Low-Cement Engineered Cementitious Composites: Application in Bridge Link Slabs
Bibliographic record
Abstract
One effective method for mitigating corrosion issues stemming from expansion joints in bridge decks is to replace expansion joints with link slabs. New link slabs are being constructed with fiber-reinforced concrete (FRC). Engineered cementitious composite (ECC) is one of the FRC materials that offer better deformability and higher durability in terms of controlling crack width for link slabs. However, the total amount of Portland cement (PC) used in ECC is much higher than that in the normal concrete, and, thus, ECC is not considered an environmentally friendly construction material. Hence, the objective of this study is to develop novel link slabs using greener ECCs that require substantially less PC and possibly use waste material such as fly ash (FA) or blast furnace slag (BFS) as an alternative binder. Different ECC mixtures were studied with substantial substitution of PC with FA or BFS, developing low-cement ECCs. A sustainability analysis was conducted on these materials, and they were used to construct link slabs to evaluate their performance in reinforced members. This study found that the embodied CO2 in low-cement ECCs reduced by up to 54% when compared with conventional ECC. This study also found that the low-cement ECC link slabs offer higher deformability and similar or higher durability compared to the control ECC link slab.
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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) | 0.001 | 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".