Predicting Maximum Effective Temperatures and Thermal Gradients for Steel I-Girder in Canadian Climate Regions
Bibliographic record
Abstract
The constant fluctuation of thermal loads on steel members, especially during construction, causes non-uniform distributions of temperatures, resulting in possible constructional and structural defects leading to unfavorable thermally induced responses and potential safety risks. The Canadian Highway Bridge Design Code (CHBDC) provides one thermal gradient variation profile without accounting for the differences in the geometrical parameters of the steel members and the variations in the climate regions of Canada. Therefore, in this study, three-dimensional finite element (FE) thermal simulations were conducted to investigate the maximum effective temperatures and positive vertical thermal gradients for different Canadian climate regions. Parametric studies were performed to conduct the FE thermal analysis using the thermal model validated in ANSYS. The comprehensive study results showed that Canada could be divided into two main zones for vertical thermal gradient calculations, meaning that one stationary thermal gradient profile cannot be applicable to all climate regions of Canada, as recommended by the CHBDC. Based on the FE thermal analysis results, empirical formulas as a function of the significant parameters were proposed to predict the maximum effective temperature and thermal gradient variations of the steel I-girder. The predicted maximum effective temperature and thermal gradient variation values were found to be highly correlated with the FE values with coefficients of determination R2 of approximately 0.97 and 0.98, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".