Development and characteristic of SFRCR-ECC as a novel multifunctional version of fire-resistive and corrosion-resistive coating
Bibliographic record
Abstract
Ordinary fireproof coatings can retard the rise of temperature on the outer wall of steel pipes during building fires. However, they still have defects such as low tensile and compressive strength, limited ductility and toughness, and poor bonding with the substrate, etc. At the same time, twice coating of anticorrosive primer and fireproofing on steel substrate reduces both cohesive properties of the steel substrate. Under the influence of the environment, the coatings will develop large cracks or even peel off with the deformation of the structure. In response to the above problems, a spray-applied, fire-resistive and corrosion-resistive engineered cementitious composites (SFRCR-ECC) containing fly ash cenosphere (FAC), PP fibers and PE fibers was developed in this paper. The working properties, tensile and compressive properties, bond strength and durability of SFRCR-ECC were also systematically investigated. Due to the excellent durability and mechanical properties of SFRCR-ECC-30, it was selected as the optimal ratio, and its thermal conductivity and microstructure were investigated. SFRCR-ECC-30 would be the SFRCR-ECC coating sprayed onto concrete-filled steel tubular (CFST) columns subjected to fire. The successful development of SFRCR-ECC provides a new possibility for fireproofing of steel structures and provides the necessary basic data support for subsequent tests.
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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) | 0.000 | 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".