Summary
This paper investigates the effects of uncertainties and modeling errors on the performance of nonlinear updating process through an experimental study on two 1/8‐scale four‐story steel moment‐resisting frames. To examine the accuracyo of the models calibrated using different data features and modeling assumptions, the actual structural responses obtained from shaking table tests and the response of the models have been compared. The modeling uncertainties are investigated by applying frames with different modeling assumptions, including concentrated and distributed plasticity, and different hysteretic material behaviors. To calibrate the models, four data features are considered and combined: (1) acceleration time history, (2) displacement time history, (3) instantaneous characteristics of the decomposed monocomponent of the response, and (4) dissipated energy. In this paper, a sensitivity analysis has been performed to investigate the effect of various parameters of hysteretic materials on the response of reference steel moment‐resisting frames. To this end, the optimum values of the unknown parameters are determined through minimizing the objective function defined based on the residuals of data features for the actual structure and finite element (FE) model. By comparing the structural responses of the model with concentrated plasticity calibrated by instantaneous characteristics with those of the other models, it can be concluded that the concentrated plasticity model predicts a more accurate response compared to the other models. Consequently, the aleatory uncertainty was taken into account by evaluating the performance of the system with concentrated plasticity under 40 seismic ground motions, and the structural response has been obtained through the calibrated model.
Structural system identification using recursive methods has been a research direction of increasing interest in recent decades. The two prominent methods, including the Extended Kalman Filter (EKF) and the Particle Filter (PF), also known as the Sequential Monte Carlo (SMC), are advantageous in this field. In this study, the system identification of a shake table test of a 4-story steel structure subjected to the base excitation has been implemented using these methods by considering the modeling and material model uncertainties. Implementing the 2D and 3D modelings, using the "parallelogram" and "scissors" methods for the modeling of panel zones and that of the wall panels by two methods (using beam-column elements and equivalent diagonal strut elements), are the assumptions of this study. Using the parallelogram method has resulted in fewer errors in the 2D modeling while implementing different methods for simulation of wall panels has had no specific achievements. As illustrated in the results, more significant uncertainties were expected in systems with highly nonlinear behavior, since the equivalent linearization was used to estimate the system states in the EKF method. However, this method is less time-consuming and gives more accurate results in comparison with the PF method, in which a lrge number of samples are required for the system identification.
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