Structural Engineering and Mechanics

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Nonlinear probabilistic shear panel analysis using advanced sampling techniques

  • Strauss, Alfred (Department of Civil Engineering and Natural Hazards, University of Natural Resources and Life Sciences) ;
  • Ju, Hyunjin (School of Architecture and Design Convergence, Hankyong National University) ;
  • Belletti, Beatrice (Department of Engineering and Architecture, University of Parma) ;
  • Ramstorfer, Maximilian (Department of Civil Engineering and Natural Hazards, University of Natural Resources and Life Sciences) ;
  • Cosma, Mattia Pancrazio (Department of Engineering and Architecture, University of Parma)
  • Received : 2022.01.13
  • Accepted : 2022.05.01
  • Published : 2022.07.25
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Abstract

The shear behaviour of reinforced concrete members has been studied over the past decades by various researchers, and it can be simulated by analysing shear panel elements which has been regarded as a basic element of reinforced concrete members subjected to in-plane biaxial stresses. Despite various experimental studies on shear panel element which have been conducted so far, there are still a lot of uncertainties related to what influencing factors govern the shear behaviour and affect failure mechanism in reinforced concrete members. To identify the uncertainties, a finite element analysis can be used, which enables to investigate the impact of specific variables such as the reinforcement ratio, the shear retention factor, and the material characteristics including aggregate interlock, tension stiffening, compressive softening, and shear behaviour at the crack surface. In this study, a non-linear probabilistic analysis was conducted on reinforced concrete panels using a finite element method optimized for reinforced concrete members and advanced sampling techniques so that probabilistic analysis can be performed effectively. Consequently, this study figures out what analysis methodology and input parameters have the most influence on shear behaviour of reinforced concrete panels.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) funded by the Korean Government (MSIT) (No. 2021R1C1C2093437). Authors Strauss and Ramstorfer also thank IABSE COM1 and the Czech Science Foundation, Czech Republic, under project number 22-00774S.

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