Assessing Effectiveness of Shape Memory Alloys on the Response of Bolted T-Stub Connections Subjected to Cyclic Loading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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