Identifying the local nonlinear hysteretic behaviors in structures arises as an important issue in structural health monitoring. We developed a new hysteretic loop identification framework based on restoring force reconstruction and the Kalman filter. It does not require the a priori knowledge on hysteretic models and transforms nonlinear hysteretic identification into more efficient linear system estimation problem. Augmentation of the restoring forces as state variables is shown to act as the role of Tikhonov regularization.
We propose a new flexible technique for accurate vision-based seismic displacement measurement of building structures via a single non-stationary camera with any perspective view. Whereas most projective rectifications are conducted by specifying the positions of four or more fixed reference points, our method adopts a stratified approach to partially determine the projective transformation from line-based geometric relationships on the world plane. We hope that the proposed method could advance consumer-grade camera system for vision-based structural measurement one more step, from laboratory environments to real world structural health monitoring systems.
We present a novel sparse-regularized minimum constitutive relation error approach for structural damage identification. The inverse identification problem is treated as a nonlinear optimization problem whose objective function is just the constitutive relation error. To circumvent the ill-posedness of the inverse problem that is caused by use of the possibly insufficient modal data, the sparse regularization is introduced where a sparse regularization term is added to the original CRE function.
LINKS: https://doi.org/10.1002/stc.2255, https://doi.org/10.1016/j.ymssp.2019.106496
Wang L, Guo J, Takewaki I. (2021). Real-time hysteresis identification in structures based on restoring force reconstruction and Kalman filter. Mechanical Systems and Signal Processing, 150: 107297, doi:10.1016/j.ymssp.2020.107297
Guo J, Jiao J, Fujita K, Takewaki I. (2020). A spectrum-driven damage identification by minimum constitutive relation error and sparse regularization. Mechanical Systems and Signal Processing, 136: 106496, doi:10.1016/j.ymssp.2019.106496
Guo J, Jiao J, Fujita K, Takewaki I. (2019). Damage identification for frame structures using vision-based measurement. Engineering Structures, 199: 109634, doi:10.1016/j.engstruct.2019.109634
Guo J, Deng K, Wang L, Takewaki I. (2019). Physical-based parametrization and local damage identification for frame type structures using response sensitivity approach in time domain. Structural Control and Health Monitoring, 26(10): e2412, doi:10.1002/stc.2412
Guo J, Wang L, Takewaki I. (2019). Frequency response-based damage identification in frames by minimum constitutive relation error and sparse regularization. Journal of Sound and Vibration, 443: 270-292, doi:10.1016/j.jsv.2018.11.020