Performance of Fiber-Reinforced Ultra-High-Performance Concrete Incorporated with Microencapsulated Phase Change Materials
Bibliographic record
Abstract
In the era of environmental concerns, many attempts were proposed to optimize energy efficiency for buildings and consequently reduce their carbon footprint. As a sustainable approach, it is a promising solution to incorporate phase change materials (PCMs) in construction materials (i.e., ultra-high-performance concrete (UHPC)) to increase its thermal storage capacity and reduce the operation energy. However, incorporating microencapsulated phase change materials (MPCMs) into cementitious materials negatively impacts the fresh and hardened properties. UHPC’s improved mechanical strength allows for the creation of slimmer and lighter structures, which may result in less demand in concrete manufacturing and fewer emissions. Hence, the properties of UHPC incorporated with MPCMs (MPCM-UHPC) need more investigations. To fill the gap in the literature about the lack of information about MPCM-UHPC performance, this paper provides a comprehensive work to study the mechanical, thermal, and impact resistance properties of (MPCM-UHPC). Proportions of 5% and 10% of MPCMs were incorporated as a replacement of sand by volume. Proportions of 0.5%, 1.0%, and 1.5% of micro steel fiber reinforcement were used as a percentage of the mixture’s total volume. The results revealed the importance of fiber reinforcement in compensating for the negative effect of MPCMs inclusion for improving the thermal properties. Increasing the amount of MPCMs enhanced the thermal performance of the produced UHPC panels through the ability to absorb and release the energy during the phase change process.
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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) | 0.001 | 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 | 0.001 |
| 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".