Shrinkage Characteristics of Geopolymer Concrete: A Comprehensive Review
Bibliographic record
Abstract
Geopolymer concrete (GC) has become apparent as a promising and sustainable alternative to ordinary portland cement (OPC) concrete, presenting notable advantages in both environmental impact and mechanical performance. Despite these benefits, shrinkage remains a critical issue, influencing cracking susceptibility, long-term durability, and structural reliability. While previous investigations have focused on isolated parameters, such as activator concentration or curing techniques, this review provides a comprehensive analysis of the shrinkage behaviour of geopolymer concrete by exploring a broader range of influential factors. Key contributors-including precursor composition, alkali activator concentration, sodium silicate-to-sodium hydroxide ratio, liquid-to-solid ratio, pore structure, and curing conditions-are evaluated and mitigation strategies are discussed. Comparative evaluation of experimental studies reveals key patterns and mechanisms: heat curing around 60 °C consistently limits shrinkage, low-calcium binders outperform high-calcium systems, and chemical additives can reduce shrinkage by as much as 80%. The analysis also highlights emerging, bio-based additives that show promise for simultaneously controlling shrinkage and preserving mechanical performance. By integrating these diverse insights into a single framework, this paper provides a comprehensive reference for designing low-shrinkage GC mixtures.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.005 | 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".