Shear performance evaluation of sustainable concrete beams containing rice husk ash and carbon nanotubes as cement replacement
Bibliographic record
Abstract
The utilization of rice husk ash (RHA) as a cement substitute in concrete can mitigate the environmental issues caused by concrete production. This study is the first investigated the effect of different concentrations of multiwall carbon nanotubes (MWCNTs) on the shear behaviour of RHA-reinforced concrete (RC) beams. Three RHA RC beams incorporating different MWCNTs concentrations (0%, 0.1%, and 0.2%) were subjected to shear failure tests, with shear behaviour evaluated based on load-deflection capacity, failure load, strain distribution, and crack development morphology. The results indicate that the incorporation of well-dispersed MWCNTs (0.1%) significantly enhances the shear resistance of RHA RC beams. Specifically, with the addition of 0.1% MWCNTs, the first crack load, initial stiffness, and ultimate shear capacity increased by 34.78%, 38.4%, and 4.76%, respectively, while midspan deflection and ductility improved by 23.19% compared to the control specimen. However, excessive MWCNTs incorporation negatively impacted shear capacity, as the inclusion of 0.2% MWCNTs led to an 8.57% reduction in the ultimate shear capacity of the RHA RC beam. Additionally, the experimental results were compared with predictions from several models, including the ACI 318 model, Eurocode 2-05 model, Canadian standard, Bazant and Kim, Zsutty, and Collins and Kuchma models. The comparison revealed that the Eurocode 2-05 model, Canadian standard, and Zsutty models provide reliable predictions of the shear behaviour of RHA RC beams, with an error margin within 20%. The findings suggested that well-dispersed MWCNTs can play a role in improving the initial stiffness and ductility of RHA RC beams, enhancing the shear resistance of the beams, delaying shear failure, and preventing brittle failure of the beams. Furthermore, the improvement of the performance of RHA sustainable concrete components by MWCNTs contributes to the sustainable development of the construction industry.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".