Deterioration Assessment of Infrastructure Using Fuzzy Logic and Image Processing Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The safety and serviceability of civil infrastructures have to be ensured either as part of a periodic inspection program or immediately following a given hazardous event. The use of digital imaging techniques to identify the deformed or deteriorated surfaces of structures is a substantial area of research and aims to investigate a number of unknown parameters, including damage quantification and condition rating. This manuscript illustrates the integration of previously developed fuzzy logic–based decision-making tools with the currently developed image processing algorithm to quantify the damage for the condition rating of civil infrastructures. The proposed integrated framework exploits visual specifics of different elements of the infrastructure to perform automated evaluation of structural anomalies such as cracks and surface degradation. Two different image segmentation tools, (1) bottom hat transform and (2) hue, saturation, color (HSV) thresholding, are applied to identify the surface defects. The developed image processing software is used with the fuzzy set framework proposed in the previous research to gauge the damage indices due to various deterioration types like corrosion, alkali aggregate reaction, freeze–thaw attack, sulfate attack, acid attack or loading, fatigue, shrinkage, and honeycombing. Case studies of a long-span bridge and a warehouse building are illustrated for concept validation. The refined comprehensive method is presented as a graphical user interface (GUI) to facilitate the real-time condition assessment of civil infrastructures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it