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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.21 No.5
- 발행연도
- 2018.1
- 수록면
- 669 - 675 (7page)
이용수
초록· 키워드
오류제보하기
Under braking forces of a freight train, there are great longitudinal structural responses of a large freight railway cable-stayed bridge. To alleviate such adverse reactions, viscous dampers are required, whose parametric selection is one of important and arduous researches. Based on the longitudinal dynamics vehicle model, responses of a cable-stayed bridge are investigated under various cases. It shows that there is a notable effect of initial braking speeds and locations of a freight train on the structural responses. Under the most unfavorable braking condition, the parameter sensitivity analyses of viscous dampers are systematically performed. Meanwhile, a mixing method called BPNN-NSGA-II, combining the Back Propagation neural network (BPNN) and Non-Dominated Sorting Genetic Algorithm With Elitist Strategy (NSGA-II), is employed to optimize parameters of viscous dampers. The result shows that: 1. the relationships between the parameters of viscous dampers and the key longitudinal responses of the bridge are high nonlinear, which are completely different from each other; 2. the longitudinal displacement of the bridge main girder significantly decreases by the optimized viscous dampers.
목차
등록된 정보가 없습니다.
참고문헌 (19)
참고문헌 신청
Chou, M. / 2007 / Modelling and model validation of heavy-haul trains equipped with electronically controlled pneumatic brake systems / Control Eng. Pract. 15(4):501~509
Cole, C. / 2006 / Simulated comparisons of wagon coupler systems in heavy haul trains / Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 220(3):247~256
Dai, H. / 1997 / Effects of learning parameters on learning procedure and performance of a BPNN / Neural Networks 10(8):1505~1521
Deb, K. / 2002 / A fast and elitist multiobjective genetic algorithm: NSGA-II / IEEE T. Evolut. Comput. 6(2):182~197
Ghose, D.K. / 2010 / Prediction of water table depth in western region, orissa using BPNN and RBFN neural networks / J. Hydrology 394(3-4):296~304
Cole, C. / 2006 / Simulated comparisons of wagon coupler systems in heavy haul trains / Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 220(3):247~256
Dai, H. / 1997 / Effects of learning parameters on learning procedure and performance of a BPNN / Neural Networks 10(8):1505~1521
Deb, K. / 2002 / A fast and elitist multiobjective genetic algorithm: NSGA-II / IEEE T. Evolut. Comput. 6(2):182~197
Ghose, D.K. / 2010 / Prediction of water table depth in western region, orissa using BPNN and RBFN neural networks / J. Hydrology 394(3-4):296~304
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