1993 Fiscal Year Final Research Report Summary
Development of an Intelligent Sub-structuring Loading-test System for Large-scale Frames
| Project/Area Number |
04555138
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| Research Category |
Grant-in-Aid for Developmental 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 | University of Tokyo |
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Principal Investigator |
OHI Ken-ichi University of Tokyo, IIS, Associate Prof., 生産技術研究所, 助教授 (90126003)
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| Co-Investigator(Kenkyū-buntansha) |
HARADA Kazuaki Tokyo Electric Power Co.INC, Research Engineer, 研究員
CHEN Yiyi University of Tokyo, IIS, Research Associate, 生産技術研究所, 助手 (00242123)
KOU Ki University of Tokyo, IIS, Research Associate, 生産技術研究所, 助手 (80186600)
TAKANASHI Koichi University of Tokyo, IIS, Prof.POSITION, 生産技術研究所, 教授 (60013124)
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| Project Period (FY) |
1992 – 1993
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| Keywords | Sub-structuring Technique / Hybrid Analysis / Large-scale Frame / Application of Neural Network / On-line Test |
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| Research Abstract |
1. An intelligent loading-test system was developed by using hybrid sub-structuring technique. This system provides a useful tool to investigate the inelastic behaviors of large-scale frame.
2. Three types of flexible steel frame models were supposed, and they were tested by using the developed test system. The reliability of the system was checked too.
3. One of the characteristics of flexible frames is that the moment gradient along the column. A special test device is designed is changeable. A special test device used to test the columns under such a stress condition was designed.
4. The procedure of removal of the unbalanced forces produced during test was proposed. Four different predictors were tried during the tests and the results were compared after tests. (1) The simple predictor with the shortest time-consuming is elastic beam-column model ; however the effect to remove unbalanced force is not so good as the other models. (2) Multi-spring inelastic beam-column predictor makes unbalanced forces minimized but with the longest demand of time. An alternative is the use of a two-component bilinear model. (4) Neural network predictor is first tried in on-line test.
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