Plastic concrete mechanical properties prediction based on experimental data
Bibliographic record
Abstract
The industrial revolution brought environmental degradation to light. Concrete and plastic degrade the ecosystem and cause unsustainable development. Academic and industrial sectors are interested in lowering carbon emissions associated with concrete. Meanwhile, global sand scarcity worries environmentalists. To reach sustainable development goals, cement and fine aggregate must be substituted with other abundant waste/natural materials. This study aimed to develop a green concrete by utilizing plastic waste and creating modelling tool for predicting the mechanical properties of plastic concrete. Different composition of silica fume and superplasticizers substituted fine aggregate and cement in both irradiated (treated) and regular (untreated) plastic concrete. Compressive strength (fc’) and split tensile strength (fst) of the resulting concrete were studied. Moreover, from literature data, 320 data points each for fc' and fst were used to train gene expression programming (GEP) models. Models’ accuracy was evaluated employing various statistical measures. Regular plastic waste concrete has demonstrated a lower fc’ and exhibited anomalous behavior for fst. While irradiated plastic waste concrete has demonstrated improved mechanical characteristics, comparatively. Correlation coefficients using GEP models for fc’ and fst were found to be 0.92 and 0.88, respectively. Furthermore, sensitivity analysis revealed that plastic was the most significant in the GEP model’s development. K fold validation was employed to prevent over-fitting of the models. GEP provides an empirical expression for each outcome to predict future database features. This research improves green concrete's long-term sustainability by reducing carbon emissions and alleviating fine aggregate scarcity.
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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 | 0.001 | 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) | 0.000 | 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".