Structural Build-Up of Cement Pastes: A Comprehensive Overview and Key Research Directions
Bibliographic record
Abstract
The advancement of modern concretes, such as printable concrete, fluid concrete with adapted rheology, and ultra-high-performance concrete, has increased the importance of understanding structural build-up in cement-based materials. This process, which describes the time-dependent evolution of rheological properties, is a key factor to ensure the stability of concrete by influencing segregation, bleeding, formwork pressure, numerical modeling, and multi-layer casting. As a result, the structural build-up of cementitious materials has become a significant area of research in recent years. The structural build-up of cement based-materials results from both a reversible part (thixotropic behavior), driven by colloidal interactions, and an irreversible part, caused by cement hydration and the formation of C-S-H bridges. Various experimental techniques have been developed to investigate these processes, with various factors affecting the thixotropic behavior and overall structural build-up of cement suspensions. This review provides a comprehensive analysis of the current understanding of structural build-up in cement pastes. It covers measurement methods and key influencing factors, including the water-to-binder ratio (w/b), admixtures, temperature, and supplementary cementitious materials (SCMs). By consolidating the existing knowledge and identifying research gaps, this review aims to contribute to the development of sustainable, high-performance cement-based materials suitable for modern construction techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".