In bridge health monitoring, tiltmeters have been used for measuring rotation and curvature; however, their application in dynamic parameter identification has been lacking. This study installed fiber Bragg grating (FBG) tiltmeters on the bearings of a bridge and monitored the dynamic rotational angle. The dynamic features, including natural frequencies and mode shapes, have been identified successfully. The innovation presented in this paper is the first-time use of FBG tiltmeter readings to identify the natural frequencies of a long-span steel girder bridge. The identified results have been verified using a bridge finite element model. This paper introduces a new method for the dynamic monitoring of a bridge using FBG tiltmeters. Limitations and future research directions are also discussed in the conclusion.

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.

This paper presents a parameter-identification method for rod structures with different connections. In this method, the parameters of the structure are adjusted to match its analytical and measured displacements. The damage identification for truss structure and rigid frame were investigated. Previous studies often considered the cross-sectional area damage or joint damage; there are few studies on the simultaneous existence of these two types of damage. In this study, damage identification for a rigid frame with both cross-sectional and joint damage was performed. Based on the measured displacements, the proposed method can accurately identify the cross-sectional and joint damage for a rigid frame.

Quantifying the non-stationary properties of bridge under passing vehicle has been an important topic in structural health monitoring of bridge. There are many methods of time–frequency representation used for the study of dynamics of bridge under passing vehicle, including spectrogram, wavelet, Hilbert–Huang transform, and so on. This article uses adaptive optimal kernel time–frequency representation to quantify the non-stationary properties of the response of bridge under passing vehicle and illustrates and discusses its advantages over conventional time–frequency methods.