Finite Element Analysis of Flat Plate Structures in Fire
Bibliographic record
Abstract
Understanding the structural behaviour of flat plate systems during fire exposure is critical for ensuring the safety of occupants and emergency personnel. Flat slabs, a widely used structural system, undergo significant thermal deformations in fire, which increase demands on supporting columns and reduce the stiffness and strength of concrete and steel. While experimental fire tests have provided valuable data to understand the behaviour of isolated components of flat slabs, numerical analysis is the only route to comprehending the structural behaviour of full-scale flat plate structures during fire exposure. ABAQUS is commonly used for modelling reinforced concrete (RC) structures under fire, with two prevailing techniques: (1) solid element modelling for concrete and truss reinforcement and (2) shell element modelling with embedded steel layers and line-column elements. However, uncertainties remain regarding the influence of modelling parameters such as dilation angle and concrete tensile stress, and the impact of surface fire exposure has not been comprehensively studied. This study presents a novel contribution by conducting a detailed numerical investigation of a full-scale flat plate structure exposed to fire using both modelling approaches. The shell-element model was validated against experimental data and used to evaluate the effect of dilation angle and tensile strength assumptions. A unique aspect of this work is the assessment of fire exposure on different slab surfaces, including bottom, top, and both, which provides insights into slab deflections and column displacements under different surface fire exposure scenarios. The structure was then modelled using solid elements to systematically compare modelling techniques. The results highlight key differences between approaches and guide for selecting the most suitable modelling strategies for fire-exposed flat plate systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".