Finite Element Analysis Workflow for Heat Straightening of Impact-Damaged Steel Bridges
Bibliographic record
Abstract
Impacts between over-height vehicles and steel bridges are common throughout North America and the resulting bridge damage is unpredictable and sudden. Heat straightening is an alternative to replacing steel members and mechanical straightening for repairs. In many cases, bridges remediated using heat straightening require fewer disruptions to traffic and lower repair costs compared to repairs replacing steel members. Heat straightening limitations and practices have been developed with American steel grades and climates in mind. Often heat straightening is described as an art as much as a science; this repair method has relied on experienced practitioners using heat straightening patterns designed to cause rotations or shortening in members. \nHeat straightening of impact-damaged steel bridges can be modelled with finite element analysis (FEA). Heat straightening is a thermo-mechanical process. Existing studies modelling heat straightening with FEA do not fully explain the modelling techniques and material parameters used. A workflow defining steps and material parameters can be used to facilitate modelling of impact and heat straightening on bridges. \nThis study proposes a workflow using FEA to model the heat straightening of impact-damaged steel bridges. The proposed workflow will be used to study heat straightening of CSA 350W steel – a commonly used Canadian steel grade. The workflow is developed by investigating modelling techniques for impact and heat straightening separately. The developed finite element models include material parameters accounting for work hardening, thermal effects and strain-rate sensitivity of steel. \nThe presented research demonstrates that the proposed workflow is viable for modelling impact and heat straightening of steel bridges. Although this study involved the application of the developed modelling techniques for a hypothetical bridge, the exercise has provided valuable insight into methods of expediting heat straightening repair and the modelling process. For instance, it has provided insight into the following: the introduction of jacking forces without causing mechanical straightening, the removal of plastically deformed stiffeners to reduce deformations, and the treatment of residual stresses from heat straightening.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.019 |
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".