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- W4386229313 abstract "The physics-informed neural network (PINN) was recently introduced as a potential technique to solve both forward and inverse structural dynamic problems by integrating physical differential equations and boundary conditions into the network to minimize the measurement data required for model training. However, it was only in the stage of solving the simple theoretical problems. This study applied PINN to solve an inverse problem to identify dynamic structural parameters for a prestressed concrete girder bridge built 40 years ago. The model input data were acceleration data measured by three sensors under vehicle load in two states (i.e., before and after strengthening by external prestressing cable) and combined with the partial differential equation (PDE) of beam bending and boundary conditions to minimize a loss function. The modal properties obtained from the PDE with the parameters identified from PINN were compared with those experimentally identified by Eigensystem Realization Algorithm (ERA) technique." @default.
- W4386229313 created "2023-08-29" @default.
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- W4386229313 date "2023-01-01" @default.
- W4386229313 modified "2023-09-30" @default.
- W4386229313 title "A Preliminary Study on Physics-Informed Machine Learning-Based Structure Health Monitoring for Beam Structures" @default.
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- W4386229313 doi "https://doi.org/10.1007/978-3-031-39117-0_50" @default.
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