Fatigue Behavior of Reinforced Concrete Bridge Decks under Moving Wheel Loads: A State-of-the-Art Review
Bibliographic record
Abstract
This article provides a comprehensive state-of-the-art review of research on bridge deck fatigue under moving wheel loads and compares it to conventional fixed-point pulsating load fatigue. An overview and a brief history of the evolution of this test method from around the world are provided. The effect of key parameters on fatigue life and performance under moving loads are discussed, including loading magnitude and stress ratio, loading footprint, boundary conditions, loading eccentricity, loading frequency, dynamic effect and impact, reinforcement layout, slab thickness, crack control, concrete strength, and environmental exposure conditions. The fatigue accumulation rule and the incremental step (staircase) rolling load method are discussed. Cracking and failure mechanisms in slabs under rolling loads are presented and compared. It is clearly demonstrated that fixed-point pulsating fatigue loads inadequately simulate fatigue damage, stiffness degradation, and cracking patterns induced by rolling loads. For example, one rolling load cycle is shown to be equivalent to 80–1,800 pulsating load cycles. Varying the magnitude of the rolling load (dynamic effect) further reduces the fatigue life. Decreasing the spacing of the transverse rebar and compression reinforcement both can increase susceptibility to crack initiation, potentially reducing fatigue life. Environmental factors, particularly moisture intrusion, drastically reduced fatigue life. A conversion factor of stiffness degradation from pulsating to equivalent rolling load fatigue is proposed. Finally, recommendations for future work in this field are proposed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".