| Project/Area Number |
07455187
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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 | 一般 |
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| Research Field |
構造工学・地震工学
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| Research Institution | MUSASHI INSTITUTE of TECHNOLOGY |
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Principal Investigator |
HOSHIYA Masaru Musashi Institute of Technology Professor, 工学部, 教授 (30061518)
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| Co-Investigator(Kenkyū-buntansha) |
MARUYAMA Osamu Musashi Institute of Technology Lecturer, 工学部, 助教授 (50209699)
NODA Shigeru Tottori University Assistant Professor, 工学部, 助教授 (80135532)
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| Project Period (FY) |
1995 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥7,300,000 (Direct Cost: ¥7,300,000)
Fiscal Year 1997: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1996: ¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1995: ¥3,800,000 (Direct Cost: ¥3,800,000)
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| Keywords | Kriging / Conditional Simulation / Conditional Non-Gaussian Field / Identification / Kalman Filter / Earthquake Wave Propagation / Stochastic Interpolation / Conditional Non-Gaussian Field. / Conditinal Non-Gaussian Field. |
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| Research Abstract |
Throughout the history of stochastic process and field simulation, unconditional simulation has been the main theme, it has been widely applied in structural, earthquake and related engineering. However, development of the method of conditional simulation for stochastic process and fields, i.e. simulation of stochastic process fields under the condition that some of their sample realizations are observed and known is of relatively recent origin, in spite of its significant usefulness in engineering applications.
The objective of this research is to develop a real time monitoring system of earthquake wave propagation, which may be contribute to the development of the earthquake disaster mitigation program. The major findings of this research are summarized as follows.
1. A time domain interpolation method of conditional simulation on a temporal and spatial gaussian field was developed. In this method a simple auto-regressive modeling which expresses the very property of spatially and temporally deviating phenomena was employed.
2. A theoritical formulation is presented to estimate conditional non-gaussian translation stochastic fields. Proposed estimation theory of non-gaussian conditional stochastic fields is based both on the estimation theory of conditional gaussian stochastic fields and on the transformation of non-gaussian variables to gaussian variables taking account of the correlation between non-gaussian variables.
3. The concept of conditional stochastic finite element method is proposed. The method developed here may be applied to updating a general stochastic field such as the physical behavior of the system was described by the finite element modeling.
4. Some important topics, sampling method for stochastic fields, fast wavelet transformation for nonstationary phenomena and future perspectives of this research are also suggested.
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