Enhancing Mechanical Properties of Self-Compacting Concrete Through the Utilization of Pozzolanic Materials and Waste Products
Bibliographic record
Abstract
This investigation delineates the impact of pozzolanic constituents and industrial byproducts on the mechanical properties of self-compacting concrete (SCC).Calcined kaolinitic clay (CKC), waste marble powder (WMP), and limestone powder (LP) were utilized as cementitious supplements in various binary, ternary, and quaternary formulations.Specimens were synthesized by varying the substitution ratios of CKC, WMP, and LP within the cement matrix.The fresh state characteristics, encompassing L-Box height ratio, segregation resistance, V-funnel flow time, and slump flow diameter, were quantitatively assessed.Concurrently, the hardened state properties were examined at 7, 28, and 56-day maturation periods.Results demonstrated that the optimal binary blend, containing 10% WMP and 10% LP, significantly augmented the fresh properties and compressive strength of SCC.Ternary mix compositions further enhanced both compressive and tensile strengths, as well as ultrasonic pulse velocity, with peak values reaching 57.8 MPa, 4.61 MPa, and 4670 m/s, respectively, thereby surpassing traditional mortar benchmarks.The study's findings substantiate the potential of integrating CKC, WMP, and LP to not only bolster the performance of SCC but also to curtail cement usage, thereby reducing associated CO2 emissions and enhancing sustainability.This research offers a compelling narrative for the construction sector, advocating for the adoption of alternative materials in the production of advanced, high-performance selfcompacting concrete.
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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".