Post-fire investigations of prestressed concrete structures
Bibliographic record
Abstract
Concrete structures reinforced with prestressing steel, referred to as prestressed concrete (PSC) structures, are commonly used in bridge and tall building construction as they can easily be optimized. However, prestressing steel is well known to have complex behavior when exposed to high temperatures. To date, this structure’s high temperature behavior is not fully understood and can potentially complicate post-fire structural investigations of PSC structures. After a fire, it is often difficult to guarantee stability or specify necessary repairs for this type of structure. The study herein aims to help develop guidance for investigating and assessing PSC structures (with emphasis on bridge beams) post-fire. Investigations of specific and real PSC structures after fire exposure are reviewed as case studies herein. Characteristic damage indicators and test methods for assessment are discussed. The authors characterize the residual condition of a lab-scale PSC structure previously exposed to severe temperature. Non-destructive strength analyses of prestressing steel using a calibrated hardness test procedure were carried out. Results were compared to those from destructive methods and showed satisfactory correlation. The high temperature exposed concrete was also inspected. The paper concludes with recommendations of future research required to better identify the condition of PSC structures after fires.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".