Liana Wong

Module 8 Questions:

1. How does defining the design objectives influence the outcomes in a parametric design study using Generative Design in Revit? Provide examples of how different objectives might result in varied design options.

Defining design objectives sets up a framework for the generative design solver to produce the appropriate results that satisfy the predefined criteria. Depending on the scope of the design objectives and whether the evaluation should achieve a maximum, minimum, or optimal solution, the algorithm differs accordingly. Thus, clearly identifying the problem and defining design goals is an important step to setting up an effective Generative Design study.

In my Module 7 assignment, my design objectives included the outputs of facade embodied carbon, total facade cost, and durability. Inputs included top and base radius, the material type, and material thickness. The most optimal design depends on the specific design objectives and the combination of objectives (i.e. absolute best option or most optimal option). For example, if the design objective was to simply produce a building with the cheapest facade cost, there would be an absolute highest-performing solution, which was to use the cheapest material with the thinnest material thickness. Meanwhile, if the design objective was to solely maximize the durability of the facade, then the resulting solution would be the toughest material with the greatest thickness - regardless of the influence of cost. However, if I were to combine the design objectives, an optimal solution would need to be chosen due to the inverse relationship between the total facade cost and durability. Typically, more durable materials are more costly, especially at greater thicknesses due to increased material usage. This set of design objectives will produce a range of iterations that us engineers will have to identify as the most optimal solution based on our design priorities. If durability is essential due to environmental exposure or lifespan requirements, a higher-cost, higher-carbon material may be justified. If the focus is on reducing environmental impact or staying within budget, a moderately durable but more sustainable option may be preferable.

1. Discuss the importance of identifying target taxonomies when generating synthetic datasets in architectural design. How can this help in managing large datasets and ensuring diversity and accuracy in your designs?

Identifying target taxonomies is important when generating synthetic datasets in architectural design, such that large datasets are organized into defined categories based on footprint or massing levels. Each subset is categorically differentiable so that it is representable by specialized algorithms, increasing efficiency with smaller, more manageable chunks of data. This approach supports modular workflows that are reproducible and scalable, where each taxonomy is modeled independently but integrated through a category selection variable to form a unified data generation system. By minimizing category overlap, redundancy and bias are reduced to ensure balanced diversity in large datasets. This prevents overfitting in machine learning models. For example, an L-shaped category may be defined by parameters like height, width, and indentation direction, allowing the generation of diverse yet non-redundant forms.

1. What are the potential benefits and challenges of using automated workflows for generating diverse synthetic datasets in parametric design? How can modularity and scalability be achieved in such workflows?

Potential benefits of using automated workflows for generating diverse synthetic datasets in parametric design include increased efficiency and consistency of data generation, as well as reproducibility. The generative design workflow is established by defining inputs and outputs, selecting an algorithm, and running simulations. This automated structure is helpful for standardizing data and allows for rapid iteration. The baseline framework allows for easy expansion of the dataset with new variables or additional algorithms, giving it flexibility for modifications.

Challenges of using automated workflows include the risk of overfitting or bias is the taxonomies aren’t diverse enough (overlaps), thus careful taxonomy definitions are required at the beginning of modeling. Time and expertise are needed to create a robust workflow; therefore setting up automated workflows is not the most beginner-friendly. Different classification designs may require adjustments to the workflow and algorithms used. Issues of data validation may also arise, where automation may produce unrealistic geometries.

Modularity and scalability can be achieved in these workflows by organizing Dynamo scripts into reusable custom nodes and organizing the workflow based on each geometric category. This would involve allocating the corresponding generative algorithm and input variables to each respective workflow per geometric category. A category selection variable or index may be used to call the appropriate workflow for a robust overall workflow. Scalability may be achieved by using batch runs for larger datasets or exporting data in structured formats via Excel for easy viewing.

1. Explore the role of iterative processes in optimizing design options within the generative design framework. Why might it be necessary to tweak and repeat studies, and how can this approach lead to better design outcomes?

Iterative processes allow the generative design framework to evaluate a range of options in order to explore the most optimal solution. It involves providing a range of input variables that the framework can adjust and run multiple simulations of different combinations of variables based on the evaluation metrics of the previous iteration. This cyclic process allows for an efficient workflow in order to find a high-performing design that satisfies the design objectives.

