High-performance strain-hardening cementitious composites with tensile strain capacity exceeding 4%: A review
Bibliographic record
Abstract
A state-of-the-art review on the development of high-performance strain-hardening cementitious composites (SHCC) with over 55 MPa compressive strength, 4% tensile strain capacity, and 300 kJ/m3 strain energy was conducted. The different designs of high-performance SHCCs with respect to the type of ingredients (cementitious materials, aggregates, fillers, and nanomaterials), water/binder and sand/binder ratios, fiber type, and aspect ratio, along with diverse curing regimes to satisfy the performance criteria, were analyzed. Some fiber surface refinement processes, e.g., graphene oxide coating and oxidation, were explained, and their effects on the mechanical properties of high-performance SHCCs were discussed. The durability and impact/blast resistance were also evaluated and compared with those of conventional SHCCs, such as engineered cementitious composites (ECC) and ultra-high-performance fiber-reinforced concrete (UHPFRC). It was discovered that ductility-enhanced high-strength SHCC has a higher impact resistance than ECC and UHPFRC do. Because of its excellent mechanical properties, high-performance SHCC can be effectively used for various purposes, e.g., strengthening of existing structures, fireproofing of steel structures, elimination of stirrups, partial or total replacement of longitudinal steel rebars in reinforced concrete structures, and earthquake-resistant frame structures. Based on data analysis, a very-high-performance SHCC, which can absorb five times more energy than ECC, has also been recently developed. This SHCC has over 100 MPa compressive strength, 8% tensile strain capacity, and 800 kJ/m3 strain energy, respectively.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".