| Project/Area Number |
12555135
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
構造工学・地震工学
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
SATO Tadanobu Kyoto University, Disaster Prevention Research Institute, Professor, 防災研究所, 教授 (00027294)
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| Co-Investigator(Kenkyū-buntansha) |
HONDA Riki Kyoto University, Disaster Prevention Research Institute, Assistant, 防災研究所, 助手 (60301248)
MITA Akira Keio University, Dept. of System Design Eng. Associate Professor, 大学院・理工学研究科, 助教授 (60327674)
SUZUKI Yoshiyuki Kyoto University, Disaster Prevention Research Institute, Professor, 防災研究所, 教授 (50027281)
邸 元 京都大学, 防災研究所, 非常勤研究員
武田 展雄 東京大学, 大学院・工学系研究科, 教授 (10171646)
楊 峻 京都大学, 土木学会, 主任研究員
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥9,200,000 (Direct Cost: ¥9,200,000)
Fiscal Year 2002: ¥2,600,000 (Direct Cost: ¥2,600,000)
Fiscal Year 2001: ¥2,600,000 (Direct Cost: ¥2,600,000)
Fiscal Year 2000: ¥4,000,000 (Direct Cost: ¥4,000,000)
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| Keywords | sensor / memorizing sensor of peak response / Monte Carlo Filter / Kalman filter / structural damage identification / Genetic Algorithm / structural health monitoring / 光ファイバー / 最大歪記憶センサ / 弾性座屈 / キャパシタンス / システム同定 / 適応型モンテカルロフィルタ / 非線形構造システム / 逐次線形化法 / 微動計測 |
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| Research Abstract |
Based on a recent review of system identification, we developed several promising methodologies corresponding to the system identification such as an adaptive H infinity and Kalman filter, a neural network with forgetting capability of network structure and an adaptive Monte Carlo filter. Those algorithms possess efficient time-marching capabilities to identify non-linear and non-stationary structural systems. To improve convergence of non-stationary change of structural parameters we also developed a hybrid algorithm combining the Kalman filter with the Monte Carlo filter and the Genetic algorithm was also introduced into the Monte Cairo filter. Both parametric and non-parametric system identification technologies were also developed suitable to deal with highly nonlinear and time varying systems. Those algorithms were implemented and executed on a high speed laptop computer synchronized with input data through AD converters from an observation system, a structural identification system, which enables a real time identification of non-stationary change of structural parameters.
Simple and inexpensive passive sensors that can monitor the peak strain or displacement were developed. The memorized data can be wirelessly retrieved by introducing an LC circuit into the sensor enabled wireless reading of the data. A prototype sensor was fabricated and tested.
A portable equipment to be used for structural system identifications was developed by combining a wireless data acquisition system with the structural identification system. Efficiency of the developed system was demonstrated by micro-tremor observations of a three stories frame structure.
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