Bibliographic record
Abstract
Purpose The collapse of a structure resulting from the instability of steel frames due to fire is the worst failure mode to consider in fire-structural engineering, and should be avoided. The purpose of this paper is to propose a new method for estimating the minimum possible duration of a fire event that could result in the instability of an unbraced steel frame. Design/methodology/approach The proposed method is in the form of a constrained minimization problem that determines the worst case fire scenario that can cause instability of a structure, and is solved using nonlinear constrained mathematical programming algorithms. The formulation is demonstrated via a numerical example. Findings For frames subjected to fire events modelled with monotonically increasing fire curves, the worst case fire causing instability of a frame is always one where all of the compartments catch fire at the same time. For frames subjected to fire events where fire curves decay, the minimization problem must be solved rigorously. The results are significantly affected by the fire curves and amount of insulation applied to each member. Originality/value The proposed method is an extension of a method previously established by Xuet al.(2018) to assess the stability of unbraced steel frames subjected to elevated member temperatures. The previous method does not consider fire duration and heat transfer mechanics, which are included in the proposed method. The proposed method is potentially useful for designers in conducting fire scenario analysis in the performance-based design of structures.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".