Prediction of Drying Shrinkage Cracking of Eco-efficient Steel Chip Reinforced Cementitious Composite Considering Tensile Creep
Bibliographic record
Abstract
This paper reports on the characteristics of drying shrinkage and creep of steel chip reinforced cementitious composite (SCRCC). In this study, first, four restrained wall specimens made of normal mortar and SCRCC with various numbers of steel reinforcing bars (4 or 10) were prepared to compare drying shrinkage characteristics. The specimens were re-strained on the rigid laboratory floor so that shrinkage cracks were induced. The drying shrinkage strains were measured by the contact gauge method and compared with unrestrained small specimens. The number of cracks was simultane-ously observed. Second, bond tests were prepared to evaluate the bond characteristics between the SCRCC and the steel bar. Third, creep tests were performed to improve the accuracy of the analysis of the drying shrinkage behavior. Twelve block specimens were made and a constant flexural load was applied for 7, 14, and 28 days. The observed shrinkage strains and creep strains of SCRCC were modeled according to CEB-FIP Model Code 1990. These models were incor-porated with bond computation between the SCRCC and the steel bar to predict the number of drying shrinkage cracks. The computed equivalent number of cracks based on the shrinkage strain model, the creep model, and the bond model derived from a pull-out test generally agreed with the test results.
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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 | 0.001 |
| 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 | 0.001 | 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".