NON-LINEAR HYSTERETIC STRUCTURAL IDENTIFICATION BY UTILIZING ON-LINE SUPPORT VECTOR REGRESSION

Abstract

Structures exhibit highly nonlinear characters under severe loads such as strong seismic excitations. Therefore, it is crucial to make nonlinear structural identification in civil engineering. However, nonlinear hysteretic structural identification is still a challenging topic due to structural model complexity and the strong noises existing in input and output (I/O) data. An efficient approach based on the incremental support vector regression (SVR) is proposed here to identify nonlinear hysteretic structural parameters on-line. Instead of the Gaussian loss function utilized in the least squares method, a novel einsensitive loss function is employed in SVR, and therefore the suggested SVR-based approach produces robust and accurate identification results. Furthermore, as an incremental algorithm employed to train SVR in a sequential way, the presented SVR-based approach not only works rapidly, but also identifys nonlinear structural constitutive parameters on-line. The performance of the proposed approach is verified by a five degree of freedom nonlinear hysteretic structural identification problem, in which two cases (power parameter is known/unknown) are both investigated. The identified results show evidently that the proposed technique has potential performance in robustness and accuracy for nonlinear structural identification, even when the measurement data in the presence of noises.