Limit state behavior and response sensitivity analysis of endplate steel connections with shape memory alloy bolts
Bibliographic record
Abstract
Shape memory alloys have been used in developing self-centering steel moment connections. This article presents a numerical study aiming at evaluating the cyclic response sensitivity and limit states of extended endplate steel connections with shape memory alloy bolts. Three-dimensional finite element models are developed and validated against a recent experimental study. Using a statistical design-of-experiment method, the effects of 21 design factors and their interactions on the cyclic response of shape memory alloy connections are assessed. The sensitivity of six response parameters is studied. In addition, four limit states for shape memory alloy connections are discussed, including beam local buckling, bolt excessive axial strain, endplate yielding, and column flange yielding. Results show that endplate thickness, shape memory alloy bolt diameter, beam web slenderness ratio, and shape memory alloy maximum transformation strain are the most influential factors. Furthermore, endplate yielding is found to be the governing limit state in almost 80% of the analyzed connections, whereas shape memory alloy bolt excessive strain and column flange yielding are observed in less than 20% and 5% of the connections, respectively. Beam local buckling is not governing in the analyzed shape memory alloy connections designed as per the AISC 358-16 and AISC 341-16 seismic design requirements for extended endplate connections and highly ductile members.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".