Enhancing Predictive Accuracy of Compressive Strength in Recycled Concrete Using Advanced Machine Learning Techniques with K-means Clustering
Bibliographic record
Abstract
The urgent need to mitigate environmental impacts in the construction industry drives the exploration of sustainable practices, such as the use of recycled materials in concrete production. The primary objective of this study was to enhance the predictability of compressive strength in the concrete through the application of advanced machine learning (ML) techniques, specifically Gradient Boosting Regression (GBR) and Random Forest Regression (RFR). Using a comprehensive dataset of 353 eco-friendly concrete samples, the study carefully developed and validated these models to evaluate their performance. The findings exposed that the GBR model outperformed the RFR model, obtained an R² of 0.97 in training phase and 0.96 in testing phase, the findings supported further with root mean squared error (RMSE) of 1.99 and 3.06, and by mean absolute error (MAE) of 1.44 and 2.38 for training and testing phases respectively, where indicating high predictive accuracy. Conclusively, the broader adoption of GBR model for similar applications recommended by the study and points towards future research directions to integrate more diverse datasets and investigate more predictive models to improve sustainable construction practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".