AI-Driven Real-Time Monitoring and Prediction Framework for Surface Degradation and Corrosion Resistance Assessment
Bibliographic record
Abstract
Surface deterioration and corrosion are major challenges facing industrial maintenance, structural failure and higher operating costs. Convention methods have limitations in lack of real-time capability and incorporation of environmental information, making their predictive capability lower. In this study, an adaptive and intelligent real-time corrosion monitoring and prediction were introduced. It introduces a new Dual-Stage Vision-PhysicsHybrid Transformer (DVPH-Transformer) which combines visual surface feature learning with environmental parameter integration. The model utilizes a Swin Transformer and Convolutional Neural Network (CNN) to capture local and global surface patterns, while a physics-aware attention network integrates important environmental parameters like humidity, temperature, and exposure time. The model is trained and tested on the Corrosion Detect dataset with tools such as TensorFlow and OpenCV. Experimental results demonstrate the DVPH-Transformer outperforms all other methods with 99.4% accuracy, 95.1% precision, 96.3% recall, and 95.7% F1 score. The proposed study guarantees robust, and highly accurate predictive corrosion management.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".