Ocampo Escobar, Andrés F.

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    Evaluation of the lateral stiffness for the seismic analysis and design of squat RC walls
    (2019-05-15) Ocampo Escobar, AndrĂ©s F.; Vidot Vega, Aidcer L.; College of Engineering; Montejo Valencia, Luis A; Lopez Rodriguez, Ricardo R.; SuĂ¡rez, Luis E.; Department of Civil Engineering; Calcagno, Barbara
    Reinforced concrete (RC) squat walls provide most of the structure lateral strength and stiffness to resist earthquakes and wind loadings in nuclear power plants and other buildings. A reduction factor is usually applied to the stiffness to compensate for the effect of cracking in linear analysis. An appropriate definition of lateral stiffness is important to properly capture cracking effects and to accurately design and evaluate the behavior of RC shear walls. The basis for the effective stiffness values or expressions and their applicability to nuclear power plant elements is not clearly presented in current seismic standards. This research studies the effective stiffness of reinforced concrete (RC) squat walls under lateral loads. RC squat walls have height to length ratios less than or equal to 2. Prediction of the seismic response and proper capturing of the effective stiffness of squat walls is a challenging task since these walls exhibit a shear dominated behavior with strong coupling between shear and flexure responses. Finite element models of several RC squat walls are developed using the commercial software Abaqus and OpenSees. The main objective of these models is to predict the lateral stiffness of RC squat walls appropriately and to identify the parameters that have main influence in the lateral stiffness of these walls. The results from analytical modeling are compared with the results of experimental tests available in the literature. Available expressions and reduction factors in current seismic standards and in the literature for the calculation of effective stiffness for RC squat walls are also evaluated. Improved predictive equations for lateral stiffness are developed by calibration against the available data. Multiple-linear regression analysis is used to develop the predictive equations. Key parameters influencing the effective stiffness are identified during the nonlinear analyses and from existing experimental data.