Application of Nonlinear Static Method with Corrective Eccentricities to Multistorey Buildings: A Case Study

Abstract

AbstractNonlinear static methods are not always effective in the assessment of three-dimensional building structures because their estimate of the torsional response is not always reliable. To overcome this shortcoming, some of the authors have proposed a double application of the nonlinear static analysis by means of lateral forces applied to points of the deck characterised by different eccentricities with respect to the centre of mass. These eccentricities, named “corrective eccentricities”, were calibrated in such a way that the displacements of the edges of the deck evaluated by the two nonlinear static analyses equal those evaluated by nonlinear dynamic analysis. Analytical expressions of the corrective eccentricities were derived and presented in a previous study based on a parametrical investigation conducted on a wide set of one-storey systems subjected to bidirectional ground motions. In this chapter, the peculiarities of the application of the proposed nonlinear static method to multistorey buildings are discussed first. Second, as an example, the method is applied to predict the seismic response of a five-storey asymmetric building. The structure of the analysed building is constituted by reinforced concrete frames arranged along two orthogonal directions. The cross sections of the structural element are equal at all storeys. To test the effectiveness of the proposed method, the in-plan distributions of the deck displacement and drift are evaluated by nonlinear dynamic analysis and compared to those resulting from the nonlinear static method with corrective eccentricities. The obtained results show that the use of the corrective eccentricities leads to a suitable estimate of the maximum dynamic displacements and drifts.KeywordsSeismic ResponsePushover AnalysisNonlinear Dynamic AnalysisStorey DriftNonlinear Static AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.