Building Information Modelling and the documentation of architectural heritage: Between the ‘typical’ and the ‘specific’
Bibliographic record
Abstract
One of the greatest challenges to using Building Information Modelling (BIM) for the documentation of architectural heritage is in overcoming the propensity of the software toward standardization. Most BIM applications are optimized for industrialized building systems where even a minor deviation in geometry or dimension between like elements is considered problematic. Heritage buildings, on the other hand, are more typically constructed of unique elements that, while sometimes similar, can never be assumed to be identical. For example, two Corinthian capitals from the Temple of Mars Ultor may be similar, but they are not the same. In this paper, we discuss a novel method for developing a BIM for a unique vernacular building in eastern Ontario, Canada. Constructed anonymously in two discrete stages during the last half of the 19C, the builders employed both stacked log and an idiosyncratic balloon frame construction. Both types of construction are far from the standard assemblies found in commercial BIM software. In discussing the construction of the model, we will outline the integration of detailed survey data, including pointcloud, with a library of `typical', but parametric, construction details under development by our research group. While the survey provides an accurate geometrical record of the building under discussion - including structural deformations - the library is used to develop the specific assemblies and is based on, and fully indexed to, `typical' details culled from construction manuals available in Canada during the late 19C.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".