Identification of bridge mode shapes using Short Time Frequency Domain Decomposition of the responses measured in a passing vehicle

Abstract

Publication informationEngineering Structures, Publisher Elsevier Item record/more information http://hdl.handle.net/10197/6931 Publisher's statementThis is the author's version of a work that was accepted for publication in Engineering Structures. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Structures (VOL 81, ISSUE 2014, (2015 , several segments are defined on the bridge and the measurement is performed using two instrumented axles. Here, the FDD method is employed in a multi-stage procedure applied to the bridge segments in sequence. A rescaling process is used to construct the global mode shape vector. The performance of the proposed method is validated using numerical case studies. In other indirect bridge identification methods, the road profile may excite the vehicle, making it difficult to detect the bridge modes. This is addressed using two concepts: applying external excitation to the bridge and subtracting signals in the axles of successive trailers towed by the vehicle. The results obtained from the numerical investigation demonstrate that the proposed method can estimate the bridge mode shapes with acceptable accuracy. Sensitivity of the method to added white noise is also investigated.

Application of Laser Measurement to the Drive-by Inspection of Bridges

“…A coupled Finite Element (FE) model similar to that used by [14] is employed for the numerical analysis. The half-car system shown in Fig.…”

“…In addition, several studies have been carried out to identify the bridge damping using the signal measured on a passing vehicle [10,11]. In most the recent studies, the possibility of estimating the bridge mode shapes from indirect measurement has been investigated [12][13][14]. Consideration of the road surface profile in indirect methods is a critical issue as it can excite vehicle-related frequencies to a much higher amplitude level than bridge-related ones, making it difficult to identify the bridge frequencies.…”

Damage detection using curvatures obtained from vehicle measurements

“…In this model, VBI is represented as a coupled system which is a well-accepted approach in the literature [32,34,49]. A Half-car model with 4 degrees of freedom (DOFs) is implemented in MATLAB to simulate the interaction between the vehicle and the bridge.…”

“…A Half-car model with 4 degrees of freedom (DOFs) is implemented in MATLAB to simulate the interaction between the vehicle and the bridge. The Half-car model is demonstrated to be a suitable model representing a vehicle [50] and has been used widely in the literature [34,51]. These DOFs are related to body bounce translation ( ), body pitch rotation ( ) and the two axle vertical translations ( 1 and 2 ).…”

“…Five damage identification parameters can be highlighted: (i) frequency [28][29][30], (ii) damping [31][32][33], (iii) mode shapes [30,34,35], (iv) accelerations [36][37][38] and (v) curvatures. Curvature methods are usually focused on the mode shape and the deflection [39].…”