2020 Fiscal Year Research-status Report
Development of Seismic Damage Assessment Method for Instrumented Large Civil Structures using Sparse Representation Techniques
| Project/Area Number |
18K04320
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| Research Institution | Yokohama National University |
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Principal Investigator |
シリンゴリンゴ ディオン 横浜国立大学, 先端科学高等研究院, 特任教員(准教授) (60649507)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Keywords | system identification / seismic records / sparse representation / wavelet decomposition / bearing malfunction |
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| Outline of Annual Research Achievements |
Implementation of structural identification by sparse representation was carried out using real data from fullscale seismic monitoring of Katsuta viaduct and ShinNakagawa cable-stayed bridge.FE model was also developed in Abaqus for ShinNakagawa bridge for comparison with monitoring results. The monitoring data consist of 63 seismic events in 45 months (2017-2020)using wireless sensor networks. The full-scale implementation is continuation of the previous year's results where model was studied only in FEM of fictitious multi-span bridge. The objective of implementation is to investigate the capability of detecting seismic isolation malfunction using monitoring data by CWT-DWT and Sparse model analysis and compared the results with simulation using finite element model.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
There was some delay in the process due to the inability to properly collect and analyze the measurement data. The pandemic and enforcement of state of emergency have impacted the progress of data gathering and research work to some extent.
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| Strategy for Future Research Activity |
In the third and final year, the research will focus on completion of the following tasks:
(1)Completion of finite element model for the benchmark Shin-Nakagawa bridge and implement several scenarios of structural damage caused by earthquake and generate data for comparison with the recorded seismic monitoring data.
(2)Implement more cases of feature extraction and unsupervised learning novelty detection in the finite element simulation.
(3)Implement the sparse representation directly to monitoring data to extract essential parameters related to the condition of seismic isolation bearing with respect to type and scale/level of earthquake excitation.
(4)Dissemination of research results and paper writing.
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| Causes of Carryover |
1.Continuation of use of software and hardware for computation.
2.Attending conferences and workshop for dissemination of research.
3.Site visit and gathering data related to research
Reasons: Continuation of use of software for computation because of the license and postponed planned research. Some activities such as conference, workshop, site visit etc were suspended or canceled last year due to pandemic covid19. The conferences that we plan to attend are rescheduled for this year. The same reason also applies to site visits.
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