Influence of Glass Microfibers on the Control of Autogenous Shrinkage in Very High Strength Self-Compacting Concretes (VHSSCC)
Bibliographic record
Abstract
High-performance concrete (HPC) is widely used in infrastructure for its durability and sustainability benefits. However, it faces challenges like autogenous shrinkage, leading to potential cracking and reduced durability. Fiber reinforcement offers a solution by mitigating shrinkage-induced stresses and enhancing concrete durability. In this sense, this study investigates the use of glass microfibers to mitigate autogenous shrinkage and early-age cracking in high-strength self-compacting concrete. Samples were prepared with two water-to-binder ratios (w/b): 0.25 and 0.32; and three glass microfiber contents: 0.20%, 0.25%, and 0.30 vol.%. The concrete mixtures were characterized in the fresh state for slump flow and in the hardened state for compressive strength, static, and dynamic Young’s modulus. Unrestrained and restrained shrinkage tests were also conducted in the seven days-age. The findings revealed that glass microfibers reduced the workability in mixtures with lower slump flow values (w/b of 0.25), while less viscous mixtures (w/b of 0.32) exhibited a slight improvement. Compressive strength showed a proportional enhancement with increasing fiber contents in concretes with a w/b ratio of 0.32. A contrasting trend emerged in concretes with a w/b ratio of 0.25, wherein strength diminished as fiber additions increased. The modulus of elasticity improved with fiber additions only in the matrix with a w/b ratio of 0.25, showing no correlation with compressive strength results. In shrinkage tests, the addition of glass microfibers up to specific limits (0.20% for a w/b ratio of 0.25 and 0.25% for w/b of 0.32) demonstrated improvements in controlling concrete deformation in unrestrained shrinkage analyses. Concerning cracking reduction in restrained concrete specimens, the mixtures did not exhibit significant improvements in crack prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".