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Time-Dependent Reliability Analysis of RC Deep Beams considering Linear/Nonlinear Creep and Shrinkage Using ANFIS Network and MCS

Author(s):

Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2019
Page(s): 1-15
DOI: 10.1155/2019/2170701
Abstract:

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.

Copyright: © 2019 Seyed Bahram Beheshti Aval et al.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
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  • Reference-ID
    10316998
  • Published on:
    09/07/2019
  • Last updated on:
    02/06/2021
 
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