A numerical investigation of overstrength and ductility factors of moment resisting steel frames retrofitted with GFRP plates
Bibliographic record
Abstract
This paper reports the results of an investigation conducted to assess the effectiveness of using glass fiber reinforced polymer (GFRP) plates to enhance the overstrength and ductility factors of moment resisting steel frames. The GFRP plates are bonded to the flanges of steel beams of the frame with an aim to enhance their local buckling capacities and consequently their ductility. The flexural behaviour of GFRP retrofitted beams is first determined using a nonlinear finite element model developed in-house. In this numerical model, consistent shell elements are used to simulate the flanges and web of the steel beam as well as the GFRP plate. The interface between the steel and the GFRP plate is simulated using a set of continuous linear spring system representing both the shear and peeling stiffness of the adhesive based on values obtained from a previous experimental study. The moment–rotation characteristics of the retrofitted beams are then implemented into the frame model to carry out nonlinear static (pushover) analyses. The seismic performance level of the retrofitted frames in terms of overstrength and ductility factors is then compared with that of the bare frame. The results show a significant enhancement in strength and ductility capacities of the retrofitted frames, especially when the beams of the frame are slender.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".