STR-866: NON LINEAR FINITE ELEMENT MODEL FOR POST-EARTHQUAKE FIRE PERFORMANCE EVALUATION OF STEEL PORTAL FRAMES
Bibliographic record
Abstract
Post-earthquake fires (PEF) especially in densely populated urban areas have been catastrophic in recent seismic events. It appears to be an important design load which has not been considered critical by most design standards. Moreover, current performance-based seismic design philosophy permits certain level of damage to a structure based on the assumed design seismic hazard. These damaged structures are extremely vulnerable to post-earthquake fires. Even after the outbreak of fire, the structural integrity of the damaged structure must be intact for sufficient duration enabling the firefighters to evacuate and extinguish the fire in the affected building. The recent performance-based design, necessitates evaluation of the fire resistance level of earthquake damaged building with or without the outbreak of post-earthquake fire. In this study an integrated seismic and thermal analysis model was developed using the sequential thermal–structural analysis scheme using the finite element program, ABAQUS. A simple portal frame was considered to investigate the global behaviour of the frame and determine post-earthquake fire resistance. A 2D transient heat transfer analysis was conducted and the transient nodal temperatures across the structural elements cross sections were stored for subsequent thermal structural analyses. The state of earthquake inflicted damage, corresponding to desired performance level was realized using pushover analysis. The results of the simplified 2D model matched reasonably well with that of 3D finite element model considered for validation study. The developed model is being used for subsequent study to investigate the multi-story moment resisting frames with fire scenarios resulting in asymmetric heating of the frame.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".