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Fig 4: Project 1 — Two-dimensional field drawings with one variable parameter (top) and two variable parameters (bottom). (S. Akolly) After establishing this rule set, students then layer a second logic this parameter can change from one geometry to the next.

Figure 4 Project 1 — Two-dimensional field drawings with one variable parameter (top) and two variable parameters (bottom). (S. Akolly) After establishing this rule set, students then layer a second logic this parameter can change from one geometry to the next.

Related Figures (8)

Fig. 1: Iterative series of parametrically generated massings (A. Rouhi) Learning parametric design software can be very daunting for the beginning design student, particularly those with limited software experience. By the same token, the remarkable ease of contemporary visual programming languages such as Grass- hopper can often yield a kind of banal uniformity to the work; we see this in the glut of twisty towers and endlessly panelized surfaces found in almost any architecture school. This paper presents one approach to addressing the challenge of how best to introduce these powerful tools at an early stage of a design curriculum. The approach, developed through a series of intro- ductory courses and workshops, explores how parametric tools can be applied to two conventions of the architectural design process: iteration and representation.
Fig. 1: Iterative series of parametrically generated massings (A. Rouhi) Learning parametric design software can be very daunting for the beginning design student, particularly those with limited software experience. By the same token, the remarkable ease of contemporary visual programming languages such as Grass- hopper can often yield a kind of banal uniformity to the work; we see this in the glut of twisty towers and endlessly panelized surfaces found in almost any architecture school. This paper presents one approach to addressing the challenge of how best to introduce these powerful tools at an early stage of a design curriculum. The approach, developed through a series of intro- ductory courses and workshops, explores how parametric tools can be applied to two conventions of the architectural design process: iteration and representation.
Fig. 2: Screenshot of a typical Grasshopper definition, showing the visual programming interface of graphical nodes linked in a relational network. While the program remains in beta mode and has yet to be officially released, it has had an unquestionably transformative effect on both the practice and teaching of architecture. The ease and speed with which Grasshopper enables parametric’ workflows largely explains its widespread adoption, so much so that the word “Grasshopper” has become shorthand for a go-to aesthetic of complex, curvilinear forms and richly patterned surfaces. Patrik Schumacher, perhaps the most vocal proponent of this contingency between technique and aesthetic, cele-
Fig. 2: Screenshot of a typical Grasshopper definition, showing the visual programming interface of graphical nodes linked in a relational network. While the program remains in beta mode and has yet to be officially released, it has had an unquestionably transformative effect on both the practice and teaching of architecture. The ease and speed with which Grasshopper enables parametric’ workflows largely explains its widespread adoption, so much so that the word “Grasshopper” has become shorthand for a go-to aesthetic of complex, curvilinear forms and richly patterned surfaces. Patrik Schumacher, perhaps the most vocal proponent of this contingency between technique and aesthetic, cele-
Fig. 3: A search for “parametric design” on Google Images demon- strates the formal homogeneity that characterizes much of contempo- rary design computation. Towards A Critical Pedagogy of Design Computation
Fig. 3: A search for “parametric design” on Google Images demon- strates the formal homogeneity that characterizes much of contempo- rary design computation. Towards A Critical Pedagogy of Design Computation
Fig. 5: Project 2 — Iterations of a three-dimensional field system that demonstrates parametric change. The drawings at the bottom use color to index the changing rotation of the geometries. (A. Rouhi) The second assignment extends the logic of Project 1 to a three- dimensional system of geometries deployed along a surface. The process is similar: students identify two different parame- ters that are programmed with variable behaviors, and they output a series of iterations exploring the effects of that varia- tion. Although the outcomes begin to evoke the language of continuous differentiation and panelization that is so often as- sociated with Grasshopper, the intent here is otherwise: to foster a rigorous understanding of how to establish properties that are standard (or repetitive) and properties that are variable (or different). Importantly, the students are given a common surface to use as the basis for this project; this neutralizes the question of form and ensures consistency from one iteration to the next.
Fig. 5: Project 2 — Iterations of a three-dimensional field system that demonstrates parametric change. The drawings at the bottom use color to index the changing rotation of the geometries. (A. Rouhi) The second assignment extends the logic of Project 1 to a three- dimensional system of geometries deployed along a surface. The process is similar: students identify two different parame- ters that are programmed with variable behaviors, and they output a series of iterations exploring the effects of that varia- tion. Although the outcomes begin to evoke the language of continuous differentiation and panelization that is so often as- sociated with Grasshopper, the intent here is otherwise: to foster a rigorous understanding of how to establish properties that are standard (or repetitive) and properties that are variable (or different). Importantly, the students are given a common surface to use as the basis for this project; this neutralizes the question of form and ensures consistency from one iteration to the next.
The first two projects incorporate techniques and tutorial strat- egies that are fairly typical for introductory courses in paramet- ric design. The third and final project takes on more advanced techniques, but it does so in a way that departs significantly from the mainstream of parametric design coursework in aca- demia. Rather than focusing on formal acrobatics, complex facade systems, or logics of optimization or performance-driven design, the project instead takes a more disciplinary approach tc understanding how parametric tools can be folded into an ar- chitectural design process. Fig. 6 (left): Sol Lewitt, “Variations of Incomplete Open Cubes” (1974) Fig. 7 (right): Manfred Mohr, “P-197 J” (1979) design, the project instead takes a more disciplinary approach to facade systems, or logics of optimization or performance-driven
