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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. This paper presents a novel methodology for modal parameter estimation in OMA that overcomes the problems presented by driving frequencies and significantly improves the damping estimates. This methodology is based on finding the power spectral density (PSD) of a given modal coordinate, and then dividing the modal PSD 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 that uses optimization to find the modal parameters that best fit each side spectra of the PSD. 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 PSDs distorted or altered by driving frequencies. [ABSTRACT FROM AUTHOR]

Accurate estimation of the damping in a structure has remained an important but challenging problem for the structural engineering community. The relative difficulty of damping estimation can be compounded when the excitation is not uniformly broadband or ambient in nature, such as when car traffic or large trains travel over a bridge. A bridge model that consisted of a series of simply supported (SS) stringers resting atop a larger girder was constructed using finite elements, and several simulations were conducted in which cars and trains crossed the bridge model. The presence of the cars and trains led to the appearance of driving forcing frequencies in the response. Driving frequencies are inherent to moving loads and proportional to the velocity of the moving loads and the length of the beam or bridge being crossed. The moving loads produced a pulse-like response in the SS stringers on the bridge model, and the resulting power spectral density (PSD) of the stringers showed that its power was concentrated at the first driving frequency and its even multiples. The vertical component of the moving load and the SS stringer responses were transferred to the girder, and owing to the continuity within the girder, each of its nodes experienced all of the car crossings. The repeated nature of the car crossings led to a PSD of the girder responses that contained peaks at the even multiples of the driving frequency. Over the course of a full simulation, the peaks produced by the different car velocities merged to form shelves of elevated power; these shelves were repeated at each of the even multiples of the driving frequency, causing sustained regions of distortion in the frequency response spectra of the girder. Attempts were made to identify the modal damping ratios from the bridge acceleration responses using the frequency domain decomposition (FDD) and blind source separation (BSS) methods, but the driving frequencies interfered with the estimates. The regions of distortion compromised the spectra for the frequency-based methods and altered the estimated modal responses recovered from the BSS method, creating problems with identification in the time domain. The driving frequencies generated by the car and train traffic on a bridge negatively impacted both the reliability and accuracy of the damping estimates found using various operational modal analysis (OMA) techniques. [ABSTRACT FROM AUTHOR]