Architecture and affordance: a data driven and computational approach to the architectural analysis and design of buildings using affordance as a model of typology
Bibliographic record
Abstract
ISpace syntax has long been \nused as a spatial analysis tool to \ngraphically represent the relationships and connections between spaces. This has been used to promising \neffect in the past to compare buildings of a similar typology in order \nto better understand them. We can use \nthe framework of space syntax and \nintroduce affordances to gain a better understanding of how affordances \nof access, natural light, sound, and \nactivity congregate in building typology. By building a digital sensor \narray and conducting an analysis of \na particular building typology, we \ncan start to find optimal patterns of \naffordance which exist within living \nbuildings. The typology I am looking at \nfor this thesis is the United Churches of Sudbury. Sacred space is a phenomenologically dense and interesting typology which lends itself to \ngenerating interesting data. United \nChurches as a typology also have a \nphilosophy of shared multi-use space \nwhich lends itself well to this more \ngeneralized approach to understanding program through affordances. \nIn this thesis, I look at four \ndifferent case studies of United Churches, one to understand what \nmakes a United Church, and three others as examples of typology and subjects for collecting data. The aim of \nthis work is to use this data to develop a set of computational design \ntools that will eventually be used to \nsuggest a possible design for a United Church on the site of Larchwood \nMemorial United Church in Dowling. \nAside from site analysis, there \nare two parts to this affordance \nbased process. Just like how Gibson distinguishes between affordances and the invariant properties of \nobjects, I take stock of affordances \nthrough a set of affordance graphs \nand tables of relevant data from each \nof my case studies in Sudbury. I also look at the design solutions such \nas furnishings and building openings \nwhich these churches used to satisfy \nthose affordances and document them \nin the from of a pattern language. \nUsing the affordance data, I \nfind common patterns of design and \nlayout for a given typology. These \npatterns of affordance can then be \nused with a generative algorithm to \ngenerate a schematic design. In this \nthesis, I will present a number of \nschematic designs and explore the \nrange of outcomes, limitations, and \nareas for improvement that you can \nexpect from this approach to generative design. \nA final schematic design can \nthen be matched with examples of vernacular objects and strategies found \nand documented through the pattern \nlanguage of sacred space. Using data \nthat I have collected about openings \nand furnishings in the case studies, \nit becomes possible to automatically populate these schematic designs \nwith objects to complete the design. \nThis thesis will detail this process \nand the theory behind it.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".