A Review of Ship Collision and Seismic Impacts on Scour-Affected Bridge Pile Foundations
Bibliographic record
Abstract
In recent years, the frequent collapse of bridges has underscored the severe threats posed by ship collisions and seismic forces to bridge pile foundations, particularly under scour conditions. Scour significantly increases bending moments, weakens foundation stability, and exacerbates damage under ship impacts and seismic loading. This review systematically examines the dynamic responses of bridge pile foundations subjected to multi-hazard scenarios, focusing on how scour-induced degradation exacerbates the impacts of ship collisions and seismic events. The synthesis covers experimental studies, numerical simulations, and theoretical approaches, providing a comprehensive evaluation of methodologies and findings. Advanced bibliometric tools, such as CiteSpace and VOSviewer, are employed to identify research trends, hotspots, and collaborations in this domain. Additionally, the review highlights the integration of intelligent technologies for mitigating ship collision risks and improving bridge safety management in scour-prone environments. By consolidating existing knowledge, this paper can serve as a critical reference for understanding the compounded effects of scour and other hazards on bridge pile foundations, offering guidance for future research and engineering practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".