Why Buildings Fail: Are We Learning From Our Mistakes?
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
Most building professionals have investigated or performed remedial designs for at least one architectural or engineering system failure during their careers. Other practitioners, especially those who work for forensic consultants or firms specializing in disaster response and repair, are more familiar with the variety and extent of building failures as they assist their clients in restoring damaged or deficient buildings. The advent of social medial and twenty-four-hour news channels along with the general ease of finding more examples of failures in the Internet have made us realize that building failures in the broad sense are much more common than we may have realized.Relatively recent events leading to building failures such as the Christchurch, New Zealand earthquakes, the roof/parking deck of the Algo Centre mall in the northern Ontario, Canada city of Elliot Lake and the Indiana State Fairground stage collapse in the US are just a few reminders that much more work needs to be done on a variety of fronts to prevent building failures from a life safety standpoint. The need is compounded by economic concerns from what would be considered more mundane or common failures. Inspections by the author after Hurricane Katrina revealed a huge number of failures associated rain water alone as roofs, windows, flashing, mechanical penetrations etc. failed leading to interior water penetration often resulting in more damage from damp conditions and mold propagation than outright structural collapses. [...]
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