CONDITION ASSESSMENT OF FRP-STRENGTHENED CONCRETE BRIDGE DIAPHRAGMS USING NON-DESTRUCTIVE TESTING
Bibliographic record
Abstract
Carbon fibre-reinforced polymer (CFRP) materials are lightweight, corrosion-resistant composite materials extensively used to strengthen or retrofit deteriorated concrete bridge components. The retrofitting process is usually a multi-stage manual process with the inherent capability of introducing defects at the various stages of work. In this paper, a methodological approach involving various nondestructive testing (NDT) techniques has been developed for a detailed condition assessment of the state of damage in deteriorated concrete bridge diaphragms. These three diaphragms from a major Canadian bridge were externally retrofitted with multiple layers of CFRP materials and subjected to years of environmental exposure. In addition, a detailed comparison of results obtained from the NDTs and visual inspection is presented. Several issues were identified, including CFRP delamination, material incompatibility, discolouration due to corrosion, inter-fibre cracks, and fundamental problems arising from the construction of the bridge diaphragms and installation of the CFRP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".