Novel Curve-Shape Sandwich Composites with Flexible Cores for Rehabilitation of Buried Infrastructure: Experimental and Analytical Studies Considering Geometric Nonlinearity
Bibliographic record
Abstract
This paper presents findings from an experiment examining a novel curve-shape sandwich composite made of fiber-reinforced polymer (FRP) facesheets and flexible cores under transverse compressive loading. The curve-shape sandwich composites aim to enhance strength and stiffness while minimizing material use, particularly as liner for rehabilitation of large buried infrastructure like pipes and culverts. The study involved fabricating and testing 24 circular liners with various facesheet–core combinations. Results include deflection measurements, load data, and tensile strain values at different points on the liners. The stiffness of each sandwich specimen was compared to theoretical predictions based on composite facesheet behavior. Notably, bulkermat cores demonstrated superior stiffness and strength compared to three-dimensional (3D) woven fabric cores, exhibiting higher composite action. In contrast, solid-wall liners exhibited greater deformations than sandwich liners. To predict these significant deformations, an iterative analytical model was developed, accounting for geometric nonlinearity. This model accurately predicted test data prior to any material nonlinearity, such as facesheet or core failure. Additionally, the model was used to perform a parametric analysis, exploring various liner characteristics, including diameter, FRP layers, core thickness, and liner shape.
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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) | 0.000 | 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) | 0.001 | 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".