Application of the factor method to the service life prediction of architectural concrete
Bibliographic record
Abstract
Architectural concrete surfaces are a durable solution, but their deterioration process is unavoidable and begins as soon as the element is built. This study establishes a methodology for predicting the service life of architectural concrete surfaces, through the application of the factor method. For this purpose, 239 architectural concrete surfaces in in-service conditions are analysed. Different durability factors are studied and their impact in the service life of architectural concrete surfaces is evaluated. Different scenarios are analysed for the quantification of the durability factors. Scenario 4 presents the best results, leading to a higher similarity between the estimated service lives predicted by the factor method and obtained by the graphical method. The application of the factor method, as described in this study, allows one to predict the service life of architectural concrete surfaces. An estimated service life ranging between 43 and 48 years was obtained, which agrees with the literature and empirical knowledge.
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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".