Simplified structural analysis of laminated glass panels during fire exposure
Bibliographic record
Abstract
Due to their high aesthetic value, energy-efficient properties, and contribution to daylighting, the demand for using glass panels in modern buildings has considerably increased over the past decades. However, ordinary glass panels are highly susceptible to cracking during a fire because of the temperature difference between the part of the glass exposed to the fire and the part protected by the frame. Damage to the glass can allow additional oxygen intake, leading to the flashover phenomenon significantly increasing fire severity. Laminated glass is superior to ordinary glass in its impact resistance, sound insulation, and ability to maintain post-breakage integrity. This paper provides a simplified method to study the effect of temperature gradients on the cracking behaviour of laminated glass panels. The temperature of the unprotected portion of the glass panel is first estimated by evaluating the mid-thickness temperature using the general heat transfer equation. Then, equations developed based on a parametric study that utilized ABAQUS are proposed to estimate the exposed and unexposed surface temperatures. This step was followed by evaluating the temperature of the protected glass portion. Subsequently, a method based on strain-equilibrium principles was developed to predict the corresponding maximum thermal stress. Comparisons with experimental and numerical work by others validated the proposed method.
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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 | 0.000 | 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) | 0.001 | 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".