Preliminary Modelling of Plasco Tower Collapse
Bibliographic record
Abstract
In a recent tragic fire incident, the Plasco Tower collapsed after an intense outburst of fire lasting for three and a half hours and claiming the lives of 16 firefighters and 6 civilians. This paper will present continuing collaborative work between Hong Kong Polytechnic University and Queen's University in Canada to model the progressive collapse of the tower. The fire started at the 10th floor and was observed to have travelled along the floor horizontally and through the staircase and windows vertically. Plasco Tower was steel structure and all the steel sections were fabricated by welding standard European channel or angle profiles and no fire protection was applied. Four internal columns carried the loads transferred by the primary beams, and box columns were constructed along the perimeter of the building as a braced tube for resisting seismic loading. OpenSees fibre-based sections and displacement-based beam-column elements are used to model the frames, while shell elements are used for the reinforced concrete floor slabs. The thermal properties and elevated temperature mechanical properties are as recommended in the Eurocodes. The results in this preliminarily analysis are based on rough estimations of the structure's configuration. The ongoing work looks at modeling the Plasco Tower based on the most accurate findings from reviewing many photographs and collected data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".