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Improving damping estimation through enhanced frequency response curve-fitting

While the field of structural system identification continues to experience many significant advances, one of the persistent challenges is the proper quantification of damping as damping contributes to structural safety and longevity. Even under ideal, ambient conditions it may be difficult to achieve moderately accurate damping estimates. Therefore, the complexity presented by real-world, non-ambient scenarios dramatically increases the challenge of accurate damping estimation. The authors have previously shown that many traditional approaches to operational modal analysis (OMA) struggle to properly identify the modal damping ratios for bridges under traffic loading due to the interference caused by the driving frequencies of the traffic loads.

A novel methodology for modal parameter estimation in OMA that overcomes the problems presented by driving frequencies and significantly improves the damping estimates is presented. This methodology is based on finding the frequency response function (FRF) of a given modal coordinate, and then dividing the modal peak into separate regions, left- and right-side spectra. The modal coordinates were found using a blind source separation (BSS) algorithm and a curve-fitting technique was developed using optimization to find the modal parameters that best fit each side spectra of the FRF. Specifically, a pattern-search optimization method was combined with a clustering analysis algorithm and together they were employed in a series of stages in order to improve the estimates of the modal damping ratios. This method was used to estimate the damping ratios from a simulated bridge model subjected to moving traffic loads. The results of this method were compared to other established OMA methods, such as Frequency Domain Decomposition (FDD) and BSS methods, and they were found to be more accurate and more reliable, even for modes that had their FRFs distorted or altered by driving frequencies.

Author(s):

Patrick Brewick    
University of Southern California
United States

Andrew Smyth    
Columbia University
United States