Seismic Design Assessment of Bridge Piers Location Effect on the Structural Capacity of Supports under Earthquake Action
Bibliographic record
Abstract
The objective of this study was to assess the seismic performance of two types of bridges structures under effect of earthquake by using different locations and numbers of piers. The results of D/C ratio showed that simply supported I girder bridge appeared higher structural capacity than continuous box girder bridge which was resisted the seismic demand. Continuous box girder bridge had higher seismic demand and lower structural capacity comparing with simply supported I girder bridge. Commonly, the seismic design for two types of bridges models with increasing of piers numbers was suitable to resist the earthquake action for region type B. The results of non-linear static analysis (pushover method) showed that the increasing of piers numbers had significant effects on the seismic design of bridges structures to increase the displacement capacity, force capacity, and decreasing of seismic demand to reduce the effects of earthquake action on the bridges structural members. The bridge type simply supported I girder had higher capacity in longitudinal direction than continuous box girder bridge. Whereas, for continuous box girder bridge appeared higher capacity in transverse direction than simply supported I girder. The performance points which were based on displacement were decreased with increasing the piers numbers for bridges structures supports.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".