Utilizing Agricultural Residues from Hot and Cold Climates as Sustainable SCMs for Low-Carbon Concrete
Bibliographic record
Abstract
Supplementary cementitious materials (SCMs), such as fly ash, slag, and silica fume, predominantly derived from industrial waste, are widely utilized in concrete due to their proven ability to enhance both its mechanical and durability properties. Moreover, these SCMs play a crucial role in mitigating the carbon footprint of concrete by reducing its cement content, which is responsible for approximately 8% of global CO2 emissions. However, the sustainability and long-term availability of conventional SCMs are increasingly under scrutiny, particularly in light of the impending shutdown of coal-fired power plants, which threatens the future supply of fly ash. As a result, the concrete industry faces an urgent need to identify alternative SCMs to maintain and advance eco-friendly practices. This article stands out from previous reviews by employing a bibliometric analysis to comprehensively explore the use of commonly utilized agricultural ashes (rice husk, palm oil, and sugarcane bagasse), prevalent in tropical and subtropical regions as SCMs. Additionally, it provides valuable insights into the potential of cold-weather crops (e.g., barley, canola, and oat) that demonstrate promising pozzolanic reactivity. The study critically evaluates and compares the physical and chemical characteristics of agricultural ashes from both hot and cold climates, assessing their influence on the fresh, mechanical, and durability properties of concrete. It also addresses the challenges and limitations associated with their use. Furthermore, in line with the United Nations and Environmental Protection Agency (EPA) sustainability goals, the review evaluates the environmental benefits of using agricultural ashes, emphasizing waste reduction, resource conservation, and energy savings. This comprehensive review paper should deepen the understanding of agricultural ashes as sustainable SCMs, providing a strategic direction for the construction industry to adopt low-carbon concrete solutions across various climates while promoting advancements in production methods, performance standards, and emerging technologies such as hybrid materials and 3D printing.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".