Tweaking and repeating studies is necessary to give the generative design framework a good initial guess that it can base its subsequent iterations on. If results aren’t favorable, a tweak with the range of input variables may be necessary. Other tweaks may include varying solver strategies and using diverse taxonomies to avoid bias and broaden exploration. Repetition is important in iterative design as models build on the data generated to enhance predictive capabilities and achieve design convergence.

Questions Related to the Autodesk Class:

1. Describe the general workflow of creating a generative design study in Revit, as presented in the lecture. What are the key steps involved, and how do they contribute to the generation of optimized design options?

The general workflow of creating a generative design study in Revit is as follows:

1. Define design variables: Configure design variables as parametric inputs that will control the automatic generative design process. Select which variables vary as inputs and which may be held constant.
2. Define design objectives: These are evaluation metrics that are set as outputs (i.e. gross floor area, energy performance, or construction cost).
3. Select an appropriate solver: Solvers such as Randomize, Cross-Product, Like-This, or Optimize offer different strategies for navigating the design space, from exploration to optimization.
4. Run Generative Design engine: Generate a range of design options for evaluation.
5. Evaluate results: Filter and rank design options based on performance according to design criteria. Save preferred designs for further refinement.
6. Tweak study and repeat study iteratively: Objectives can be adjusted to re-run the study in an iterative loop to improve results and enhance optimization.

This workflow allows for an automated process that can quickly define, generate, evaluate, and iterate design possibilities in an efficient manner. It allows for quick analysis of tradeoff relations between design variables that designers can reference to tweak the study for enhanced results.

1. Given the properties of a good synthetic dataset outlined in the class, such as being large, labeled, parametric, expandable, diverse, and balanced, how would you apply these principles to create a dataset for a specific building morphology study using Dynamo? Outline your approach.

To create a dataset that aligns with the 6 properties (large, labeled, parametric, expandable, diverse, and balanced), I would apply these principles as follows:

1. Large: Use a large enough dataset so that deep-learning algorithms have enough information to effectively learn and draw from a range of input combinations. This may be done by using number sliders or inputting a large list of values that may be combined with other input variables.
2. Labeled: Each sample is labeled with an output value that corresponds to the calculated value for recording purposes (i.e. “Cost”, “Embodied Carbon”).
3. Parametric: To maintain a flexible model for the study, it is important to avoid hard-coding any input variables from the dataset. Adjustable parameters are used to define the geometry, using number slides for example.
4. Expandable: Using custom nodes and modularizing the workflow in Dynamo allows for a structures framework that can be easily modified and expanded without needing to completely rework the existing setup. This includes adding new input variables or new node logic to add evaluation metrics. Adding new taxonomies can also expand the dataset to facilitate more complex learning.
5. Diverse: Varying inputs or providing a diverse set of taxonomies (with no overlap) allows for a wide range of building forms to be developed.
6. Balanced: Controlling the number of designs for each form is important to ensure that no one type of form is dominant, such that overrepresentation occurs in the dataset.

For example, a dataset consists of synthetically generated parametric mid-rise residential towers, ranging from 8 to 15 stories. To ensure the dataset is large, over 10,000 unique design variants are generated by combining different values for parameters such as floor count, footprint shape (rectangular, L-shaped, T-shaped, and U-shaped), floor-to-floor height, facade glazing ratio, and core-to-perimeter ratios. These combinations are controlled using number sliders and list inputs in Dynamo.

Each sample is labeled with quantitative outputs, including construction cost (calculated as $/m² × GFA), embodied carbon (in kgCO₂e/m²), daylight access score (based on orientation and glazing), and the surface area-to-volume ratio. These labels are automatically computed using embedded logic and custom nodes in the Dynamo workflow, enabling consistent, structured output across the dataset.

The dataset is fully parametric, meaning all geometric elements and performance metrics are defined through adjustable inputs rather than hard-coded values. Parameters such as footprint dimensions and window-to-wall ratios can be altered through sliders, ensuring flexibility and adaptability in both study and dataset generation.

To make the workflow expandable, the system is built using modular custom nodes in Dynamo. Each node controls a specific aspect of the model—such as footprint creation, performance calculations, or output ranking—which allows for easy integration of new variables, building typologies, or evaluation metrics. New taxonomies, outputs, or analysis logic can be added by simply inserting additional nodes.

