Condition assessment for bridges: a hierarchical evidential reasoning (HER) framework
Bibliographic record
Abstract
Infrastructure risk management practices enable decision-makers to effectively monitor and assess structural condition for repairing/replacing elements before major damage or collapse state is reached. Improved techniques have enhanced inspection and monitoring of infrastructure, but assessment and interpretation of the collected data remains a challenge. In this article, a hierarchical evidential reasoning (HER) framework is proposed for the condition assessment of bridges. The approach involves using a HER framework for classifying bridge data into primary, secondary, tertiary and life safety-critical elements. The proposed HER framework combines different distress indicators (bodies of evidence) at different hierarchical levels. The information is aggregated using Dempster–Shafer (D–S) and Yager rule of combination to propagate both aleatory and epistemic uncertainties throughout the model. Furthermore, importance and reliability factors (collectively termed ‘‘credibility factor’) are introduced for discounting evidence based on importance of bridge element and reliability of the collected data. The data are systematically combined to obtain primary/secondary/tertiary/life safety-critical condition indices. Finally, an overall bridge condition index is obtained. The indices are based on information from multiple sources thereby providing a more reliable assessment of bridge condition. The HER framework is applied to data from an existing bridge in order to demonstrate application of the proposed approach.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".