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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Structural Engineering and Mechanics, An Int'l Journal Structural Engineering and Mechanics, An Int'l Journal Vol.71 No.5
- 발행연도
- 2019.1
- 수록면
- 545 - 552 (8page)
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초록· 키워드
오류제보하기
The analysis and design of complex structures like sandwich-panel elements are difficult; the use of finite element method for the analysis is complicated and time consuming when non-linear effects are considered. On the other hand, artificial neural network (ANN) models can capture the non-linear effects and its application requires lesser computational demand. Two ANN models were trained, tested and validated to compute the force for a given displacement of a sandwich-type roof element; 2555 force and element deformation pairs were used for training the ANN models. For the models trained without considering the damping effect, there were two values in the input layer: maximum displacement and current displacement, and for the model considering damping, displacement from the previous step was used as an additional input. Totally, 400 ANN models were trained. Results show that there is a good agreement between the experimental and simulated data, and the models showed a good performance with a mean square error value of 4548.85. Both the ANN models could simulate the inelastic behaviour, loss of rigidity, and evolution of permanent displacements. The models could also interpolate and extrapolate, which enables them to be used as an analysis and design tool for such complex elements.
목차
등록된 정보가 없습니다.
참고문헌 (29)
참고문헌 신청
Bulent Akbas / 2006 / A neural network model to assess the hysteretic energy demand in steel moment resisting frames / Structural Engineering and Mechanics 23(2):177~193
J. Amani / 2012 / Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network / Scientia Iranica 19(2):242~248
2018 / ASTM E564-06, Standard Practice for Static Load Test for Shear Resistance of Framed Walls for Buildings / ASTM International
2015 / ASTM E72-15, Standard Test Methods of Conducting Strength Tests of Panels for Building Construction / ASTM International
Mojtaba Sheikhi Azqandi / 2018 / Optimization of spring back in U-die bending process of sheet metal using ANN and ICA / Structural Engineering and Mechanics, An Int＇l Journal 65(4):447~452
J. Amani / 2012 / Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network / Scientia Iranica 19(2):242~248
2018 / ASTM E564-06, Standard Practice for Static Load Test for Shear Resistance of Framed Walls for Buildings / ASTM International
2015 / ASTM E72-15, Standard Test Methods of Conducting Strength Tests of Panels for Building Construction / ASTM International
Mojtaba Sheikhi Azqandi / 2018 / Optimization of spring back in U-die bending process of sheet metal using ANN and ICA / Structural Engineering and Mechanics, An Int＇l Journal 65(4):447~452
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