Fatigue Analysis of Peened Bridge Welds under Realistic Service Loading Conditions Including Periodic Overload Events
Bibliographic record
Abstract
Post-weld treatment by various peening methods (i.e. needle or hammer peening, ultrasonic impact treatment) has received recent attention as a promising means for extending the fatigue lives of existing steel bridges or for enhancing the fatigue performance of new welded civil infrastructure. Peening treatments work primarily by introducing compressive residual stresses near the treated surface, which have the effect of reducing crack growth rates at the smaller crack depths. Recent research [1–4] has examined the effects of variable amplitude loading conditions on the fatigue performance of welds subjected to various peening treatments. This research has included fracture mechanics analysis using various models and test-based studies considering a variety of stress histories. In general, it has been observed that stress histories containing periodic compressive overload events can result in a reduced treatment benefit. The benefit of the treatment is predictable though and may still be substantial. Herein, a strain-based fracture mechanics model, validated elsewhere [2], is used to study the effects of needle peening to enhance the fatigue performance of steel bridge welds under realistic service loading conditions. To do this, a typical transverse fillet weld detail is analyzed under several load histories generated by simulating measured truck data passing across influence lines for the critical locations on bridge girders with two typical configurations (simply supported and two-span continuous). Following this, an additional series of analyses is performed to investigate the influence of stress histories for highway bridge welds subjected to periodic overload events that may occur, for example, due to the passage of trucks with axle loads that exceed the legal limit or convoys of heavy trucks. Based on these analytical studies, it is found that peening treatments can result in a significant fatigue life increase under realistic service loading conditions, including those containing periodic overloading events.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".