Bibliographic record
Abstract
Engineered timber is being used to create increasingly taller structures, with building codes adapting to reflect this drive. While these structures continue to become more common, there has been little investigation into the possibility of repairing fire-damaged timber structural members, leaving practitioners with little guidance in the event of a fire and insurance companies with little information to assess risk. The research herein attempted to repair fire-damaged timber members by removing damaged portions of the timber and replacing them with new timber panels secured with structural screws. Members were loaded in four-point bending, and the findings concluded that the members were able to regain a significant amount of stiffness compared to the control members and on average deflected 19% less than they did prior to repair. The repaired members were not able to reach full strength, however, failing at a load ranging from 49% to 66% of the failure load of the control members. This indicates the need to further examine possible alterations that may improve this repair procedure to the point where composite action is fully enabled and a larger portion of the original strength is recovered. A hypothetical cost analysis based on this repair procedure was provided herein, aiming to help direct several research gaps to be addressed in order to enable the repair of fire-damaged timber.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".