2007 Fiscal Year Final Research Report Summary
Structural health diagnosis method based on a system identification technique of self-learning type for bi-axial nonlinear vibration system
| Project/Area Number |
18560542
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Building structures/materials
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| Research Institution | Tokyo University of Science (2007)
Tohoku University (2006) |
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Principal Investigator |
KURITA Satoshi Tokyo University of Science, Faculty of Engineering, Professor (90195553)
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| Co-Investigator(Kenkyū-buntansha) |
栗田 哲 東京理科大学, 工学部, 教授 (90195553)
KANAZAWA Kenji Central Research Institute of Electric Power Industry, 地球工学研究所, Research Scientist (00371435)
MAEDA Masaki Tohoku University, School of Engineering, Associate Professor (30262413)
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| Project Period (FY) |
2006 – 2007
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| Keywords | Structural health diagnosis / Damage / System identification / Nonlinear / vibration system |
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| Research Abstract |
This project proposed a structural health diagnosis method using a system identification technique. The structural health diagnosis method evaluates the damage level of a building from the earthquake responses of a building estimated using the identified structural model. The earthquake damages of some buildings were estimated by the structural health diagnosis method. The buildings are installed the observation system of earthquake response and monitored the response of vibration. The feasibility study of the structural health monitoring using the structural response to weak wind was performed.
The proposed identification technique estimates the parameters of nonlinear story shear-deformation models for a building from earthquake response records and updates automatically the parameters after earthquakes. The nonlinear story-deformation relationship takes bi-axial deformation into account. The structural health diagnosis method evaluates the reliability of the estimated damage level from the estimate error of the model parameters based on stochastic theory.
The results of the structural health diagnosis using the proposed method illustrate the affectivity in damage detection. The data of the response to weak wind is useful in the structural health diagnosis.
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