In- and Out-of-Plane Bending in Steel Through-Truss Bridges
Bibliographic record
Abstract
Steel through-truss is a very common configuration for railroad and highway bridges. There is a large number of such spans in surface transportation infrastructure worldwide, especially in railway systems. In design and evaluation, these spans’ in- and out-of-plane bending have been either omitted or approximated. A fuller understanding of this effect will be able to assist in reliably designing and assessing these structures for longevity and/or increasing allowable service load to facilitate economic developments. A full scale load test is performed in this paper on five such bridges of the Canadian National Railway (CN) using train load. The main truss elements prone to in- and out-of-plane bending are identified and strain-gauged including the hanger (L1U1 or L’1U’1). Out-of-plane bending is seen to produce more significant flexural stress than in-plane bending. Three-dimensional (3D) numerical simulation is also verified by physical tests and covers other uninstrumented members of the tested spans. These results are used to evaluate the accuracy of a new and simplifying two-dimensional (2D) analysis method for the most significantly bent vertical hanger out-of-plane. The 2D method is shown to capture a significant portion of the bending but still underestimate flexural stress. An empirical and hybrid approach is therefore developed and recommended to address the inadequately accounted out-of-plane bending for routine practice of design and evaluation. It is needed when resources for detailed 3D analysis are not readily available, and/or when a quick and reliable method is needed, e.g., for verification or calibration of another method. These results are also useful for stress range estimation for fatigue analysis, although fatigue is not a concern to these bridges and is therefore not specifically addressed in this paper. CN has adopted the recommended method and the other research findings in load-rating their existing through-truss bridges.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".