About This Project
This project develops a boundary to core and core to boundary shape grammar for office tower planning, aimed at systematically exploring core configurations under competing demands for net to gross efficiency, daylight, and flexible floor plates, alongside post 911 code-driven core complexity. It organizes core generation as a rule sequence that sets the core boundary, places corridors, establishes a grid, and then places passenger elevators, service elevators, stairs, and restrooms using edge-based logic and adjacency constraints. The workflow also supports controlled variation through rule-based find and replace, such as converting selected elevator elements into storage or other functions when service requirements shift.
SHAPE GRAMMAR
George Stiny (2022), Shapes of Imagination: Calculating in Coleridge's Magical Realm. The MIT Press. https://doi.org/10.7551/mitpress/14469.001.0001
George Stiny defines a shape grammar as a way to calculate visually, entirely in terms of looking and what I see. This assumption distinguishes shape grammars from symbolic computing, where calculation depends on indivisible primitives and counting. In shape grammars, shapes are not treated as fixed assemblies of predefined parts. Instead, parts emerge through the act of applying rules, and what counts as a part can change as the calculation continues. Ambiguity is therefore not an error to eliminate but a productive condition that enables seeing new structures in the same drawing over time.
A shape grammar rule is a pair of shapes, often written as A to B, that can be applied whenever a transformed copy of A can be embedded as a part of a current shape C. Following a rule means identifying an instance of the left-hand shape through embedding, then carrying out a corresponding replacement on the drawing through erasing and drawing. Stiny formalizes this as C prime equals C minus t of A plus t of B, which highlights the two-stage embed and fuse cycle that links analysis and synthesis within one recursive operation.
SHAPE MACHINE
Shape Computation Lab, Georgia Institute of Technology. https://shape.gatech.edu/index.html
Shape Machine is a shape rewrite system developed at the Georgia Tech Shape Computation Lab that brings shape grammar operations directly into CAD workflows. It provides vector-based search and replace within the actual geometry of a model, so a designer can find a target subshape and replace it with new geometry that fuses cleanly into the drawing. Shape Machine operates within Rhino and supports rapid iteration, which makes it well-suited for systematic studies where many variants must remain consistent in a shared design language. It is implemented as a Rhino plug-in, and its published prototype emphasizes robust shape representation, recognition, and modification, including subshape recognition in vector graphics.
Conceptually, Shape Machine mirrors the rule logic described in shape grammars. A rule application involves finding a transformation that embeds the left-hand shape into a current design, then replacing it with the corresponding transformed right-hand shape to produce a new design. Beyond single operations, Shape Machine supports rule sequences and DrawScript, a Turing-complete visual programming language in which shapes stand in for lines of code, with optional Python integration for hybrid visual and textual workflows.