Fatigue Testing and Analysis of Aluminum Welds under In-Service Highway Bridge Loading Conditions
Bibliographic record
Abstract
For the fatigue design of aluminum structures, most applicable international codes specify fatigue-resistance (S-N) curves with slopes that vary, depending on the detail category. This complicates the selection of appropriate damage equivalence factors for use in highway bridge applications. The existing codes also differ in their treatment of high cycle fatigue, with single-slope S-N curves specified in some cases and multislope curves specified in others. In this paper, a recent investigation conducted to examine the fatigue behavior of aluminum welds under in-service highway bridge loading conditions is summarized. Specifically, calculations performed to establish damage equivalence factors for aluminum for use with the AASHTO and Canadian Standards Association (CSA) CAN/CSA-S6 codes are first reviewed. Following this, small-scale fatigue tests of aluminum welds under simulated highway bridge loading conditions are described. A fracture mechanics model is then validated by comparison with the test results and used to perform simulations encompassing a wider range of loading conditions. On the basis of this work, the adequacy of the current design provisions is discussed and possibilities for further extending the employed methodology are identified.
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) | 0.002 | 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".