Optimizing rice husk ash for ultra-high-performance concrete: a comprehensive review of mechanical properties, durability, and environmental benefits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it