An Experimental Investigation on the Mechanical Performance of Engineered Cementitious Composites with Different Types of Steel Fibers
Bibliographic record
Abstract
Engineered cementitious composites (ECCs), known for their superior ductility and strain-hardening behavior compared to conventional concrete, have been predominantly studied with polyvinyl alcohol (PVA) fibers. However, the potential economic and technical advantages of incorporating steel fibers into ECCs have been largely overlooked in the literature. This study investigates the mechanical performance of ECC reinforced with different types of steel fibers, including straight, twisted, hooked, and hybrid fibers of different lengths, as compared to PVA. The inclusion of various supplementary cementitious materials (SCMs) such as slag and fly ash with each type of steel fiber was also considered at a constant fiber volume fraction of 2%. The mechanical properties were assessed through compressive strength, splitting tensile strength, and four-point flexural tests along with calculations of toughness, ductility, and energy absorption capacity indices. This study compares the mechanical properties of different ECC compositions, revealing that ECCs with hybrid steel fibers (short and long) achieved more than twice the tensile strength, 12.7% higher toughness, and 36.4% greater energy absorption capacity compared to ECCs with PVA fibers, while exhibiting similar multiple micro-cracking behavior at failure. The findings highlight the importance of fiber type and distribution in enhancing an ECC's mechanical properties, providing valuable insights for developing more cost-effective and resilient construction.
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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 | 0.000 | 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 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".