Structural characterization of UHPC pipes under transverse compression using digital image correlation
Bibliographic record
Abstract
Ultra-High Performance Concrete (UHPC) has the potential for thin-walled structural applications, including pipelines subjected to external overburden loads. This pioneering study focused on performance evaluation of UHPC pipes under transverse compression load using digital image correlation (DIC). The parallel plate test method was used in accordance with ASTM Standard D2412–21. The parameters investigated were type of fibers (Polyoxymethylene (POM) and Steel), fiber content by volume (0 %, 1 % and 2.5 %) and internal circumferential steel reinforcement ratio (0 % and 1.07 %). The results showed that steel fibers significantly increased load capacity and stiffness compared to POM fibers. A pipe with 1.5 % steel fibers and no steel mesh reinforcement has an equivalent strength to that reinforced by 1.07 % steel mesh with no fibers. A simple design equation is developed for the strength of UHPC pipes based on a nonlinear finite element model and a parametric study. Half of the UHPC pipes analyzed met the requirements of ASTM C1765 ultimate load classes IV and V as well as AS/NZ 4058 classes 6 and 8. The parametric study demonstrated the effectiveness of UHPC pipes relative to conventional reinforced concrete (RC) pipes. For example, a UHPC pipe with only 1 % steel fibers achieved the same ASTM C76 Class IV rating as an RC pipe of the same inner diameter but with a range of wall thicknesses 63–156 % larger than that of the UHPC pipe.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".