Structural Engineering and Mechanics

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Probabilistic shear strength models for reinforced concrete beams without shear reinforcement

  • Song, Jun-Ho (Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign) ;
  • Kang, Won-Hee (Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign) ;
  • Kim, Kang-Su (School of Architecture and Architectural Engineering, University of Seoul) ;
  • Jung, Sung-Moon (Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering)
  • Received : 2008.10.01
  • Accepted : 2009.10.16
  • Published : 2010.01.10
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Abstract

In order to predict the shear strengths of reinforced concrete beams, many deterministic models have been developed based on rules of mechanics and on experimental test results. While the constant and variable angle truss models are known to provide reliable bases and to give reasonable predictions for the shear strengths of members with shear reinforcement, in the case of members without shear reinforcement, even advanced models with complicated procedures may show lack of accuracy or lead to fairly different predictions from other similar models. For this reason, many research efforts have been made for more accurate predictions, which resulted in important recent publications. This paper develops probabilistic shear strength models for reinforced concrete beams without shear reinforcement based on deterministic shear strength models, understanding of shear transfer mechanisms and influential parameters, and experimental test results reported in the literature. Using a Bayesian parameter estimation method, the biases of base deterministic models are identified as algebraic functions of input parameters and the errors of the developed models remaining after the bias-correction are quantified in a stochastic manner. The proposed probabilistic models predict the shear strengths with improved accuracy and help incorporate the model uncertainties into vulnerability estimations and risk-quantified designs.

Keywords

References

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