Testing and optimisation of resilient deck-to-pier connections for tsunami-prone coastal bridges
Bibliographic record
Abstract
This paper presents the testing and optimisation of resilient deck-to-pier connections for tsunami-prone coastal bridges to mitigate the common superstructure washout failure mode. The deck-to-pier connection consists of replaceable bars designed to increase the structure’s overturning moment capacity and protect against superstructure washout. Experimental testing on a one-third scale bridge deck, pier, and connection subassembly was conducted to assess the performance of the three bar materials: galvanised mild steel, stainless steel and glass fibre-reinforced polymer. The experimental results verified that the connection increased the superstructure’s resistance to uplift and overturning moments, with minimal damage to the sub- and superstructure. Furthermore, three materials suitable for coastal environments were investigated, which provided different degrees of energy dissipation, overturning moment capacity, corrosion resistance, and consistency of performance across the entire wave loading cycle. An optimised connection was developed to improve the connection response to the tsunami loading. Testing of the optimised connection identified that the material properties can be exploited to: (i) increase the overturning moment capacity and energy dissipation and (ii) reduce bar damage and increase the superstructure displacement capacity compared to the standard connection.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".