Enhancing mechanical performance and microstructure of reactive powder concrete through optimization of high-temperature curing regimes
Bibliographic record
Abstract
Curing regimes significantly affect the mechanical properties of reactive powder concrete (RPC), particularly under high-temperature exposures. This study introduces a novel investigation into the impact of various high-temperature curing regimes on the mechanical properties (compressive strength, split tensile strength, and flexural strength) and microstructural investigations of reactive powder concrete (RPC). In a novel way, we investigate RPC formulations with different ratios of steel fiber, water/binding (w/b) ratios, and slag amount under four different curing conditions: standard room curing, steam curing, hot-temperature curing for 12 h after 3 days of steam curing, and 24 h after 3 days of steam curing. Test results indicated that the RPC of the mix containing 2 % steel fibres, 0.3 slag content, and 0.18 w/b ratio showed a higher strength at 24 h of hot-temperature curing. The hot-temperature 24-hour curing regime has considerably improved RPC's 28-day compressive, flexural, and split tensile strength by 177–183 %, 136–143 %, and 124–142 %, respectively. Microstructure investigations were also carried out using a scanning electron microscope to better understand the microstructure of different mixes. The development of secondary hydrated products such as tobermorite, was found due to high-temperature curing, which resulted in compact microstructure and improved strength. In conclusion, this study not only emphasizes the critical influence of curing regimes on RPC's mechanical performance but also shows that high-temperature curing can significantly improve RPC's overall strength and structural integrity, paving the way for its use in demanding engineering applications. • Workability and mechanical properties of RPC under different high temperature curing regimes were studied. • The f cu , f st, and f f of RPC outperformed of all curing regimes at hot-temperature curing for 24 h duration. • Tobermorite gel formation at 24h hot temperature densified RPC’s pores, contributing to improved strength. • Constitutive models and relationship equations were developed among the mechanical properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".