<b>Analyzing the Nonlinear Finite Element Behavior of FRP </b> <b>Composite Electrical Structures in Flexural Loading </b>
Bibliographic record
Abstract
Finite Element Analysis of Flexural Behavior in Full-scale Tapered FRP Pole Structures: Influence of Fiber Orientations, Circumferential Layers, and Carbon Fiber Substitution. In this study, we present a finite element modeling analysis of the nonlinear behavior of laterally loaded full-scale tapered fiber-reinforced polymer (FRP) pole structures. The study explores the impact of various parameters, including fiber orientations in longitudinal and circumferential layers, the number of circumferential layers, and the substitution of glass fiber with carbon fiber in the FRP pole compositions. The FRP poles in question were manufactured using the filament winding technique, with E-glass fiber and epoxy resin as the primary materials. Our analysis results exhibit a significant correlation between the finite element analysis and experimental data, emphasizing the critical role of fiber orientation in determining flexural behavior. The findings underscore the advantages of incorporating circumferential layers and highlight that enhanced strength can be achieved by incorporating both outer and inner circumferential layers alongside longitudinal layers. Moreover, substituting carbon fiber for glass fiber in the FRP poles results in notable improvements, with increased total load capacity and stiffness as the percentage of carbon fibers rises.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".