Low‐cycle fatigue performance of high‐strength steel rebars in concrete bridge columns
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Design codes often restrict the use of high‐strength steel (HSS) rebars in seismic applications due to their inferior low‐cycle fatigue performance when compared to conventional steel rebars. In this study, previously established low‐cycle fatigue life models of HSS rebars, namely ASTM A706 Grade 550 and ASTM A1035 Grade 690, were incorporated into a new cumulative damage model to identify conditions under which such rebars can achieve seismic performance comparable to that of benchmark ASTM A706 Grade 420 steel bars in concrete bridge columns. An ensemble of well confined flexure‐dominated circular concrete bridge columns located in high seismic regions was considered where the axial load ratio, and longitudinal and spiral reinforcement ratios were varied. The bridge columns were reinforced with ASTM A706 Grade 420, ASTM A706 Grade 550, and ASTM A1035 Grade 690 steel and numerically analyzed under different displacement ductility levels (2, 4, and 6), earthquake types (crustal and subduction), and ratio of hoop spacing to longitudinal bar diameter ratio (4 and 6). Their low‐cycle fatigue performances were compared based on the computed bar fracture and accumulated damage indices. Based on the reported observations, it is concluded that design codes are overly restrictive in not permitting the use of HSS in seismic application on the basis of inadequate low‐cycle fatigue life. As an alternative, this study proposes imposing certain limits on displacement ductility demand and ratio of hoop spacing to longitudinal bar diameter ratio to promote safe and efficient use of HSS rebars in concrete bridge columns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it