Diversity in the dataset is achieved by varying the geometric taxonomies and form types. Each taxonomy—such as L-shapes or T-shapes—is uniquely defined with distinct rules for leg length, indentation, and alignment, ensuring there is no overlap and a broad spectrum of spatial and formal configurations. This level of variation captures a wide range of architectural possibilities.

Finally, the dataset is carefully balanced by generating approximately the same number of design samples for each taxonomy. For instance, around 2,500 samples are produced per form type, ensuring no single geometry dominates the dataset. This balance helps avoid bias in machine learning tasks.

1. Identify and discuss the four different solvers mentioned in the lecture that can be used to generate building masses in the Generative Design tool. How do these solvers impact the sample space and variety of design options produced?

In Generative Design in Revit (GDiR), four distinct solvers—Randomize, Cross Product, Like This, and Optimize—play key roles in shaping the sample space and variety of generated design options, each offering different strengths depending on the design goals.

The Randomize Solver assigns values randomly across the defined parameter ranges, allowing for rapid exploration of a broad and diverse design space. This is particularly helpful for large datasets. While it encourages creativity and helps identify unexpected solutions, it may result in less structured or lack in validity.

The Cross Product Solver systematically combines all possible combinations for each parameter across defined steps, resulting with a comprehensive grid of design permutations. This type of sampling ensures complete coverage of the design space and is ideal for mapping relationships between parameters. However, as the number of inputs increases, it becomes computationally intensive and may produce an overwhelming volume of results.

The Like This Solver produces slight variations of a user-selected design. This method is valuable when a promising option has been identified and needs further exploration within a constrained range. The solver clusters sample points around the base design, making it ideal for fine-tuning and sensitivity analysis.

The Optimize Solver uses a goal-directed, iterative approach to improve performance relative to specified objectives, such as minimizing cost or maximizing daylight. By evaluating and adjusting input values based on previous results, this solver efficiently converges on high-performing solutions. However, it may overlook diverse design alternatives if performance criteria are too narrowly defined.

Overall, Randomize and Cross Product solvers support broad, varied exploration of possibilities, which is useful for uncovering novel or unexpected design strategies. In contrast, Like This and Optimize solvers focus on refinement and performance enhancement, suitable for honing in on optimal solutions once a design direction is established.

1. Reflect on the examples of building masses generated with different solvers from the class handout. What insights can you gain about the relationship between solver choice and design diversity? How would you leverage this understanding in a practical parametric design project using Dynamo?

The examples from the class materials show that the choice of solver in Generative Design in Revit (GDiR) has a direct influence on the diversity, structure, and focus of the generated design options. Each solver offers unique strengths suited to different phases of the design process.

The Randomize Solver generates a broad range of design outcomes by randomly sampling input parameters. This promotes diversity and creative exploration, making it useful for early-stage ideation. However, because the selection is random, the resulting designs may lack consistency or practicality.

The Cross Product Solver explores all possible combinations of input values, ensuring full coverage of the design space. This approach is ideal for comprehensive analysis and mapping of parameter relationships, but can become computationally heavy and may lead to redundant results if not carefully managed.

The Like-This Solver focuses on creating incremental variations around a selected design, making it effective for refining promising options. It supports sensitivity analysis and local optimization but offers limited diversity since it samples only a small portion of the overall space.

The Optimize Solver uses iterative logic to search for high-performing solutions based on predefined performance goals, such as minimizing embodied carbon or maximizing solar value. While this solver is highly efficient for performance targeting, it tends to narrow the range of outputs and may overlook more unconventional or creative alternatives.

In practical applications, such as developing a Generative Design tool to balance embodied carbon, material cost, and solar gain in mid-rise residential buildings, a combined solver strategy is most effective. For example, Randomize can be used at the start to explore a variety of forms and trade-offs. Cross Product can then be applied to understand full combinations of key variables. Once promising solutions are identified, Like-This can help refine them further, and Optimize can be used to converge toward the most efficient and high-performing outcome.

By sequentially applying these solvers, designers can maintain a balance between creative exploration and performance optimization. This layered approach not only ensures that no valuable solutions are overlooked but also supports a more informed and adaptable design process.