Nonlinear response history analysis is accepted as the most accurate analytical tool for seismic response determination. However, accurate estimation of displacement responses using conceptually simple, approximate analysis procedures is preferable, since there are shortcomings in the application of nonlinear response history analysis resulting from its complexity. An equivalent linearization procedure, which utilizes the familiar response spectrum analysis as the analysis tool and benefits from the capacity principles, is developed in this thesis study as an approximate method for predicting the inelastic seismic displacement response of MDOF systems under earthquake excitations. The procedure mainly consists of the construction of an equivalent linear system by reducing the stiffness of structural members which are expected to respond in the inelastic range. Different from similar studies in literature, equivalent damping is not explicitly employed in this study. Instead, predetermined spectral displacement demands are utilized in each mode of the equivalent linear system for the determination of global displacement demands. Response predictions of the equivalent linearization procedure are comparatively evaluated by using the benchmark nonlinear response history analysis results and other approximate methods including conventional pushover analysis and modal pushover analysis (MPA). It is observed that the proposed procedure results in similar accuracy with approximate methods which employ nonlinear analysis. Considering the conceptual simplicity of the procedure and the conventional analysis tools used in its application, presented equivalent linearization procedure can be suggested as a practically applicable method for the prediction of inelastic seismic displacement response parameters with sufficient accuracy.

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A novel fuzzy system named as 2-way fuzzy system is developed by combining the intuitionistic fuzzy set theory with the fuzzy systems theory. The developed system is used in modelling and minimizing uncertainty and inconsistency. Uncertainty is the width of the interval introduced by the independent assignment of membership and nonmembership functions of the intuitionistic fuzzy sets / and inconsistency is the violation of the consistency inequality in this assignment. The uncertainty and inconsistency is reduced through a 2 phase training. An evaluation of the degree of reduction of inconsistency is carried out at the end of the rst phase of training by forming the shadowed set patterns of the membership and nonmembership functions. The system is further trained for a second phase in order to reduce uncertainty. There are three dierent methods developed for the stability analysis of fuzzy systems. The rst method is based on the approximating sequences technique, and the design turns into an optimal control problem. In the sec ond analysis, describing function of a 2-way fuzzy system is evaluated analytically, and a systematic design approach is developed using describing function technique. The last analysis technique employs the Lie algebra theory in the stability analysis of Takagi-Sugeno fuzzy systems. The theoretical results are simulated on an application system, which is a exible-joint robot arm system.

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