Feasibility of using reduced length superelastic shape memory alloy strands in post-tensioned steel beam–column connections
Bibliographic record
Abstract
Driven by a need to reduce repair costs and downtime in structures following a major earthquake, self-centering systems have been introduced. Post-tensioned high strength steel strands have shown promising results in providing self-centering capability in steel frames, where the beams are compressed to columns. This study aims at investigating the feasibility of using reduced length of steel and shape memory alloy strands in steel beam–column connections. Through finite element modeling, the study first evaluates the effect of using short-length regular post-tensioned strands in steel connections. The results show higher strength, stiffness, and energy dissipation capacity for connections with shorter length regular post-tensioned strands. The moment capacity and energy absorption capacity of a post-tensioned beam–column connection with one-third strand length were 105% and 114% higher than those of with full-length strands, respectively. However, residual drifts increased from 4 to 39 mm. To avoid loss in the re-centering capability of such connections due to yielding/failing of post-tensioned steel strands, the application of shape memory alloy and hybrid strands are proposed. The results show that shorter length shape memory alloy strands are effective in regaining self-centering and dissipating higher amount of energy compared to the full-length steel strands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".