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.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".