Machine learning (ML) techniques are widely used in structural health monitoring (SHM) and non-destructive testing (NDT), but the learning process, the learned models, and the prediction consistency are poorly understood. This work investigates and compares a wide range of ML models and algorithms for the detection of hidden damage in materials monitored using low-cost strain sensors. The investigation is performed by means of a multi-domain simulator imposing a tight coupling of physical and sensor network simulation in the real-time scale. The device under test is approximated by using a mass-spring network and a multi-body physics solver.
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