Quebec Bridge Inspection Using Common Nondestructive and Destructive Testing Techniques
Bibliographic record
Abstract
Various nondestructive testing (NDT) techniques are available to evaluate the condition of existing concrete structures. These NDT techniques can help to determine in-situ load carrying capabilities and in turn be used to help develop a cost effective rehabilitation solution. In this project, the four pier caps of Denver Colorado' Quebec Street Bridge over Air Lawn Road were inspected using several different NDT techniques. These tests included: carpenter hammer sounding, Schmidt hammer, and ultrasonic pulse velocity (UPV) testing including tomography. In addition visual testing was used to identify crack patterns and spalling conditions. Contour plotting of the NDT data was completed on individual and combined NDT techniques to better determine the condition of the piers. Equal weighted percentages were assumed in combining the hammer sounding, Schmidt, and direct ultrasonic transmission data. Although hammer sounding and Schmidt rebound techniques were used to determine the condition of the exterior layers of the piers, ultrasound and tomography were used to determine the condition of the interior. Various tomographic slices were completed between adjacent sides and from face to face. After the NDT tests were completed, the data were analyzed, interpreted and recommendations were given to further destructively examine local areas of the piers. Destructive tests included compressive strength, chloride, and petrographic testing. The specific detail of all testing methodologies used in this study will be discussed further along with the specific results for the northwest pier cap.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".