Impact of SCMs and fibres in ECC on the fire resistance of hot-jointed SCC/ECC composites
Bibliographic record
Abstract
The influence of various supplementary cementitious materials (SCMs) and fibres on the fire resistance of composite systems that combine engineered cementitious composites (ECCs) in tension with self-compacting concrete (SCC) in compression was examined. The study was designed to determine the ideal ECC formulation for optimising mechanical properties and bonding performance at ambient and elevated temperatures. The SCC and ECC were hot-joined without vibration or surface preparation, using a fresh-to-fresh casting method. Modifications to the chemical composition of the ECCs included the addition of class F fly ash (FAF), class C fly ash (FAC) or slag, as well as polyvinyl alcohol (PVA) fibres or steel fibres. The samples were exposed to temperatures of 200°C, 400°C, 600°C and 800°C, followed by comprehensive testing to evaluate their flexural strength, tensile strength and interfacial properties. The results indicate that the incorporation of an ECC layer within the SCC system significantly improved mechanical strength and thermal stability, both at ambient temperatures and under high-temperature conditions. Notably, the addition of FAF (rather than FAC or slag) in the ECC layer offered superior thermal stability and ensured the retention of desirable residual mechanical properties. Moreover, steel fibre reinforcement greatly improved the SCC/ECC bonding, outperforming PVA reinforcement at elevated 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".