Development of an Assessment Method to Evaluate the Quality of Remedial Treatments on Concrete Flat Roofs of Multiple Buildings
Bibliographic record
Abstract
Existing assessment tools have not been successfully used to evaluate the performance of the remedial treatment of concrete flat roofs by building users or building management personnel because they are complicated and difficult to be applied by non-experts. In this study, a semi-quantitative method has been developed to assess the quality of remedial treatment on concrete flat roofs of multiple buildings. A 10-point scale weighting was calculated based on the results of ranking, and then the score assessment scheme was developed. Results revealed that the repairing principles included protection against ingress (W = 7.25), physical resistance against ingress (W = 5.23), concrete restoration (W = 8.22), structural strengthening (W = 8.22), moisture control (W = 6.59), and chemical resistance (W = 4.57). The grouped principles included physical resistance (W = 6.24), structural stability (W = 8.22), and chemical resistance (W = 5.58). The scoring assessment scheme was applied to a remedial treatment on multilayer concrete flat roof which was treated with bituminous membrane. The assessment scheme was revised based on the surveyors’ comments and then verified by the experts. This assessment method secured and improved the quality of the remedial treatment, since the building management staff/owner successfully evaluated, controlled, and monitored the quality of remedial treatments.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".