Experimental and Nonlinear Finite Element Analysis Data for an Innovative Buckling Restrained Bracing System to Rehabilitate Seismically Deficient Structures
Bibliographic record
Abstract
This article presents experimental data and nonlinear finite element analysis (NLFEA) modeling for an innovative buckling restrained bracing (BRB) system. The data were collected from qualification testing of introduced BRBs per the AISC 341 test provision and finite element modeling. The BRB is made of three parts: core bar, restraining unit, and end units, in which duplicates of three different core bar cross sections (i.e., fully threaded, threaded notched, and smooth shaved) were tested. The BRBs introduced in this research come with innovative end parts, so-called fingers. These fingers provide the longitudinal gap required in every BRB system and simultaneously prevent buckling of the core bar at the end regions at both ends of the BRB sample, thus facilitating an easy core replacement if it gets damaged in the event of an earthquake. The measured parameters were the applied cyclic load and the corresponding displacement. Analysis of the acquired data illustrated an almost symmetric hysteric behavior with a little higher capacity under compression but a noticeable overall ductility of 4. Moreover, finite element modeling data for one type of core bar (fully threaded) were curated. The data presented in this paper will be valuable for fabricating BRBs in practice and further research on the topic considered.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".