Optimal overlays for preservation of concrete in cold climate: decision-making by the method of fuzzy comprehensive evaluation combined with AHP
Bibliographic record
Abstract
Abstract Overlays have been extensively employed as an effective preservation or rehabilitation tool to extend the service life of concrete bridges and pavements, especially concrete slabs suffering from salt scaling and abrasion. However, limited attention has been paid to the durability and performance of these overlays which can be jeopardized when they are exposed to freeze/thaw and wet/dry cycles, deicer applications, studded tires, and their coupled effects. Various overlays feature different engineering properties, and they might be only effective in specific service environments but not in others, and research is lacking to examine their ability to adapt to different environments. This study subjected five overlay products on concrete slabs to the combined action of freeze/thaw (F/T) and wet/dry (W/D) cycles with periodical exposure to either 15 wt.% NaCl solution or 15 wt.% MgCl 2 solution, to simulate the typical field scenarios in an accelerated manner. The bond strength, splitting tensile strength, and abrasion resistance of the overlaid concrete slabs were tested to evaluate the effectiveness of various overlays against the deicer scaling and the abrasion by studded tires. Based on the experimental data, this study demonstrated a multi-criteria decision-making method, fuzzy comprehensive evaluation (FCE) combined with analytic hierarchy process (AHP), for the selection of optimal overlays in three different service scenarios (e.g., states of Washington and Oregon [USA] and British Columbia [Canada]). The analysis results indicate that one epoxy overlay exhibited the comprehensively best performance and could be a promising candidate in all three given scenarios, another polymer overlay took second place, while the adaptability of the three cement-based overlays varied in different environments. Graphical Abstract
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".