BRIDGE SPAN DISLODGEMENT DURING EARTHQUAKES - MECHANISMS AND ITS PREVENTION
Bibliographic record
Abstract
Bridges are one of the lifeline structures and their uncertain or sudden failure can catastrophically affect life, transport system, supply chains, and more. A common form of damage in these structures is due to unseating of the span when bridges are subjected to earthquakes. Numerous past earthquakes have shown that the bridge superstructure and substructure remained nearly undamaged. However, due to unseating of the span in any of the longitudinal, transverse or rotational directions, the bridge becomes unsuitable for use. This paper collates the causes of bridge span dislodgement through examples from past earthquakes and illustrates the mechanisms involved in span dislodgement in various types of bridges under various seismic hazard scenarios. Many national and international codes of practices like ASSHTO, CALTRANS, EUROCODE, JRA, New Zealand Code, Canadian Code, Australian Code, Indian code (IRC and IRS), etc. include provisions to prevent bridge deck unseating. The prevention technique can mainly be divided into two categories; (a) the provision of minimum support length (MSL) at the abutment/pier cap; and/or (b) provision of unseating prevention devices. The provision of MSL is easy to implement during construction. The provisions of MSL by various codes of practices are presented in this paper through an illustrative example to understand the influential parameters, and the differences that exists in international 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".