Two-tier data fusion method for bridge condition assessment
Bibliographic record
Abstract
Fusing collected inspection data provides comprehensive and relatively more accurate diagnostics of defects and accordingly more accurate condition assessment of structures. This paper presents a new two-tier method that utilized data fusion methods for condition assessment of reinforced concrete bridge decks. The method utilizes pixel and feature levels fusion of data collected from multiple nondestructive evaluation (NDE) methods such as ground penetrating radar, impact echo, half-cell potential, and electrical resistivity. Data and measurements of NDE methods are extracted from the Iowa Highway research board project 2011 report for three case studies. It is observed from the three cases that each level of data fusion has its unique advantage. The power of pixel level fusion lies in its ability to provide an overview of bridge deck deterioration in one map as it appears in the fused image. On the other hand, feature fusion works better when only specific types of defects such as corrosion, delamination, and deterioration captured from inspection carried out by each of technologies referred to above. The proposed method is tested against filed inspection methods and core sample results described in the three case studies. The main findings of this research recommend utilizing data fusion in two levels as a new method to facilitate and enhance the confidence and capabilities of inspectors in interpretation of the NDE test results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".