Advances towards Multihazard Hybrid Simulation of Buildings in Fire and Fire Following Earthquake
Bibliographic record
Abstract
Hybrid simulation is an innovative testing technique which takes both advantages: the efficiency in numerical modelling and accuracy of physical test.This research presents a framework for assessing the risks of large-scale structures in fire and fire following earthquake through hybrid simulation.Full interactions between the thermal and mechanical behaviour of the structures are considered in the assessment.In the proposed framework, the element of the prototype structure that is exposed to the sequence of fire loads is selected as physical domain for physical test while the remainder structure as numerical domain is numerically modelled.An illustrative example of the building exposed to fire hazard is presented to demonstrate the hybrid fire simulation methodology.For the multi-hazard risk, a numerical study on the performance of a 4-storey steel moment resisting frame subjected to fire following earthquake is also presented in this research.The results show that the sequential combination multi-hazard effect of fire following earthquake causes more severe damage to the example building when correspond to the case of damage due to fire hazard alone.The results from the fire following earthquake numerical simulation example contribute to the development of the multi-hazard hybrid simulation technique in future studies.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".