Comparative study of statistical computational approaches to investigate the degraded compressive strength of concrete under the freeze-thaw effect
Bibliographic record
Abstract
The primary cause of frost damage and early failure in concrete structures is the repeated freezing and thaw cycles (FTC). The evolution of internal fractures and scaling at the surface of concrete can be used to assess the impact of frost damage. Surface scaling mechanisms and interior frost damage are contingent upon numerous environmental factors, including the rate of freezing, low temperature, and duration of the freezing point. However, evaluating the amount of strength loss of concrete material is a challenging factor to consider. This work explores the application of predictive modelling tools, including random forest (RF), multilayer perceptron (MLP), decision tree (DT), and bagging, to evaluate the degraded compressive strength (D-CS) of concrete. The models use five input variables: initial compressive strength (I-CS), water-to-cement ratio (W/C), FTC, minimum temperature (T min ), and maximum temperature (T max ), with D-CS as the output. Model performance was compared using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics, with the bagging model achieving the highest predictive performance at 92 %. A 10-fold cross-validation approach validated the model's accuracy. The influence of each variable was assessed using SHapley Additive exPlanations (SHAP) analysis. Importantly, a user-friendly Graphical User Interface (GUI) was developed based on the models, making it easy for researchers and professionals to make predictions and thereby increasing the practicality and accessibility of this research. This study aids the research community in selecting appropriate models to forecast the strength of various concrete types, thereby enhancing the practical application of this research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".