Investigation of Mechanical and Thermal Performance of Nanoclay Modified Concrete for Energy Efficiency
Bibliographic record
Abstract
In recent years, the integration of Nanoclay (NC) into concrete has garnered significant global attention due to its potential added benefits and importance in the construction industry. However, the existing literature lacks sufficient experimental validation and empirical analysis pertaining to several unexplored key properties, such as thermal resistance, conductivity, diffusivity, and fire resistance. This study aims to address these knowledge gaps and contribute to the current body of literature by providing a comprehensive review of the advantageous effects of NC incorporation on the aforementioned properties of concrete. A thorough examination of available data was conducted, focusing on the mechanical and thermal characteristics of concrete after the inclusion of NC in the mix design. The findings of this critical review indicate that the incorporation of NC into concrete can reduce building energy consumption and enhance thermal insulation properties. Moreover, the integration of NC in concrete was found to improve various thermal features, including thermal stability, fire resistance, thermal performance, thermal behavior, resistance to thermal cracking, and resistance to thermal degradation. In addition, the inclusion of NC in concrete was observed to decrease thermal conductivity, thereby facilitating effective thermal insulation and resulting in lower energy consumption during heating and cooling periods. Simultaneously, the integration of NC was found to bolster the compressive, flexural, and tensile mechanical properties of concrete. Furthermore, the incorporation of NC into concrete materials has the potential to mitigate negative environmental impacts, such as pollution and poor air quality. This comprehensive review provides valuable insights into the benefits of NC integration in concrete, paving the way for further research and innovative applications in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".