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Numerical models of RC elements and their impacts on seismic performance assessment
Authors:Xu Huang  Oh‐Sung Kwon
Institution:Department of Civil Engineering, University of Toronto, Toronto, ON, Canada
Abstract:This paper aims to provide a guideline for numerical modeling of reinforced concrete (RC) frame elements for the seismic performance assessment of a structure. Several types of numerical models of RC frame elements are available in nonlinear structural analysis packages. Because the numerical models are formulated based on different assumptions and theories, the models' accuracy, computing time, and applicability vary, which poses a great difficulty to practicing engineers and limits their confidence in the analysis results. In this study, the applicability of five representative numerical models of RC frame elements is evaluated through comparison with 320 experimental results available from the Pacific Earthquake Engineering Research column database. The accuracy of a numerical model is evaluated according to its initial stiffness, peak strength, and energy dissipation capacity of the global responses. In addition, a parametric study of a cantilever RC column subjected to earthquake excitation is carried out to systematically evaluate the consequence of the adopted numerical models on the maximum inelastic structural responses. It is found from this study that the accuracy of the numerical models is sensitive to shear force demand–capacity ratio. If a structural period is short and the structure is shear critical, the use of numerical models that can explicitly capture the shear deformation and failure is suggested. If the structural period is long, the selection of a numerical model does not greatly influence the global response of the structure. The paper also presents statistical parameters of each numerical model, which can be used for probabilistic seismic performance assessment. Copyright © 2014 John Wiley & Sons, Ltd.
Keywords:seismic performance assessment  nonlinear numerical model  RC element  model validation
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