Ossature: Bone Remodeling as a Generative Structuring Process in Architecture
Bibliographic record
Abstract
There is an inherent and complex interrelationship between material, structure and form that exists in nature, whereby each informs the other through a dynamic process.In nature, form is not imposed; instead it emerges as an expression and articulation of dynamic material responses to environmental stresses and circumstances.I propose looking at this responsive form generation as a model for developing architectural structures.Specifically, this thesis proposes looking at bone tissue remodeling as a new generative process for structure in architecture.Bone tissue becomes highly optimized by the self-organizing and remodeling of its structure in response to loads; creating a complex structure that is both high in strength and low in weight.Its shape is directly informed by the forces acting upon and within it; material and structure are distributed along stress paths three dimensionally.The objective of this research will be to examine the feasibility of utilizing bone remodeling algorithms as a generative design tool in the development of structures in architecture.iii Definitions Algorithm -A sequence or procedure for calculation Additive Manufacturing (AM) -The process of making a three-dimensional object by successively adding layers of material Anisotropy -The property of being directionally dependent Bidirectional Evolutionary Structural Optimization (BESO) -Based on FEM, iteratively adds or removes material from a structure Computer-Aided Optimization (CAO) -Based on FEM, this method thickens highly stress areas of a structure much like a tree Computer Aided Internal Optimization (CAIO) -Based on FEM, optimizes fiber orientation within an object Evolutionary Structural Optimization (ESO) -Based on FEM, iteratively removes unused material from a structure Finite Element Analysis (FEA) -The application of FEM to solve engineering problems Finite Element Method (FEM) -An analysis technique used to solve a complex equation using many smaller equations Generative -Refers to a rule based system where complex behaviours emerge from the interaction of simpler elements Isotropy -Identical properties in all directions Load -a force applied to a structure causing stress, deformation or displacement Optimization -In engineering, refers to the selection of the best solution from a set for the condition of maximizing or minimizing a desired property iv Soft Kill Option (SKO) -Based on FEM, removes under-stressed material within a design boundary Strain -A measure of deformation representing displacement relative to a reference length Stress -In engineering, refers to the measurable quantity of internal forces of an object Topology -The study of surfaces, concerned with preserving spatial properties under deformation Topology Optimization -A mathematical approach that optimizes material layout within a design space for a given set of loads so that the result meets desired performance targets Von Mises Stress -A criterion used to predict yielding of materials under any loading condition Voronoi -A way of dividing space into regions where a set of points has a corresponding region consisting of all points closer to it than to any other Young's Modulus -A measure of the stiffness of an elastic material defined by the ratio of stress along an axis over strain v Acknowledgements Firstly, a thank you to my supervisor, Manuel Baez, for your insight and relentless urging to do better.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".