Performance of Ultra High Strength Concrete Expose to High Rise Temperature
Bibliographic record
Abstract
Fire or high temperature is a serious issue to ultra-high-strength concrete (UHSC). Strength reduction of UHPCs may amount to as high as 80 percent after exposure to 800℃. A sum of four UHSC mixes was synthesized and evaluated in this study after getting exposed to extreme temperatures that reach 1000°C. Steel and polypropylene (PP) fibers were used in this experiment. A total of four mixes were made of UHSC without fibres as a control mix (UHSC-0), UHSC with 2% steel fibres (UHSC-S), UHSC with 2% PP fibres (UHSC-P) and UHSC with 1% steel fibres + 1% PP fibres (UHSC-SP). Workability, direct tensile strength, compressive strength, and splitting tensile strength were examined. Particularly, emphasis was devoted to explosive spalling since UHPCs are typically of compact structure and hence more prone to explosive spalling than other concretes. It was determined that the mixture UHSC-SP had high fire resistance. Following exposure to 1000℃, this mixture preserved a residual compressive strength of 36 MPa, splitting tensile strength of 1.62 MPa and direct tensile strength of 0.8 MPa. On the other hand, UHSC-P also had good fire resistance while UHSC-0 and UHSC-S experienced explosive spalling after heating above 200ᴼC. The incorporation of steel fibers in UHSC-S and UHSC-SP mixtures reveals higher tensile and compressive strength findings at different elevated temperatures as compared to UHSC-0 and UHSC-P. In addition, the result of direct tensile strength appears to be lower than splitting tensile strength at different raised temperatures.
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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".