Assessing Effectiveness of Shape Memory Alloys on the Response of Bolted T-Stub Connections Subjected to Cyclic Loading
Bibliographic record
Abstract
This study presents finite element analysis of double split tee (DST) connections with high-strength steel bolts and coupled split tee sections, to evaluate various cyclic response parameters and elements. The investigation included quantifying connection behavior and hysteretic response, damage indexes, and failure modes. Over 40 specimens were simulated in ABAQUS under cyclic loading, including shape memory alloy (SMA)-built specimens. In the post-analysis phase, the T-stub thickness, the T-stub yield strength, the bolt preload and bolt number, and the stiffener type and stiffener material for the most significant parts of the DST connection were calculated. Simulation results showed that a lower ultimate moment yielded fewer needed stem bolts. The energy dissipation (ED) capacity increased as the horizontal distance between the stem bolts decreased. Additionally, increasing the strength of the bolt and T-stub by 15% resulted in a 3.86% increase in residual displacement (RD) for the bolt and a 1.73% decrease in residual displacement for the T-stub. T-stub stiffeners enhanced ED capacity by 31.7%. SMA materials were vulnerable to mode 1 failure when used in T-stubs, bolts, or stiffeners. However, the use of SMA increased the rate of energy dissipation. Adding stiffeners to the T-stubs altered the failure indexes and improved the pattern of failure modes. In addition, stiffeners decreased the rupture and pressure indexes. As a result, the failure index of a T-stub shifted from brittle failure to ductile failure.
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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 | 0.001 | 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".