Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này: http://thuvienso.vanlanguni.edu.vn/handle/Vanlang_TV/20475
Nhan đề: Time-Dependent Reliability Analysis of RC Deep Beams considering Linear/Nonlinear Creep and Shrinkage Using ANFIS Network and MCS
Tác giả: Beheshti Aval, Seyed Bahram
Ghabdian, Mohsen
Từ khoá: Linear
Onlinear Creep
Civil Engineering
Năm xuất bản: 2019
Nhà xuất bản: Hindawi Limited
Tóm tắt: This paper comprehensively studies the effects of linear and nonlinear creep and shrinkage on shear strength and deformation of reinforced concrete (RC) deep beams through a time-dependent reliability analysis framework. The three-dimensional finite element-based program of ABAQUS was used to model RC deep beams. A viscoelastic approach was adopted to model linear creep and shrinkage by a user-defined material model developed and implemented in user subroutines UMAT and UEXPAN. The software was initially examined using two experimental results for short/long-term behavior of shallow and deep concrete beams. In the nonlinear range, creep was taken into account by the affinity hypothesis theorem to consider the effect of high-level sustained loading. Due to the complicated and time-consuming nature of the finite element method (FEM), adaptive neuro-fuzzy inference system (ANFIS) and Monte Carlo simulation (MCS) replaced these complex analyses. Finally, based on the numerical results obtained from the analyses of case study samples, it was concluded that, in a serviceability limit state, for RC deep beams, the probability of failure was reduced to less than one-fifth that for the shallow beams. On the contrary, in a strength limit state, a safety factor of about 1.7∼1.8 could be considered for the effect of sustained high-level loading on the shear strength of RC deep beams.
Mô tả: Hindawi Advances in Civil Engineering Volume 2019, Article ID 2170701, 15 pages https://doi.org/10.1155/2019/2170701
Định danh: http://thuvienso.vanlanguni.edu.vn/handle/Vanlang_TV/20475
ISSN: 1687-8086
1687-8094 (eISSN)
Bộ sưu tập: Bài báo_lưu trữ

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