Thermal behaviour of unstressed and stressed high strength concrete containing polypropylene fibers at elevated temperature
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate high strength concrete (HSC) containing polypropylene fibers (PP-fibers) at high temperature under a compressive load. Design/methodology/approach The use of PP fibers in HSC is known to reduce and at times eliminate the risk of spalling. HSC containing 0, 1 and 2 kg/m3 of PP-fibers were subjected to various temperatures from 20°C to 150°C, 300°C and 450°C and evaluated in a “hot condition”. One group of specimens was in a non-stressed condition during heating (unstressed hot), while a second group was subjected to an initial preload of 40 per cent of the room temperature compressive strength during the heating (stressed hot). Findings Results showed that stressed concrete containing PP-fibers had lower thermal gradients (the temperature difference between the surface and center temperatures as a function of radial distance) and a decrease in relative porosity. However, the compressive strength of stressed specimens was improved with or without fibers as compared to that of the unstressed HSC. The increased stress levels due to concrete expansion at elevated temperature were also reported. The PP-fibers did not have a significant effect on the compressive strength of stressed concrete as compared to the unstressed state. Originality/value This paper reports the compressive strength of PP-fibers in HSC at elevated temperature with and without a pre-load.
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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) | 0.001 | 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".