Bond-Slip Analytical Formulation toward Optimal Embedment of Concrete-Filled Circular FRP Tubes into Concrete Footings
Bibliographic record
Abstract
This paper presents a robust analytical model for a moment connection of concrete-filled fiber reinforced-polymer (FRP) tubes (CFFTs) to concrete footings. The CFFT connection is based on a simple approach of direct embedment into the footing, thereby eliminating the need for connection rebar or mechanical devices. The CFFT is externally subjected to lateral and axial loads, resembling practical applications such as piles affixed to pile caps, bridge columns, or utility poles. The model adopts the concepts of equilibrium, deformations compatibility, and nonlinear concrete stress-strain behavior. It also employs a “bond stress-slip” relation that can be obtained from simple push-through tests on some of the commercially used tubes. The model can predict the critical embedment length Xcr , which is the minimum length required to achieve material failure of the CFFT outside the footing, and bond failure inside the footing, simultaneously. If the actual embedment length is less than Xcr , bond failure occurs prematurely at a lower strength that can also be predicted by the model. The model was verified using experimental data and showed that Xcr was only 0.7 of the diameter for that case. A sensitivity parametric analysis was carried out that led to some approximations. Based on which, a simple closed-form expression was established for Xcr in the case of lateral loading only.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".