Defect-Based Condition Assessment of Concrete Bridges
Bibliographic record
Abstract
Reliable bridge condition assessment is considered the first step, and perhaps one of the most essential elements, of an efficient bridge management system. This consideration stems from the fact that available assessment inputs are constantly interpreted for maintenance decisions and budget allocation to the deserving, intervention-needy bridges within a region's inventory. Thus, carrying out effective bridge assessment is vital to ensure the safety and sustainability of the bridge infrastructure. In practice, the evaluation of concrete bridges is mostly conducted on the basis of visual inspection, associated with considerable uncertainty and subjectivity inherent in human judgments. Additionally, conclusions are often drawn in the absence of a thorough review of critical factors. Therefore, to circumvent the existing limitations, this study proposes a fuzzy hierarchical evidential reasoning approach for detailed condition assessment of concrete bridges under uncertainty. The essence of this framework addresses the treatment and aggregation of detected bridge defect measurements systematically to establish an enhanced platform for reliable bridge assessment. The proposed approach is facilitated by a hierarchy structure that models the several levels of a concrete bridge under assessment: bridge components, structural elements, and, most particularly, the measured defects. A belief structure is employed to grasp probabilistic uncertainty (ignorance) in the assessment, while fuzzy uncertainty (subjectivity) is processed through a set of collectively exhaustive fuzzy linguistic variables. Eventually, the Dempster–Shafer theory is used within the suggested framework for accumulating supporting pieces of evidence toward a comprehensive and educated overall condition assessment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".