Experimental investigation of the effect of aging on perforated composite tubes under axial compressive loading
Bibliographic record
Abstract
The use of fiber-reinforced polymer (FRP) composite materials in various applications such as aerospace, automotive, sport equipment, and oil and gas industries has been growing in a steady rate in recent years. The potential use of perforated FRP tubes (pipes) in oil and gas industry-related applications can become significantly greater, provided that the influence of the harsh environmental conditions specific to the industry could be tolerated by the materials used to form such tubes, with minimal degradation to system’s mechanical and physical properties. Unfortunately, there is not adequate database and information in the literature in regards to the long-term response of perforated FRP tubes. The purpose of this study is therefore to investigate whether FRP could be confidently used in structural applications that are primarily subjected to compressive loading and exposed to harsh environments, without significant deterioration of their physical and mechanical properties. For that, three sets of perforated glass fiber-reinforced plastic (GFRP) pipes were fabricated and subjected to accelerated aging conditions in an acid. Subsequently, the pipes were tested to failure under an axially applied compressive load. Results showed a considerable decrease in the load carrying capacity and axial stiffness of perforated pipes having certain D/t ratios, as a result of the aging.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".