The first two projects incorporate techniques and tutorial strat- egies that are fairly typical for introductory courses in paramet- ric design. The third and final project takes on more advanced techniques, but it does so in a way that departs significantly from the mainstream of parametric design coursework in aca- demia. Rather than focusing on formal acrobatics, complex facade systems, or logics of optimization or performance-driven design, the project instead takes a more disciplinary approach tc understanding how parametric tools can be folded into an ar- chitectural design process. Fig. 6 (left): Sol Lewitt, “Variations of Incomplete Open Cubes” (1974) Fig. 7 (right): Manfred Mohr, “P-197 J” (1979) design, the project instead takes a more disciplinary approach to facade systems, or logics of optimization or performance-driven
Fig. 8: Peter Eisenman, “House IV” (1975) Allen’s reading of Eisenman’s early work touches on the com- plex nature of authorship within a rigorous process-based pro- cess. It is this dynamic between the designed and the
Fig. 8: Peter Eisenman, “House IV” (1975) Allen’s reading of Eisenman’s early work touches on the com- plex nature of authorship within a rigorous process-based pro- cess. It is this dynamic between the designed and the
Fig. 9: Project 3 — Boolean operations are systematically and iteratively performed ona simple primitive. (J. Joong) The iterative structure of this exercise demands a high degree of representational rigor to ensure the legibility of the volumetric transformation. This aspect of the project taps into the soft- ware’s ability to control not only formal or geometric opera- tions, but also representational decisions. Students learn how to program parametric behaviors into conventions of representa- tion such as line weights, line types, shading techniques, surface textures, and shadow casting. This fosters a representational awareness in the work and helps to reinforce the rigor of the iterative process. By leveraging the computational tool to itera- tively construct both form and the representation of that form, this process promotes a synthetic understanding of computa- tion—one that delivers a technical understanding of parametric software while also grounding the work within broader conven- tions of architectural design. software while also grounding the work within broader conven- The final phase of the project asks students to perform a quanti- tative analysis on the geometry produced through the iterative
Fig. 9: Project 3 — Boolean operations are systematically and iteratively performed ona simple primitive. (J. Joong) The iterative structure of this exercise demands a high degree of representational rigor to ensure the legibility of the volumetric transformation. This aspect of the project taps into the soft- ware’s ability to control not only formal or geometric opera- tions, but also representational decisions. Students learn how to program parametric behaviors into conventions of representa- tion such as line weights, line types, shading techniques, surface textures, and shadow casting. This fosters a representational awareness in the work and helps to reinforce the rigor of the iterative process. By leveraging the computational tool to itera- tively construct both form and the representation of that form, this process promotes a synthetic understanding of computa- tion—one that delivers a technical understanding of parametric software while also grounding the work within broader conven- tions of architectural design. software while also grounding the work within broader conven- The final phase of the project asks students to perform a quanti- tative analysis on the geometry produced through the iterative
Fig. 10: Massing studies (top) are subject to quantitative analysis (bottom) that uses color to represent the surface area of each face of the geometry. (H. Koo) massing studies. Tne Intent Is to Introduce tne sottware's analyt- ic capabilities in a way that demonstrates the value of quantita- tive analysis without suggesting that the entire design process must be contingent on such techniques. Once the geometric transformation is complete, students identify a simple metric— such as surface area, or volume—and link this parameter to a color gradient that allows for a visual representation of the data. While the analysis in this case is largely self-referential (quantify- ing properties of scaleless geometry produced by an abstract formal process), it nonetheless reinforces the understanding of the software’s ability to perform highly analytical operations. By doing so in a way that inverts the causal relationship between data and form that typically characterizes parametric design, it demonstrates how the technology can augment a design pro- cess rather than determine it.
Fig. 10: Massing studies (top) are subject to quantitative analysis (bottom) that uses color to represent the surface area of each face of the geometry. (H. Koo) massing studies. Tne Intent Is to Introduce tne sottware's analyt- ic capabilities in a way that demonstrates the value of quantita- tive analysis without suggesting that the entire design process must be contingent on such techniques. Once the geometric transformation is complete, students identify a simple metric— such as surface area, or volume—and link this parameter to a color gradient that allows for a visual representation of the data. While the analysis in this case is largely self-referential (quantify- ing properties of scaleless geometry produced by an abstract formal process), it nonetheless reinforces the understanding of the software’s ability to perform highly analytical operations. By doing so in a way that inverts the causal relationship between data and form that typically characterizes parametric design, it demonstrates how the technology can augment a design pro- cess rather than determine it.
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EducationArchitectureArchitectural EducationParametric Design (Architecture)Computational DesignParametric ModelingArchitectural Design PedagogyArchitectural Pedagogy
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