Data-Driven Strength Prediction of Recycled Aggregate Concrete: Insights from Boosting-Based Machine Learning Models
Why this work is in the frame
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Bibliographic record
Abstract
Accurate prediction of the compressive strength (CS) of recycled aggregate concrete (RAC) is crucial for optimizing mix design and ensuring structural integrity.This study compares the predictive performance of six tree-based and ensemble learning models-Decision Tree, Random Forest, Adaptive Boosting, Gradient Boosting, Light Gradient Boosting Machine, and Extreme Gradient Boosting-using a dataset comprising RAC compositions and testing age.The models are evaluated based on predicted versus actual CS values, residual distributions, and statistical performance metrics, including the coefficient of determination (R) and root mean squared error (RMSE).The results indicate that boosting-based models, particularly Extreme Gradient Boosting and Light Gradient Boosting Machine, achieve the highest predictive accuracy, with R values of 0.94 and the lowest RMSE scores, demonstrating their effectiveness to capture complex nonlinear relationships.In contrast, Decision Tree and Adaptive Boosting exhibit greater variance and lower reliability, primarily due to their sensitivity to data partitioning and noise.These findings underscore the effectiveness of ensemble learning techniques in predicting RAC properties and highlight the potential for further improvements through hybrid modeling approaches and hyperparameter optimization.This study contributes to advancing sustainable construction practices by enhancing the accuracy and reliability of machine learning-based predictive models for recycled concrete applications.
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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 | 0.000 | 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 | 0.001 | 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 it