Fatigue Testing and Finite Element Analysis of Bridge Welds Retrofitted by Peening under Load
Bibliographic record
Abstract
Residual stress-based post-weld treatments such as needle peening, hammer peening, and ultrasonic impact treatment (UIT) offer a promising means for extending the fatigue lives of existing welded highway bridges. When applied to bridge welds in service, these treatments can be particularly effective, since the stresses due to the self weight of the bridge have already been imposed. In this paper, a finite element (FE) analysis study is performed to investigate the additional benefit that may result from applying post weld-treatments under load. Fatigue tests of small-scale weld specimens treated with and without preloading are first described. 2D FE models that simulate the treatment process are then described and used to model the treatment of the fatigue specimens. Effects of plate thickness, indentation depth, and preload level on the residual stress distribution induced by peening under load are then studied. Based on the results of this work, recommendations are made to aid in the prediction of the fatigue performance of bridge welds retrofitted by peening under load.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".