| 研究実績の概要 |
This year, we have enhanced the algorithms developed in previous years and demonstrate the effectiveness of the proposed method in estimating the local hysteric behaviors of dynamic systems with limited measurement data, as well as its ability to successfully overcome numerical instabilities. Specifically, we proposed a computationally practical approach for recovering the local hysteric behavior based on the combination of Kalman filter and unsupervised autoencoders. In the autoencoder, the measurement data is encoded and the feature of its noise level is first learned in the space of the latent variable. Then another neural network is used to predict system full states with ground motion and latent variable as input. These predictions are used for measurement data reconstruction by a physical guided decoder with the linear state equation from the Kalman filter. The improved algorithm is validated by a numerical example of a multi-story building with dampers on each floor and an experimental test conducted on a two-story base-isolated shear structure. In conclusion, the above-mentioned approach demonstrates computational stability with less measured data, making it more promising for real-world structural health monitoring systems.
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