Machine learning for rapid quantitative stucco phase analysis in plasterboard
Bibliographic record
Abstract
Stucco phase composition critically influences the mechanical properties of plasterboard, a cornerstone in modern construction. Traditional complete phase analysis (CPA) methods, while accurate, are hindered by prolonged processing times exceeding 12 h, impeding real-time quality control. This study introduces a machine learning-assisted CPA (ML-CPA) method leveraging artificial neural networks (ANNs) to enable rapid, quantitative analysis of industrial stucco compositions. By integrating calcination weight loss profiles and hydration temperature curves—collected within 40 min—the method circumvents the need for extended conditioning periods. A dataset of 490 synthetic stucco samples, covering typical industrial phase ranges, was used to train the ANN model. The model achieved a root-mean-square error (RMSE) of 2.2 % in phase prediction and 87.7 % accuracy in free moisture detection. In particular, this approach reduces analysis time by 96 %, offering a scalable solution for online industrial quality control. By bridging the gap between laboratory accuracy and production-line efficiency, ML-CPA represents a transformative advancement in gypsum product manufacturing, with potential annual cost reduction and rapid quality control capability.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".