Machine learning approaches for RCPT modeling of concrete
Bibliographic record
Abstract
Chloride ion penetration can lead to the corrosion of embedded steel reinforcement in concrete and deteriorate its durability. One of the commonly used methods for determining the durability of concrete is the rapid chloride permeability test (RCPT). The accurate prediction of RCPT is essential in optimizing concrete mixture design. In this study, RCPT modeling was conducted using a dataset of 469 samples. The contributions of this study are to apply a large-scale dataset with essential variables, to identify the optimal variables to maximize the performance of RCPT prediction models, to determine the most accurate prediction method for RCPT prediction, and to introduce a novel algorithm to tune hyperparameters of machine learning methods. To this end, first, feature selection was implemented to select the optimal variables. Then, different machine learning methods were used to predict RCPT. A novel less-parameter algorithm (STML) was adjusted to tune hyperparameters considering multiple metrics, which was considerably more accurate than the conventional tuning method. The results suggested that eXtreme Gradient Boosting tuned by STML was the best-performing model, with an MAE of 38.893 coulombs. Subsequently, SHapley Additive exPlanation was synchronized with the best-performing model, and the results showed that the test temperature had the highest relative influence on RCPT, followed by fly ash to binder ratio, silica fume to cement ratio, and coarse aggregate content. Finally, the Partial Dependence Plots were applied to capture the influence direction of different variables on RCPT, allowing the identification of the optimal range of materials to minimize RCPT. • All the vital features to accurately predict RCPT are identified. • A novel less-parameter algorithm is developed to tune hyperparameters. • XGB tuned by STML is the best-performing model. • Test temperature has the highest relative influence on RCPT. • The optimal range of variables to minimize RCPT is determined.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".