Reliability of probabilistic numerical data for training machine learning algorithms to detect damage in bridges
In structural health monitoring of bridges, machine learning algorithms for damage detection
are typically trained using an unsupervised learning strategy, with data gathered from …
are typically trained using an unsupervised learning strategy, with data gathered from …
Physics-informed neural networks for structural health monitoring: a case study for Kirchhoff–Love plates
Physics-informed neural networks (PINNs), which are a recent development and incorporate
physics-based knowledge into neural networks (NNs) in the form of constraints (eg …
physics-based knowledge into neural networks (NNs) in the form of constraints (eg …
Hybrid training of supervised machine learning algorithms for damage identification in bridges
Hybrid approaches for training machine learning algorithms to identify damage in bridges
rely on the use of both monitoring and numerical data. While monitoring data account for …
rely on the use of both monitoring and numerical data. While monitoring data account for …