FEA for improved implementation of IRT for monitoring of concrete bridges
Bibliographic record
Abstract
Infrared thermography (IRT) is a non-destructive technique (NDT) with the potential for contactless and wide-area monitoring of concrete structures like bridges in transportation networks. Dealing with practical challenges of IRT such as the determination of a favourable timeframe for data collection, detection of defects of various types and geometry, differentiation of the true concrete defects from environmental and operational effects, and so on only by laboratory experiments is time-consuming, arduous, and costly. Therefore, finite element analysis (FEA) is an indispensable tool for complementing laboratory experiments and addressing the practical challenges facing the implementation of IRT for structural health monitoring (SHM) of concrete structures. This paper presents the FEA of concrete slabs with subsurface defects in the LUSAS software. The FE models are validated based on surface temperatures of concrete slabs with subsurface defects measured in the laboratory by an infrared camera and used to estimate the variation of thermal contrast on the surface with depth of defect. In addition, they are used to estimate the amount of energy required for the creation of minimum safe detectable thermal contrast recommended by ASTM D4788-03 standard (0.5°C) and other criteria. Such FEA estimations will provide a basis for decision-making, feasibility assessment, and improving the practical implementation of IRT, especially for early-stage detection of defects at rebar depth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".