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Basic Concepts of Structural Behavior • Loads, Vibrations, Stress, Strain, and Deformation • Damage Indicators in Structural Systems • Traditional and Data-Driven Damage Detection Approaches

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Sensors and Measurement Concepts in Structural Systems • Vibration, Displacement, Strain, Acoustic, and Environmental Data • Data Quality, Noise, Missing Values, and Preprocessing • Preparing Structural Data for Learning-Based Analysis

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Fundamentals of Learning-Based Models • Neural Networks and Pattern Recognition • Training, Validation, Testing, and Model Performance • Importance of Data Representation in Structural Applications

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Using Learning Models for Damage Detection • Classification of Healthy and Damaged Structural Conditions • Feature Learning from Structural Response Data • Applications in Crack Detection, Vibration Analysis, and Fault Identification

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Image-Based Monitoring of Cracks and Surface Defects • Signal-Based Analysis for Vibration and Sensor Data • Pattern Recognition in Structural Response Measurements • Combining Visual and Sensor-Based Evidence for Better Assessment

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Predicting Structural Deterioration and Performance Loss • Condition Assessment for Bridges, Buildings, and Industrial Structures • Risk-Based Maintenance Planning • Role of Artificial Intelligence in Safer Infrastructure Decisions

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Case Studies in Structural Health Monitoring • Challenges in Data Availability, Model Reliability, and Field Deployment • Ethical and Practical Considerations in AI-Assisted Infrastructure Monitoring • Future Opportunities in Smart Infrastructure and Resilient Structural Systems