Fatigue Behavior of Reinforced Concrete Bridge Decks under Moving Wheel Loads: A State-of-the-Art Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it