Optimizing rice husk ash for ultra-high-performance concrete: a comprehensive review of mechanical properties, durability, and environmental benefits
Bibliographic record
Abstract
Abstract This review critically examines the potential of rice husk ash (RHA) as a supplementary cementitious material (SCM) in ultra-high-performance concrete (UHPC), focusing on its impact on mechanical properties, microstructure, and sustainability. Literature for this review was selected through a systematic search of Scopus, Web of Science, and Google Scholar, focusing on studies from the last two decades that provide empirical data on RHA-enhanced UHPC performance and microstructure. With a silica content ranging from 85 % to 95 %, RHA enhances pozzolanic reactions, leading to improved UHPC performance. Maximizing RHA’s efficacy in UHPC requires optimization techniques, such as utilizing superplasticizers and fibers, maintaining low water-to-binder ratios (0.18–0.22), and regulating replacement amounts (10–20 %). At optimal replacement levels of 10–15 %, RHA increases compressive strength by up to 9.78 %, tensile strength by 25.09 %, and flexural strength by 10.9 %, compared to control mixes. Additionally, its use reduces carbon dioxide emissions by approximately 10–15 % and energy consumption by up to 20 %, contributing to more sustainable concrete production. The review also highlights a reduction in chloride penetration and improved resistance to sulfate attack and freeze-thaw cycles, due to microstructural densification and reduced porosity. However, performance is sensitive to RHA quality, processing methods, and mix design parameters. This review identifies current limitations and recommends future research in standardization, long-term durability, and optimization strategies, underscoring the role of RHA in advancing eco-efficient, high-performance concrete technologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 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".