What Bricks Want: Machine Learning and Iterative Ruin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ruin has a bad name. Despite the obvious complications, failure provides a rich opportunityÂhow better to understand a buildingÂs physicality than to watch it collapse? This paper offers a novel method to exploit failure through physical simulation and iterative machine learning. Using technology traditionally relegated to special effects, we can now understand collapse on a granular level: since modern-day physics engines track object-object collisions, they enable a close reading of the spatial preferences that underpin ruin. In the case of bricks, that preference is relatively simpleÂto fall. By idealizing bricks as rigid bodies, one can understand the effects of gravitational force on each individual brick in a masonry structure. These structures are sometimes able to Âsettle, resulting in a stable equilibrium state; in many cases, it means that they will simply collapse. Analyzing ruin in this way is informative, to be sure, but it proves most useful when applied in series. The evolutionary solver described in this paper closely monitors the performance of constituent bricks and ensures that the most successful structures are emulated by later generations. The tool consists of two parts: a user interface for design and the solver itself. Once the architect produces a potential design, the solver performs an evolutionary optimization; after a few hundred iterations, the end result is a structurally sound version of the unstable original. It is hoped that this hybrid of top-down and bottom-up design strategies offers an architecture that is ultimately strengthened by its contingencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it