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Trường DCGiá trị Ngôn ngữ
dc.contributor.authorChen, Yue-
dc.contributor.authorGu, Chongshi-
dc.contributor.authorShao, Chenfei-
dc.contributor.authorQin, Xiangnan-
dc.date.accessioned2020-06-06T13:14:08Z-
dc.date.available2020-06-06T13:14:08Z-
dc.date.issued2019-
dc.identifier.issn1687-8086-
dc.identifier.issn1687-8094 (eISSN)-
dc.identifier.otherBBKH1429-
dc.identifier.urihttp://thuvienso.vanlanguni.edu.vn/handle/Vanlang_TV/19087-
dc.description"Hindawi Advances in Civil Engineering Volume 2019, Article ID 9742961, 17 pages https://doi.org/10.1155/2019/9742961"vi
dc.description.abstract"The deformation behavior of rockfill is significant to the normal operation of concrete face rockfill dam. Considering both the nonlinear mechanical behavior and long-term rheological deformation, the E-ν model and modified Burgers model are coupled to describe the deformation behavior of the rockfill materials. The coupled E-ν and Burgers model contains numerous parameters with complex relationship, and an efficient and accurate inversion analysis is in demand. The sensitivity of the parameters in the coupled E-ν and modified Burgers is analyzed using the modified Morris method initially. Then, a new approach of parameter back analysis is proposed by combining back-propagation neutral network (BPNN) and Cuckoo Search (CS) algorithm. The numerical example shows that parameters K , R f , and φ 0 as well as G are more sensitive to the deformation of the rockfill body. The inversion analysis for these four parameters and η 2 , E 2 , and A as well as B in modified Burgers model is performed by the CS-BPNN algorithm. The numerical results demonstrate that the parameters obtained with the proposed method are reasonable and its feasibility is validated.vi
dc.language.isoenvi
dc.publisherHindawi Limited,vi
dc.subjectSensitivity analysisvi
dc.subjectRheologyvi
dc.subjectConcretevi
dc.subjectParameter sensitivityvi
dc.subjectArtificial neural networkvi
dc.subjectGenetic algorithmsvi
dc.subjectNeural networksvi
dc.subjectDeformationvi
dc.subjectMechanical propertiesvi
dc.subjectRockfill damsvi
dc.subjectRheological propertiesvi
dc.subjectDemand analysisvi
dc.subjectParameter modificationvi
dc.subjectSearch algorithmsvi
dc.subjectConcrete damsvi
dc.subjectMathematical modelsvi
dc.subjectEngineeringvi
dc.subjectMathematical problemsvi
dc.titleParameter Sensitivity and Inversion Analysis for a Concrete Face Rockfill Dam Based on CS-BPNNvi
dc.typeOthervi
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