Unlike traditional visual diagnosis, the AI analyzes each pixel of the images captured by the drones to distinguish between: • Surface corrosion vs. structural corrosion with loss of section. • Coating detachment. • Accumulation of material that accelerates degradation. 5.2. Model Training and Validation Process The robustness of the AI comes from training with a massive dataset composed of thousands of images of real mining infrastructures (concentrating plants, ports, chutes). • Labeling: Integrity experts mark degraded areas in the original images to create the "Ground Truth". • Supervised Learning: The neural network learns to identify the visual characteristics (texture, color, patterns) associated with each type of failure. • Validation: The model is tested with new images, measuring its accuracy to ensure that the diagnosis is consistent and superior to that of the human eye. 5.3. Damage Quantification under ASTM Standard 610 The system translates the identified pixels into a standardized metric. By calculating the percentage of affected metal surface relative to the total area of the component, the AI assigns a numerical grade according to ASTM 610 (Scale from 0 to 10). • Advantage: This automation ensures that the evaluation is objective and repeatable, regardless of the inspector in charge of the work. 5.4. Decision Engine: Automated Mitigation Recommendations The innovation culminates in an engineering rules engine that, based on AI diagnosis and asset criticality, generates proactive actions: • Critical - ASTM 0-3: Automatic generation of red alert. Recommended action: Detailed inspection by a specialist, access restriction (ECF22), and development of reinforcement engineering. • Moderate - ASTM 4-6: Recommended action: Mechanical cleaning (SSPCSP3/SP10) and application of specific industrial coatings. • Slight - ASTM 7-10: Recommended action: Predictive monitoring and inclusion in the long-term maintenance plan.
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