DEVELOPMENT OF PROGRAM ON NONLINEAR STRUCFURAL ANALYSIS USING NEURAL NETWORK
| Project/Area Number |
13555134
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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 | 展開研究 |
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| Research Field |
構造工学・地震工学
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| Research Institution | Kyushu University |
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Principal Investigator |
MAZDA Taiji KYUSHU UNIVERSITY, CIVIL ENGINEERING, ASSOCLATE PROFESSOR, 工学研究院, 助教授 (50264065)
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| Co-Investigator(Kenkyū-buntansha) |
OTSUKA Hisanori KYUSHU UNIVERSITY, CIVIL ENGINEERING, PROFESSOR, 工学研究院, 教授 (70108653)
YABUKI Wataru KYUSHU UNIVERSITY, CIVIL ENGINEERING, RESEARCH ASSOCIATE, 工学研究院, 助手 (70304748)
YAMAMOTO Kosuke CRIEPI, SENIOR RESEARCHER, 構造部, 主任研究員
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| Project Period (FY) |
2001 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥12,800,000 (Direct Cost: ¥12,800,000)
Fiscal Year 2003: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2002: ¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 2001: ¥8,800,000 (Direct Cost: ¥8,800,000)
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| Keywords | neural network / non-linear / hysteretic behavior / dynamic response analysis / high damping rubber bearing / 非線形履歴 / 免震支承 |
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| Research Abstract |
Generally, in formulating a spring-mass model for hysteric behavior of materials and members with inelastic characteristic, a mathematical model based on load-deformation experimental results is considered. The model must approximate the inelastic hysteresis of the material. However, assumption of material's behavior using mathematical models is crucial, since it may cause serious errors if inappropriate model is applied for a particular situation. This research describes multiple layered neural network to simulate the non-linear hysteretic behavior like Ramberg-Osgood model, modified bilinear model and Takeda model. In this study, based on the pattern recognition ability of neural network, non-linear hysteretic behavior was modeled by the network directly without replacing it with a mathematical model. The effectiveness and applicability of the network in numerical analysis were evaluated. Generalized multiple layered neural network to evaluate non-linear hysteretic curve was constructed. The network can recognize well the three types of hysteretic curve. The network is available as a subroutine of non-linear spring in dynamic response analysis.
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Report
(4 results)
Research Products
(10 results)
