Follow the deadlines and submission procedures for each exercise. The completeness of the tutorials and on time submission will count toward part of the final grade of this project.

2. Parametric Modeling through Advanced Computational Methods (45 pts). Develop your model using at least one advanced method from the course. Identify the driving parameters and their ranges, geometric relationships, inputs, and outputs. Organize the Grasshopper definition with clearly labeled groups and controls, and comment any scripts. Show how your work extends Project 1 or develops a comparable new project.

Demonstrate at least three distinct parameter changes and explain their design consequences. Your submitted definition/scripts should regenerate the modeled options. AI-generated images may communicate design intent; document any differences between those images and the geometry you actually modeled.

Examples are:

2.1. Using Genetic Algorithm to optimize your design.

2.2. Using external data (e.g. through Excel) to drive your model.

2.3. Using scripting to implement conditional statements (IF Then) for different design options.

2.4. Using scripting to loop and create patterns.

2.5. Use scripting and/or other tools to link between parametric geometry (in Rhino/Grasshopper) and BIM (in Revit).

2.6. Using Parametric Design Patterns to add components to your models.

2.7. Using Generative AI to explore design concepts or visualize modeled options, alongside a working advanced parametric method.

2.8. Using AI to help you learn and use a parametric modeling function or method and document the learning process.

2.9. Using RhinoMCP or another AI/MCP connection to help construct, inspect, debug, or revise a Grasshopper definition or modeling script, while explaining the underlying method and verifying its results.

2.10. Developing a script or plugin that connects tools, automates a modeling task, or supports a reproducible comparison of design options.

2.11. Other applications (think creatively).

Using AI/MCP to support learning

Work in small, inspectable steps: define the task and expected result, ask AI to explain its proposed method, implement or request one change, then inspect the actual Rhino/Grasshopper result before continuing. Save model checkpoints. You should be able to modify key parameters yourself and explain the important components, data flow/data trees, mathematical relationships, and script logic in your submission.

Verify accuracy and correctness. For the three parameter changes, check relevant dimensions, units, geometry validity, component outputs, and design constraints. Independently check at least one important numerical or geometric result using a hand calculation, Rhino measurement, or a separate reference method. If using optimization or simulation, explain the objectives, constraints, assumptions, and limitations, and compare the results with a baseline. Record expected and observed results, errors, and corrections. Ground your conclusions in these checks and the actual model output.

Suggested MCP prompt: “Inspect the selected geometry and explain the relevant inputs, units, and Grasshopper data flow. Help me implement [one modeling task] controlled by [parameters and ranges]. Explain the proposed components or script before making changes. After the change, report the actual outputs and any errors. Suggest checks I can perform independently to verify the result.”

3. Visualization and Documentation (20 pts). Publish your documentation on a portfolio/blog, or submit a PDF to Canvas if you do not use a portfolio/blog. Include:

• Your case study or project background, design intent, and the new work developed for Project 2.
• An explanation of the modeling method, driving parameters, geometric relationships, data flow, and any algorithms or scripts. Use text, diagrams, equations where relevant, and readable screenshots of the organized definition.
• Screenshots of the model and at least three parameter changes, with a comparison of their design consequences. Use JPG images at 1920x1080 pixels or higher.
• Verification evidence: a concise table or annotated figures showing the test inputs, expected results, observed results, and corrections or remaining limitations.
• If you use AI/MCP, identify the tools and their roles, the important prompts and outputs, what you accepted, revised, or rejected, and what you learned. Include relevant chat excerpts or a chat export as project2_AI-MCP_chat.pdf. Explain your own contributions and cite sources for borrowed material. If no AI was used, state this.

Create a movie (up to 4 minutes, strict) by capturing your screen with your own voiceover. Present your design intent, explain the main modeling logic, demonstrate parameter changes in the working model, and show an accuracy/correctness check. Explain how any AI/MCP assistance contributed to your learning and how you verified its outputs. You can use Zoom or other recording/editing software. Embed the movie in your portfolio/blog where applicable, and submit the movie file to Canvas with your other files.

Suggested resolution: 1920x1080 pixels. Suggested format: MP4. Clear images and sound are expected.

Blogger instructions for adding images and videos

Give a reference for each image, citation, or other person's work used in your documentation. Follow the “AI Usage Policy: Guidelines and Disclosure” in the Syllabus (linked to Simple Syllabus), together with the project-specific AI/MCP permissions and documentation requirements above.

4. Final presentation (10 pts)

Present your movie and participate in Q&A when time permits. Be prepared to explain the model's logic and your contributions, demonstrate a parameter change, and justify how you checked accuracy and correctness, including AI-assisted work. Make sure the movie and model are ready before the presentation. Presentation dates will be announced in Canvas.

Please also submit your movie with other files to Canvas.

5. Submission

Submit the following three items by December 2, 2026, 10:00 PM (Central Time): the project ZIP, portfolio/blog URL or PDF documentation, and thumbnail.

5.1 Project files submitted to Canvas

5.2 Documentation for Presentation: Portfolio/Blog or PDF on Canvas

Create a portfolio/blog page containing the documentation required in Section 3, including your description, screenshots, verification evidence, AI/MCP disclosure if applicable, and presentation movie. Your name and “ARCH655 Project 2 (Final Project)” should appear on the page. Submit the portfolio/blog URL to Canvas. If you do not use a portfolio/blog, indicate this in your submission and submit project2_documentation.pdf to Canvas with the movie file.

As in Project 1, project URLs and thumbnails will be collected in an access-controlled TAMU Google Drive spreadsheet for class presentation. You may control access to your portfolio/blog; if it is private, give the instructor access before the presentation. See the Students/Gallery page for the class presentation link when announced, and check that your project link works.

5.3 Submit your project thumbnail to Canvas: https://canvas.tamu.edu

Create a 160x120 pixel thumbnail image based on a representative model view or rendering. Save it as Project2_YourFirstname_YourLastname.jpg, and upload it to https://canvas.tamu.edu

The thumbnail will be included in the access-controlled class presentation spreadsheet after it is uploaded by the instructor. Check the Students/Gallery page for the presentation link when announced.

Before the deadline, reopen your submitted model/definition and check its dependencies, the documentation or portfolio/blog access, the movie, and the thumbnail.

Develop advanced parametric modeling skills through algorithms, scripting, data, optimization, simulation, and related tools. Use AI/MCP, when helpful, to strengthen your understanding and extend your modeling process.

Explain: describe your design intent, parameters, geometric relationships, data flow, and algorithms/scripts, including any work developed with AI.

Verify: independently check the accuracy and correctness of the model and its results, identify errors, and document corrections and limitations.

Present: communicate your process and findings clearly through documentation, a narrated demonstration, and Q&A.

Your work will be judged on its workload and challenge, design thinking, technical accuracy, meaningful parametric behavior, visual quality, and demonstrated understanding. The point distribution is: homework/tutorials (15), modeling (45), documentation (20), and presentation (10), with up to 10 points for exceptional work. Explanation and verification evidence are assessed within modeling/documentation, and your ability to present (and answer questions when asked) is assessed within presentation. AI/MCP use is assessed through your understanding, decisions, and checked results.