Evaluation of Concretes Made with Marble Waste Using Destructive and Non-Destructive Testing
Bibliographic record
Abstract
The present study aims primarily to investigate the possibility of assessing the physico-mechanical behavior of concrete incorporating marble waste or marble powder as a partial replacement for cement using destructive and non-destructive testing methods. Indeed, in this work, cement was partially replaced with marble powder at six different substitution levels, i.e. 5, 10, 15, 20, 25 and 30% by weight, with 1.5% adjuvant (super plasticizer) for each mixture. The samples prepared were then analyzed. In addition, the physico-mechanical properties, in the fresh and hardened states, water-to-cement ratio, absorption and compressive strengths of the concrete samples were examined as well. Moreover, the compressive strength of concrete was assessed through non-destructive testing methods such as the ultrasonic pulse velocity and rebound hammer. Likewise, the relationship between the ultrasound velocity and compressive strength of concrete were also estimated after 3, 7, 28 and 90 days of curing. The findings of the study indicated that, at early age of curing, the values of the compressive strength and ultrasonic pulse velocity were quite small for all replacement levels, of cement with marble powder, between 15 and 30%. Nevertheless, when the curing period was increased, the compressive strength and ultrasonic pulse velocity of all the samples went up as well. In the end, a linear relationship was observed between the ultrasonic pulse velocity and compressive strength for all substitution levels of cement with marble powder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".