Experimental and numerical investigation on the performance of GFRP-confined expansive concrete-filled unplasticized polyvinyl chloride tubes
Bibliographic record
Abstract
This paper describes both an experimental study and a numerical investigation on the compressive behavior of new types of solid as well as hollow composite columns, which consist of Unplasticized Polyvinyl Chloride (UPVC) tubes filled with expansive concrete. 32 specimens were categorized into four groups and cast with expansive concrete. Half of the specimens were confined additionally with two layers of GFRP wraps. Furthermore, two specimens were cast with ordinary concrete to be compared with those expansive counterparts. All the composite columns were tested under the monotonic compressive loading. The effects of GFRP confinement, aspect ratio of composite columns ( L/ D), and hollowness of concrete core on compressive behavior were investigated. It was demonstrated that adding expansive agents improved the load-carrying capacity and ductility of columns by enhancing the tri-axial state of stress in the concrete core. Moreover, an increase in aspect ratio decreased both peak load and its corresponding axial strain. Approximately 40% increase was observed in the values of peak load for specimens confined with GFRP wraps. Generally, concrete core removal in the hollow specimens resulted in slightly higher average compressive stress. Finally, a finite element (FE) simulation was performed using ABAQUS software, and the numerical results were validated with experimental tests of the present study.
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 | 0.001 |
| 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 | 0.001 |
| 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".