Predicting Mechanical Properties of Concrete Using Equivalent Mortar: A Comparative Study
Bibliographic record
Abstract
Comprehending the mechanical characteristics of concrete is paramount for its efficient deployment in construction.This investigation endeavors to predict these properties utilizing equivalent mortar, furthering previous inquiries on the topic.Literature suggests a viable approach to create concrete through a method employing both natural and crushed aggregates.This technique considers the corresponding mortar, amalgamated with crushed (SC) and dune sand (SD), as a novel mortar.Implementing this method could curtail the consumption of natural resources while conserving the mechanical attributes in both fresh (flow) and hardened states (strength and durability).This approach aims to anticipate the mechanical performance of concretes formulated from equivalent mortar data.Five mixtures were constituted to discern the optimal blend from a binary amalgamation of crushed coarse aggregates (30% fraction 3/8 and 70% fraction of 8/15) sized 3.8mm and 8.15mm, alongside five mixed sand percentages: (30% SC+70% SD), (40% SC+60% SD), (50% SC+50% SD), (60% SC+40% SD), and (70% SC+30% SD).The application of super plasticizer was investigated, and the compressive strength function of coarse aggregate was ascertained at intervals of 7, 14, and 28 days.The mechanical strength was determined at the 28-day mark.The empirical study indicates that density attains its zenith when a 40% mortar is incorporated into the concrete matrix.Conversely, with a 50% SC composition within the mixed sand, the mechanical strengths achieve acceptable values with moderate CS dosages.Specification tests reveal that incorporating 50% to 70% mortar into the concrete matrix can yield highquality 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".