| Project/Area Number |
16K18184
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Building structures/Materials
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| Research Institution | Kyoto University |
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Principal Investigator |
Fujita Kohei 京都大学, 工学研究科, 助教 (40648713)
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| Project Period (FY) |
2016-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2017: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2016: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 構造ヘルスモニタリング / システム同定 / 常時微動観測 / 層剛性同定 / 超高層建物 / 損傷同定 / 曲げせん断型モデル / 条件付き確率 / ベイズ推定 / せん断型モデル / 建築構造 / スマートセンサ情報システム |
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| Outline of Final Research Achievements |
For the purpose of developing a smart structural health monitoring system for super high-rise building, the story stiffness identification method using a shear-bending model was proposed to directly evaluate the story stiffnesses. The shear and bending model is useful to take into account the influence of bending deformation of high-rise buildings. In the statistical approach method, by supposing the floor rotation angle can be obtained as the additional measurement data, we proposed a new algorithm to correct the lowest mode shape of rotation angle. In this method, the probabilistic distribution of the lowest mode shape of floor rotation angle can be updated based on the conditional probability problem. By using the inverse-mode method for the shear-bending model, the bending stiffness can be identified stably from the updated the lowest-mode shape of floor rotation angle.
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