Synergistic Influence of Multi-Walled Carbon Nanotubes and Nanosilica Powder on Mechanical Performance of Mortar with Demolished Concrete Waste Aggregate and Polypropylene Fibers Addition Using Taguchi Design of Experiment
Bibliographic record
Abstract
This study investigates the synergistic influence of multi-walled carbon nanotubes (MWC-NTs), nanosilica powder (NSP), and polypropylene fiber waste (PFW) on the mechanical performance of mortar incorporating demolished concrete waste aggregates (DCWA). The replacement of natural aggregates with DCWA typically results in strength reductions and weak interfacial transition zones; therefore, the combined use of nanomaterials and microfibers is proposed as a mitigation strategy. A Taguchi Design of Experiments (DOE) approach was employed to optimize mix parameters, including MWCNT dosage, NSP content, PFW volume fraction, and DCWA replacement level. Mortar mixtures were prepared with MWCNTs (0-0.1% by binder weight), NSP (0-2% by binder weight), PFW (0-0.3% by volume), and DCWA (0-20% replacement of fine sand). Mechanical performance was assessed through compressive and flexural strength tests. A combined statistical approach using the Pareto chart and ANOVA identified the most influential parameters and their respective contributions to the response variable. The innovative aspect of this research lies in the synergistic integration of MWCNTs, NSP, demolished concrete waste, and polypropylene fiber waste within the mortar matrix, with the incorporation of nanomaterials specifically intended to compensate for the strength reduction typically induced by the use of demolition concrete waste aggregates. Although a potential nano-scale synergy between MWCNTs and NSP was initially considered, the experimental results indicated that the most relevant synergistic effects occurred among broader mix parameters rather than specifically between the two nanomaterials. Even so, when assessed individually, both nanomaterials contributed to improving the mechanical characteristics of the mortar-particularly nanosilica, which demonstrated a more pronounced effect-yet these individual enhancements did not translate into a distinct synergistic interaction between MWCNTs and NSP. The Taguchi DOE proved to be an efficient tool for multiple factor analysis, enabling reliable identification of the most influential parameters with a minimum number of tests. Its application facilitated the development of mortar mixtures that effectively integrate demolition waste while achieving enhanced mechanical performance through nano- and micro-scale reinforcement.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".