Modelling of Strengthened Steel Connections under Static and Cyclic Loading
Bibliographic record
Abstract
The rehabilitation of steel structures with Fibre Reinforced Polymers (FRP’s) may appear less effective because they can be bolted or welded with steel plates that display the same mechanical properties. However, this technique has some unwanted consequences such as additional dead weight and an increased risk of corrosion. The aim of the proposed study, therefore, is to present a technique for modelling steel connections strengthened with FRP’s. Two types of composites: Carbon Fibre Reinforced Polymer (CFRP) and Glass Fibre Reinforced Polymer (GFRP) are considered. They are used to strengthen welded steel connections. The main objective consists in evaluating the effect of the reinforcement on the load-carrying capacity of these connections under monotonic and cyclic loadings. The steel is considered to behave in a linear elastic perfectly plastic fashion with isotropic strain hardening, and the FRP’s are assumed to behave linearly up to failure. The behaviour of the adhesive is modelled with the Cohesive Zone Model (CZM) available in Abaqus. Lastly, a parametric study is carried out to investigate the eventuality of strengthening connections made with I-sections, which are very common in practice.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".