Managing and predicting embodied carbon emissions for ready-mix concrete products using model-agnostic meta-learning technique
Bibliographic record
Abstract
Ready-mix concrete (RMC) production is a major contributor to upstream carbon emissions in the construction industry. However, the absence of reliable emissions data, coupled with inconsistencies in reporting practices, presents significant challenges for stakeholders in effectively identifying and managing carbon hotspots across regions. Thus, this study employed a web crawling technique to compile a high-quality dataset of 59,412 Environmental Product Declarations (EPD) of RMC products in North America, then utilized the Model-Agnostic Meta-Learning (MAML) algorithm to enhance the embodied carbon emissions prediction for these products. The model was trained using three datasets related to material use, resource consumption, and waste generation as base learners in the United States (U.S.). Then, we tested the model with a new dataset from Canada containing unseen features to evaluate its generalization capability under varying environmental and technological scenarios in RMC production. The results showed that the proposed task-oriented MAML model outperformed the base learners, achieving an R 2 score of 0.902 for new task prediction, compared to scores of 0.759, 0.689, and 0.687 for the respective base learners. Furthermore, the MAML model exhibited 25 %–40 % reductions in MAE, RMSE, and MAPE relative to the base learners, highlighting its predictive performance in analyzing multi-task cases. Finally, a web-based platform framework incorporating the trained MAML model is proposed to support stakeholders in managing carbon emissions and to serve as a tool for validating EPD documents for RMC products. The findings of this study provide valuable insights to advance decarbonization efforts within the construction industry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".