Multi-fidelity modelling for uncertainty quantification of timber beam-column connections exposed to standard fire
Bibliographic record
Abstract
Fire safety design of timber structures requires a comprehensive uncertainty quantification to identify factors that potentially influence the structural fire performance. Prevalent finite element (FE) models, however, have high computational cost to be employed in the uncertainty quantification. This paper presents a multi-fidelity modelling framework for uncertainty quantification of timber beam–column connections exposed to standard fire test, aiming to predict the structural response with limited high-fidelity data points. First, the high- and low-fidelity FE models for sequential thermal-mechanical analysis are introduced. The fire resistance times of the connections with random input variables are evaluated by the high- and low-fidelity models separately. Subsequently, multi-fidelity neural networks (MFNNs) models are trained to correlate both high- and low-fidelity data. The numbers of high- and low-fidelity data used for training the MFNN are determined based on the model’s performance on the validation set. With limited high-fidelity data, the developed MFNN is demonstrated to be considerably accurate in predicting the fire resistance time and displacement evolution of the connection. Then the MFNN is used for the uncertainty quantification including sensitivity analysis, SHapley Additive exPlanations (SHAP) analysis and reliability analysis. The impacts of input variables on the connection’s fire resistance time are quantified. The failure probability of the connection under different load ratios are assessed based on Monte Carlo simulation (MCS).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".