Experimental Modal Analysis of Jacket-Type Platforms Using Data-Driven Stochastic Subspace Identification Method

Abstract

Employing efficient techniques to accurately identify the modal parameters of new and aging offshore structures has been of interest to the offshore industry for decades. Early methods of modal identification were developed for the frequency domain. The new trend is to employ either input-output or output-only time-domain modal identification methods. Under the assumption that the excitation input is a zero-mean Gaussian white noise process, a modern output-only method that allows direct application to the response time series is the data-driven stochastic subspace identification (SSI-data) method. The main objective of this paper is to evaluate the performance of the SSI-data method using the test data measured from a physical model of a realistic offshore jacket-type platform. Response acceleration data associated with three different excitation mechanisms are investigated: impact loading, step relaxation and white noise ground motion. Although the SSI-data method has been theoretically developed, and often perceived to be only valid, for the ambient noise testing environment, it is shown in this study that the SSI-data method also performs well using data from either the impact loading or step relaxation tests.