Evaluation of Existing Bond‐Slip Relations for CFRP‐Steel Joints and New Model for Linear and Nonlinear Adhesives
Bibliographic record
Abstract
Existing bond‐slip (τ‐s) relations for fibre‐reinforced polymer (FRP)‐steel joints employ different shapes and mathematical expressions, inferring that their predictions of failure load and mode, and other interface properties, might be inconsistent or inaccurate. In this study, predictions of four widely used τ‐s relations are evaluated using a large experimental database of 78 double‐lap FRP‐steel specimens. To facilitate the evaluation process, a robust finite element (FE) model is developed for each test, implementing data from either of the existing τ‐s relations to define the FRP‐steel interface. Comparisons between test and FE results indicated that the existing τ‐s models were unable of predicting the ultimate load (Pu) and effective bond length (Leff) of FRP‐steel joints, or the relation between Pu and bond length and that between Leff and FRP modulus (Ef). A new τ‐s model is developed based on an inverse FE simulation, comparison with experimental results, and regression analysis. It considers the effects of Ef, the type of FRP reinforcement (sheet or plate), and applies to both linear and nonlinear adhesives. The model predictions were validated by comparing with results from small bond tests and large FRP‐strengthened steel beams tested under bending, yielding excellent results for Pu, failure mode, and all other interfacial properties.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".