عنوان مقاله [English]
Using reliable, fast and simple methods for seismic assessment is a prerequisite for studying the real seismic ehaviour of structures. Also it is necessary to determine the credibility of new seismic methods before tilizing hem or valuation of structures. The endurance time method is a dynamic pushover analysis that evaluates structural response when the structure is subjected to special incremental excitation, named the acceleration function. In this research, the endurance time method is evaluated and compared with time history analysis in the seismic assessment of T-resisting frames. To achieve this purpose, roof displacement, interstory drift ratio and base shear are selected as comparison criteria. In this way, 2D frames that have 3, 8 and 12 stories have been defined as T-resisting, which have been studied in previous work. OPENSEES finite element software is used in order to create numerical models of the structures and to analyze them. To consider the extensive range of nonlinear behaviour, different hysteretic nonlinear models, such as elastic perfectly plastic, elastic plastic with strain hardening and stiffness degrading and strength deteriorating models, are used. The accuracy of the endurance time method in determining the responses achieved by the nonlinear time history method for different hysteretic models is evaluated. Effects of large deformations, such as P-$\Delta$ , in the models are also considered. Results show that the endurance time method predicts the comparison criteria resulted from time history analysis qualitatively. The inaccuracy of this method in forecasting the base shear is less than 6\% and, for other criteria, is satisfactory. For extracting the endurance time results, the moving average method is used and compared with the normal average. The structural responses obtained from the moving average method are closer to the results obtained from the time history analysis method in comparison with the normal average method. This method also predicts collapse distribution in stories with acceptable accuracy.