Physical-Mechanical Performance of Concrete with Agro-Industrial Ashes at Different Thermal Curing Ranges
Bibliographic record
Abstract
Cement production accounts for approximately 8% of global CO₂ emissions, which drives the adoption of sustainable supplementary cementitious materials (SCMs) to reduce clinker consumption.In Peru, sugarcane bagasse ash (SCBA) and rice husk ash (RHA) are abundant agro-industrial by-products with recognized pozzolanic potential; however, their performance under different curing temperatures is still insufficiently documented.This study evaluates the effect of partially replacing cement with 5% and 10% SCBA, RHA, and SCBA+RHA in concrete designed for f′c = 210 kg/cm² and cured at 10℃, 25℃, and 35℃.Nineteen concrete batches were proportioned following ACI 211.1 and tested for fresh-state properties (slump, bleeding, and unit weight) and hardened-state performance (compressive and splitting tensile strength) in accordance with ASTM standards.Statistical significance was assessed using ANOVA and Tukey's post hoc test (p < 0.05).A 5% SCBA+RHA replacement increased compressive strength by up to 14% across all curing temperatures, indicating a consistent synergistic effect.At 35℃, RHA at 5% and 10% increased splitting tensile strength by 14% and 13%, respectively, relative to the control.Higher replacement levels reduced slump by up to 28%, likely due to greater fineness and water demand, while also decreasing bleeding by up to 18%, thereby improving mixture cohesion.Overall, SCBA and RHA are viable SCMs for enhancing concrete performance in warm and temperate climates.Their combined use at moderate replacement levels provides mechanical benefits without significantly affecting density, whereas in cold climates, extended curing durations or temperature control are recommended to maximize pozzolanic reactivity and strength development.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".