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Technology that measures bridge responses when a vehicle is crossing over it for structural health monitoring has been under development for approximately a decade. Most of the proposed methods are based on identification of the dynamic characteristics of a bridge such as the natural frequency, the mode shapes, and the damping. Specifically, many time–frequency domain approaches have been used to extract complex spectrum signatures from the complicated vibrations of a bridge due to the interactions of a vehicle with the bridge, which usually involves nonlinear, nonstationary, stochastic, and impact vibrations. In this paper, a method known as complete ensemble empirical mode decomposition with adaptive noise is applied for the first time to analyze the acceleration response of a bridge to a moving vehicle, and the purpose is to extract the spectrum signature of the vehicle–bridge response for structural health monitoring. The time–frequency Hilbert-Huang transform (HHT) spectrum of the decomposed mode from complete ensemble empirical mode decomposition with adaptive noise is presented. The results are well-correlated with finite element analysis. The advantages of the complete ensemble empirical mode decomposition with adaptive noise method are demonstrated in comparing the data from conventional methods, including power spectra, spectrograms, scalograms, and empirical mode decomposition.

The Chulitna River Bridge is a 790-ft five girder, five-span steel bridge on the Parks Highway between Fairbanks and Anchorage, Alaska. This bridge was built in 1970 and widened in 1993. Under the no-live load condition, five support bearings are not in contact. Heavily loaded trucks often travel across this bridge to the oil fields in Prudhoe Bay, Alaska. A virtual finite element modeling, dynamic field testing of the “ambient vibrational response,” and structural health monitoring system are used to analyze, evaluate, and monitor the structural performance. As the first stage of the research, this article presents results from the dynamic testing and evaluation of the structural responses of the bridge. In the dynamic field testing, 15 portable accelerometers were placed on centerline along the bridge length to record the structural response, and an ambient free-decay response was used to evaluate the dynamic properties of the bridge structure. Natural frequencies and modal damping ratios were identified and characterized using Hilbert–Huang transform and fast Fourier transform methods. Compared with conventional approaches, this study demonstrates that (1) the Hilbert–Huang method was found to be effective and suitable for modal parameter identification of a long steel girder bridge using ambient truck loading; (2) the nonlinear damping was, for the first time, identified based on Hilbert–Huang transform’s amplitude–time slope; (3) modal frequencies are very sensitive to sensor location so their position should be optimized.

Strain gage sensors have been used to evaluate the local behavior of structures; however, there are limited studies for its application in bridge dynamic feature identification. In this study, fiber Bragg grating strain gages were installed on the lower chord members of a bridge, and dynamic features were identified successfully using strain gage readings when vehicles passed over the bridge. The results were also verified using a finite element model. The innovation presented in this article is the use of fiber Bragg grating strain gage readings to identify the dynamic features of a long-span, steel-girder bridge. To clarify the effect of truck dynamic load, the load spectrum of the truck is characterized. This article introduces a new method for identifying the dynamic parameters of bridges.