| Project/Area Number |
04452241
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Building structures/materials
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| Research Institution | Hokkaido University |
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Principal Investigator |
KAGAMI Hiroshi Hokkaido Univ., Fac.Eng., Professor, 工学部, 教授 (70016476)
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| Co-Investigator(Kenkyū-buntansha) |
SAKAI Shinobu Hokkaido Univ., Fac.Eng., Instructor, 工学部, 助手 (60235108)
MURAKAMI Hitomi Hokkaido Univ., Fac.Eng., Instructor, 工学部, 助手 (10201807)
OKADA Shigeyuki Hokkaido Univ., Fac.Eng., Associate Professor, 工学部, 助教授 (50125291)
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| Project Period (FY) |
1992 – 1994
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| Project Status |
Completed (Fiscal Year 1994)
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| Budget Amount *help |
¥6,300,000 (Direct Cost: ¥6,300,000)
Fiscal Year 1994: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1993: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 1992: ¥3,600,000 (Direct Cost: ¥3,600,000)
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| Keywords | Seismic Input Motion / Neural Network / Strong Motion Observation / Sapporo Metropolitan Area |
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| Research Abstract |
The purpose of this study is to develop a new evaluation system for seismic input motions in urban area utilizing a data processing technique of neural network. Sapporo metropolitan area was taken as a test field and a prototype model was constructed.
At first, neural network data processing techniques were reviewed trough many references from various fields and discussed a way to apply them to seismic input evaluation problems. A prototype model for evaluation of regional distribution of seismic severity was constructed on PC with an ordinal method of analytical approach paying an attention to the future extention. In parallel, seismic intensity data and strong motion records in the area were accumulated. In 1993, during the research period, we had two large damaging earthquakes in Hokkaido and detailed distribution maps of seismic intensities in the area were made through questionnaire surveys. And strong motion records were obtained at two sites by newly installed seismometers. Using these data as teacher's ones, inferring models were constructed adopting neural network data processing and improving the proposed evaluation mopdel.
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