Durability and Long-term Performance Assessment in Eco-efficient Concrete Mixtures Towards a More Sustainable Cement Industry - A Critical Review
Bibliographic record
Abstract
In the current need to fulfill a Net-Zero goal to offset greenhouse gas emissions by 2050, the cement industry deeply needs to renovate its process and products. The amount of concrete, and consequently ordinary Portland cement (OPC) used worldwide, far exceeds other industrial materials, contributing to around 8% of global emissions. A recognized solution would be to partially replace clinker with limestone, an available material in many countries with the potential to improve the particle packing of a concrete mixture. Even though several studies have been carried out on the application of Portland limestone cement (PLC) concrete, there is a need for further investigation regarding durability performance. Problems include alkali-silica reaction (ASR), delayed ettringite formation (DEF), sulphate attack, steel corrosion, and freeze/thawing, whose deteriorating effects tend to be maximized in harsh climate countries such as Canada and should be evaluated when introducing distinct materials into concrete. Therefore, this paper aims to review previous assessment of the durability of PLC concrete mixtures. The use of an advanced mix-design method (particle packing model and mobility parameters - PPM+MPs), will also be discussed on its ability to enhance the durability performance of the mixtures. It is expected that novel guidelines to design more eco-efficient concrete mixtures using PLC can be developed to reduce concrete’s cost and carbon footprint, achieving a more sustainable industry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".