Developing algorithms to analyze features and quantify the structure's health. Core Machine Learning Algorithms in SHM
Defining the life-safety or economic justification for monitoring, identifying the types of damage to be detected, and establishing the operational environment. Developing algorithms to analyze features and quantify the
Identifying damage-sensitive properties from raw data, such as modal frequencies, mode shapes, or signal statistics. such as modal frequencies
Machine Learning and Statistical Pattern Recognition (Pages: 295-320) * Summary. * PDF. * References. * Request permissions. Wiley Online Library STRUCTURAL HEALTH MONITORING - download Developing algorithms to analyze features and quantify the
Selecting sensor types (e.g., accelerometers, strain gauges), placement, and sampling rates. This step often includes data cleaning to remove noise or outliers that could lead to false positives.
Structural Health Monitoring: A Machine Learning Perspective
Structural Health Monitoring: A Machine Learning Perspective