A new damage-control target displacement procedure for direct displacement-based design of circular reinforced concrete bridge pier
Bibliographic record
Abstract
In this paper, a new perspective procedure to determine the damage-control target displacement for circular reinforced concrete (RC) bridge pier is proposed by considering the new approach of damage-control limit states (DCLS).The new approach of DCLS is explored by integrating existing damage-control concrete strain limit with recently proposed damage-control reinforcement strain limit.Modification of yield displacement and modified plastic-hinge along with new DCLS is used to estimate the damage-control target displacement for a circular RC bridge pier.Three-dimensional (3D) finite element (FE) model has been developed to validate the damage-control target displacement subject to ground motion based on a nonlinear time-history (NLTH) analysis.The 3D FE model is updated to achieve a reasonable relationship between numerical, analytical, and outcomes found in the literature.It is worth noting that the proposed procedure manages to estimate an improved damage-control target displacement for 7.0 m, 11.0 m and 13.0 m height of circular RC bridge pier.The influence of new reinforcement limit strain along with both modification of yield displacement and plastic-hinge contributes to providing better results.The result shows that new DCLS were efficient to predict damage-control target displacement, consistent with FE analysis result.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".