2022 Fiscal Year Research-status Report
Real-time hysteresis identification in controlled structures based on restoring force reconstruction and Kalman filter
| Project/Area Number |
21K14284
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| Research Institution | Kyoto University |
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Principal Investigator |
郭 佳 京都大学, 農学研究科, 准教授 (50868081)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Keywords | Deep neural network / Force reconstruction / Linear multistep method / Data-driven approach |
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| Outline of Annual Research Achievements |
Despite great progress in identifying the hysteretic forces in structures, less satisfactory hysteretic behaviors with large errors for the nonlinear component reconstruction might still be identified, due to the ill-posedness of the inverse problem. This year, we improved the Kalman filter based restoring force reconstruction method through incorporating deep neural networks into the classical numerical integration method by using a hybridized integration time-stepper. We proposed to use residual network to generally identify the nonlinear behaviors of the system. Compared to the previous method, the newly developed Physics-DNN hybridized integration time-stepping scheme provides stable solution in both of the hysteresis identification and the corresponding structural dynamic analysis.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
In 2021, a real-time hysteresis identification method has been successfully established based on restoring force reconstruction and the Kalman filter. In 2022, this method is combined with the deep learning process so that a mathematical model is involved during the identification process to circumvent the ill-posedness problem. In this way, more stable identification results can be obtained. Besides, not only numerical simulations, experimental tests were also conducted to demonstrate the performance of the method, as planned. However, due to the ongoing spread of COVID-19, this method has not yet been tested using actual field measurement data. Therefore, we have decided to extend this project by one year in order to complete our final validation.
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| Strategy for Future Research Activity |
In 2023, we will continue to enhance the proposed method using advanced computational techniques to make it more practical and feasible for real-world applications. Moreover, the proposed method will be further validated using newly generated numerical data from complex nonlinear structural systems or field measurements from a Structural Health Monitoring system installed in a real high-rise building, if possible. The researching outcome will be summarized and submitted to international academic journal.
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| Causes of Carryover |
In the final fiscal year, the budget will only be allocated for expenses to article costs relating to the research (e.g. books), or registration/participation fees for academic conferences.
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Research Products
(4 results)
