Generative Design Primer
  • Welcome
  • Introduction to Generative Design
    • Computational Design
    • Generative Design
      • What is Generative Design?
      • Why should I use Generative Design?
      • What goes into a Generative Design Process?
        • Anatomy of each stage
      • Examples of Generative Design
        • MaRs Innovation District of Toronto
        • Furniture Design
        • A Further Analogy
      • Anatomy of a Good Generative Design Process
    • Visual Programming
    • Dynamo
    • Generative Design for Revit and Dynamo
  • Deeper Dive to Generative Design
    • Algorithms
      • What are Algorithms?
      • Generators
      • Evaluators
      • Solvers
    • Optioneering
    • Optimization
      • What is Optimization?
      • Objective Function
      • Constraints
      • Data
      • Defining Goals
    • Genetic Algorithms
      • What is a Genetic Algorithm?
      • Initialization phase
      • Evaluation Phase
      • Selection Phase
      • Crossover Phase
      • Mutation Phase
    • Other Techniques
    • Genetic Algorithm Q&A
  • Hello Generative Design for Revit and Dynamo!
    • Installing Generative Design
    • Setting up a Graph for Generative Design
    • Running Generative Design
    • Visualizing Results in Generative Design
    • Refinery Toolkit
      • Installing the Refinery Toolkit from the Dynamo Package Manager
      • Using the Refinery Toolkit
    • Space Analysis for Dynamo
      • Installing the Space Analysis for Dynamo package from the Dynamo Package Manager
      • Using the Space Analysis Package
    • Using Revit alongside Generative Design
      • Using Data from Revit
      • Remember Node Inputs
      • How to Test Revit Data Capture
      • Detailed Example Workflow
      • Sharing Logic and Results
      • Current Limitations
      • Accessing Generative Design Directly From Revit
  • Sample Workflows
    • Getting Started Workflows
      • Highest Point of a Surface
      • Minimum Volume and Maximum Surface
    • Architectural Workflows
      • Building Mass Generator
      • Building Positioning based on Solar Analysis
      • Office Layout
      • Grid Object Placement in a Room
      • Entourage Placement Exploration
    • MEP Workflows
      • Distributing Spotlights in an Office Space
    • Structural Workflows
    • BIM Workflows
      • Placement of views on sheets
    • Community Examples
      • Guidelines
      • List Of Examples
  • Generative Design in Your Office
    • What Generative Design Can Be Used For?
    • What Generative Design Can’t Be Used For?
    • How to Convince Senior Stakeholders of Using Generative Design?
    • The Role of a Generative Designer
    • Hiring a Generative Designer
  • Next Steps
    • Machine Learning
      • What is Machine Learning?
      • Is Generative Design Machine Learning?
      • Can Machine Learning and Generative Design Work Together?
  • Appendix
    • Glossary
    • Reference Material
    • Need Professional Help?
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  1. Introduction to Generative Design
  2. Generative Design
  3. Examples of Generative Design

MaRs Innovation District of Toronto

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Last updated 5 years ago

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To design the new office and research space in the MaRs Innovation District of Toronto, Autodesk used generative design processes.

Starting with high-level goals and constraints, the design team used the power of computation to generate, evaluate and evolve thousands of design alternatives. The result was a high-performing and novel work environment that would not have been possible without this approach.

Generate

Above: Design goals - Mars Innovation District - The Living

The designers created a geometric system that meant the computer could explore multiple configurations of work neighborhoods, amenity spaces and circulation zones. This work represents the define step of the generate phase.

Using this algorithm, the computer varied the parameters to produce thousands of design options.

Evaluate

Above: Design option evaluated and selected- Mars Innovation District - The Living

For this stage, information was collected from employees and managers about work styles and location preferences. Based on this data, six primary and measurable goals were defined:

  • work style preference

  • adjacency preference

  • low distraction

  • interconnectivity

  • daylight

  • views to the outside

The designers then created an algorithm to measure how any given floor plan could be measured against each of the goals above. Known as evaluators, these algorithms represent the analyse and rank stages of the generative process.

After the algorithms were formulated, the computer used them to evaluate each of the designs generated in the previous stage against the defined goals.

Explore

Above: Design Options - Mars Innovation District - The Living

After the designs were evaluated, the designers looked at the solution space to explore the generated designs together with their evaluation results.

Taking into account each defined goal, they identified the design that best achieved the goals overall.