Integrated Condition-Based Rating Model for Sustainable Bridge Management
Bibliographic record
Abstract
In North America, common practices in bridge condition assessment include visual inspection and nondestructive evaluation (NDE) techniques, and results are reported as condition ratings of the bridge components. Assigning a specific condition rating to the component is a difficult task, especially when the threshold values defining the borderlines between the different ratings are not specified. These thresholds are subjectively assigned based on the judgment and experience of the inspector or expert, and can influence decisions on maintenance, repair, and replacement (MRR) of bridges and impact their safety and serviceability. Quality inspection data and accurate condition assessment and rating are the basis for determining appropriate MRR decisions. Thus, in this paper, a novel quality function deployment (QFD)-based approach for assessing bridges is proposed to develop an integrated condition rating based on data collected from visual inspection and ground penetrating radar (GPR) technology, while identifying clear thresholds between the different ratings. The k-means clustering technique, used to define the rating thresholds, is one of the unsupervised learning algorithms that solves the subjective determination of the threshold values problem. This work used four case studies on bridges in the Province of Quebec. The integrated condition model produced ratings of 0.48, 0.49, 0.37, and 0.15 for the four case studies. The developed rating model represented by an integrated condition index was validated with an average validity percentage greater than 81%. The proposed method is expected to advance the state of the art for bridge condition assessment and rating by providing an objective means for making proper MRR decisions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".