Exploring Fire Design in Canada: A Comparative Study of Probabilistic and Deterministic Approaches with Focus on FEA Modeling of Steel and UHPC Structures
Bibliographic record
Abstract
This thesis delves into the challenges of Fire Safety Engineering (FSE) in Canada, particularly addressing the modelling barriers to the analysis of steel and ultra-high-performance concrete (UHPC) structures in Canadian fire design. Discussing probabilistic and deterministic methodologies, it scrutinizes finite element analysis (FEA) for steel structures and UHPC. Addressing fire-induced failure risks, one study delves into mechanical degradation at elevated temperatures from design standards and current literature. Leveraging fire dynamics and material equations to produce fragility curves, pinpointing risks from structural deflections owing to thermal gradients. Such a strategy highlighted the effectiveness of simplified assumptions in modeling to derive a fragility function of a steel element. It expanded on the role of risk analysis and failure prediction for understanding finite resource allocation. An additional assessment of a steel beam column assembly focused on localized fires. Utilizing FEA, thermal boundary conditions and mechanical deflections were produced, spotlighting the uniqueness and challenges of the thermal gradients. The ability of the practitioner in such modelling is accentuated, both in result interpretation and in the application of experimental data. The study also underscores FEA's constraints, discusses the benefit of increased fidelity in input data and an increased understanding of model assumptions. Finally, a study on UHPC at escalated temperatures is conducted, it navigated UHPC behaviour and concrete damage plasticity model parameters. Via parametric calibration, optimal modeling parameters were found compared to empirical stress-strain curves at elevated temperatures. The study advocated for further research avenues to identify various UHPC properties at increased temperature, such as a probing of UHPC’s cyclic nature, and an incorporation of spalling in the model. Lastly, the thesis unveiled barriers in fire modeling and proposed specific solutions, underscored the necessity for constant adaptation in FSE and advocated for practitioners skilled in modern tools and techniques, enhancing the growth of fire safety engineering in Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".