Machine Learning to Predict Workability and Compressive Strength of Low- and High-Calcium Fly Ash–Based Geopolymers
Bibliographic record
Abstract
The potential substitution of Portland cement–based concrete with low- and high-calcium fly ash–based geopolymers was investigated. However, predicting the workability and compressive strength of geopolymers with the desired physical and mechanical properties is a complicated process because of the variety of chemical compositions found in aluminosilicate sources. Therefore, machine-learning techniques were used to predict the physical and mechanical properties of the geopolymers and eliminate the usual trial-and-error laboratory procedures. The experimental and predicted results of geopolymer properties using the multilayer perceptron regressor, voting regressor, and XGBoost techniques were compared. The XGBoost model outperformed the other models in terms of accuracy for predicting workability and compressive strength, producing the R2 of 0.96 and 0.89, respectively. Sensitivity analysis determined that the percentage of CaO had the largest effect on geopolymer workability of 27.13%. Fly ash content had the largest effect on compressive strength of 34.44%. Our approach offers a straightforward and dependable strategy for designing and optimizing fly ash–based geopolymers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".