Fracture Mechanics-Based Modelling of Post-Installed Adhesive FRP Composite Anchors in Structural Concrete Applications
Bibliographic record
Abstract
Adhesively bonded fibre-reinforced polymer (FRP) anchors have emerged as a progressive alternative to traditional steel anchors in concrete structures, owing to their superior corrosion resistance, high tensile strength, and light weight. Despite their increasing use, a robust mechanics-based bond model capable of accurately predicting the load transfer behaviour has not yet been developed. This study presents a fracture mechanics-based analytical bond model for post-installed adhesive FRP anchors embedded in concrete. The model formulation is derived from fundamental equilibrium and compatibility principles, incorporating a bilinear bond–slip law that captures both elastic and softening behaviours. A new expression for the effective bond length is also proposed. Validation of the model against a comprehensive database of direct pull-out tests reported in the literature shows excellent agreement between predicted and experimental pull-out forces (R2 = 0.980; CoV = 0.058). Future research should aim to extend the proposed model to account for confinement effects, long-term durability, the impact of adhesive type, and cyclic loading conditions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".