| Project/Area Number |
07555156
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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 | 試験 |
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| Research Field |
Geotechnical engineering
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| Research Institution | Okayama University |
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Principal Investigator |
KOHNO Ichiro Okayama Univ., Faculty of Environmental Science and Technology, Professor, 環境理工学部, 教授 (00025941)
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| Co-Investigator(Kenkyū-buntansha) |
TAKESHITA Yuji Okayama Univ., Faculty of Environmental Science and Technology, Lecturer, 環境理工学部, 講師 (90188178)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥6,400,000 (Direct Cost: ¥6,400,000)
Fiscal Year 1996: ¥2,600,000 (Direct Cost: ¥2,600,000)
Fiscal Year 1995: ¥3,800,000 (Direct Cost: ¥3,800,000)
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| Keywords | conservation of groundwater / multilayred aquifer / hydraulic properties / in-situ test / neural network / back analysis / 地下水 |
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| Research Abstract |
Recently, the demand for deep underground excavation and the development and utilization of its resources has increased. This raises a need to predict the behavior of the groundwater in multilayred aquifers in order to promote its conservation. The exact determination of hydraulic properties of each aquifer is very important for the correct groundwater flow prediction. Pumping tests are usually performed under the multilayred conditions. It is, however, difficult to analyze the data obtained from the pumping test under these conditions "analytically".
In this research, a new method of estimating aquifer coefficients from pumping test data in multilayred aquifers is proposed. The soil hydraulic properties, coefficient of permeability and storage are essential data to predict the behavior of groundwater. Pumping tests are usually performed to determine these properties. In this paper, a new approach to evaluate soil hydraulic properties from drawdown curves which are obtained by pumping tests has been developed. In our developed method the patten-matching capability of a neural network is used. The neural network is trained to reconize patterns of drawdown data as input and corresponding hydraulic properties in the confined aquifer as output. The trained network produces output of hydraulic properties when it receives pumping test data as the input patterns. Drawdown data which are observed in an anisotropic confined aquifer are used to evaluate availability of our proposed method.
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