Development of Seismic Damage Assessment Method for Instrumented Large Civil Structures using Sparse Representation Techniques
| Project/Area Number |
18K04320
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 22020:Structure engineering and earthquake engineering-related
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| Research Institution | Yokohama National University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | structural assessment / seismic monitoring / sparse representation / seismic isolation / system identification / bridge monitoring / cable-stayed bridge / seismic response / seismic records / seismic-isolated bridge / wavelet decomposition / moveable bearing / structural monitoring / bearing malfunction / System Identification / Seismic Records / Sparse Representation / Wavelet Decomposition / Seismic Isolated Bridge / seismic responses / long-span bridge / 構造工学 / 地震工学 |
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| Outline of Final Research Achievements |
Structural identification by sparse representation was developed and tested using full-scale seismic monitoring data of three bridges. Modal and FE-based structural identifications were developed according to complexity of structural system. In Tokachi cable-stayed bridge the sparse time-invariant and time-variant recursive subspace identification methods were implemented. Friction damping at the movable bearings was quantified by modal-based inverse analysis and sparse regularization method.In the Katsuta Viaduct, techniques were developed for detecting bearing malfunction from the seismic response using continuous & discrete wavelet transform. Evaluation of bearing condition was conducted by statistical sparse machine-learning technique.In the ShinNakagawa FE-based model was developed and compared with monitoring data. Damages related to isolation bearing were simulated and the results were compared with monitoring from 63 earthquakes in 2017-2020 using sparse model analysis.
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| Academic Significance and Societal Importance of the Research Achievements |
Using various models of sparse representations, the study shows that structural assessment can be performed using modal & FE based simulation and sparse representation techniques.The methods can be used for structural evaluation after large earthquake to detect damages in bridge bearings.
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Report
(5 results)
Research Products
(19 results)
