An intelligent semi-active bridge pier protection system with MR-STF damping and TSO–DBO optimized fuzzy control
Bibliographic record
Abstract
To address the limitations of traditional ship–pier anti-collision systems, which often fail to simultaneously protect both structures and vessels and lack adaptive control, this study proposes a novel semi-active intelligent anti-collision system. The system integrates a progressive magnetorheological shear-thickening fluid damper into a conventional fender structure, enabling adaptive response to varying impact conditions. A variable-universe fuzzy proportional–integral–derivative control scheme, enhanced by an inverse hyperbolic sine scaling mechanism, is developed to govern the damper's response. To further optimize control parameters and membership functions, a hybrid metaheuristic algorithm combining Tuna Swarm Optimization (TSO) and Dung Beetle Optimization (DBO) is proposed. Numerical simulations under different ship sizes (500 DWT and 3000 DWT) and collision speeds (2, 3, and 4 m/s) demonstrate that the proposed control strategy significantly reduces peak bow displacement by up to 19.94% and increases the output damping force, reaching over 70% of the impact load in some scenarios. Benchmark tests confirm the superior optimization performance of the TSO–DBO algorithm compared to several established algorithms. The proposed framework offers a practical, energy-efficient, and effective solution for mitigating ship–pier collision impacts, with strong potential for real-world engineering applications.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".