International Journal of Lifecycle Performance Engineering
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International Journal of Lifecycle Performance Engineering (4 papers in press)
Uncertainty analysis of the subway vehicle-track coupling system with fuzzy variables by Hongping Zhu, Ling Ye, Shun Weng Abstract: Dynamic analysis of the vehicle-track coupling system is important to the structural design, damage detection and condition assessment of the structures. The structural parameters of the vehicle-track coupling system include many uncertainties intrinsically, it is rational to treat the parameters in an uncertainty way. In this paper, the uncertain system parameters are modeled as fuzzy variables instead of deterministic values or conventional random variables with known probability distributions. Afterwards, the dynamic response functions of the coupling system are transformed into a component function based on the high dimensional representation approximation. Then the Lagrange interpolation method is used to approximate the component function. Finally, the bounds of the system dynamic responses can be predicted by performing Monte Carlo method on the interpolation polynomials of Lagrange interpolation function, avoiding the expensive Monte Carlo process on the subway vehicle-track coupling system. A numerical example is analyzed by the proposed method to predict the bounds of the subway vehicle-track coupling system responses. The results are compared with the direct Monte Carlo simulations, which show that the proposed method is effective and efficient to predict the bounds of the system responses with fuzzy system parameters. Keywords: subway vehicle-track coupling system; fuzzy variables; high dimensional representation approach; Monte Carlo simulation.
Special Issue on: Innovative Techniques in Bridge Condition Assessment and Identification Subjected to Moving Loads
Two-Step Method for Bridge Modal Mass Identification Using Synchronously Measured Bridge and Vehicle Dynamic Responses
by Tomonori Nagayama Abstract: For the identification of bridge parameters such as modal mass, both the excitation on a bridge and its corresponding bridge responses must be measured simultaneously. However, traditional methods of exciting a bridge by a hammer or shaker present many drawbacks including limited impact energy, narrow frequency range, and other site-specific difficulties. Meanwhile, vehicle-induced load can be used for bridge parameter identification if vehicle-bridge contact force is well estimated. Herein, a two-step algorithm is proposed to identify bridge modal mass. The first step is to estimate the vehicle tire forces from measured vehicle responses. In the second step, the estimated tire forces and the simultaneously measured vehicle-induced bridge acceleration responses are used as the input and output of the bridge system, respectively, to identify the bridge modal mass. Numerical examples clarify the modal mass identification performance considering observation noises and differences in the left and right path profiles. Field measurement was conducted, and the results indicate that the proposed algorithm can identify the first modal mass with satisfactory accuracy. Keywords: Bridge assessment; drive-by monitoring; tire force estimation; bridge modal mass; augmented Kalman filter.
Estimation of Bridge Surface Profile from Moving Vehicle Accelerations by means of Moving Force Identification An Experimental Field Study by Kai-Chun Chang, Chul-Woo Kim, Souichiro Hasegawa, Syunkuke Nakajima, Patrick McGetrick Abstract: Accurate estimation of road surface profiles is an important issue for inspection and maintenance of roadway bridges. Recently, a simple method was proposed to complete this task, using a limited number of accelerometers installed on a non-specialized vehicle to estimate road surface profiles from the vehicles acceleration responses. Its feasibility was preliminarily tested by several numerical simulations and laboratory experiments but validation via field experiments has yet to be completed. This paper presents the investigation of the feasibility of this method for implementation in practice; to this end a field experiment was conducted on a 40.5 m long simply-supported composite-girder bridge. A compact SUV was employed as the test vehicle. In the field experiment, the road surface profile was estimated with acceptable accuracy using this method, with most peaks and troughs identified. In particular, an artificial hump installed on the bridge was clearly identified, implying that the investigated method was able to detect an abrupt change in the road surface profile, which could be caused by damage. Parametric studies were carried out showing that the vehicle speed had little effect on the estimation accuracy while a heavier vehicle presented better estimation accuracy. Keywords: Drive-by bridge inspection; Inverse problem; Field experiment; Moving force identification; Road surface profile;.
Research on moving load identification based on measured acceleration and strain signals by Yun Zhou, Sai Zhou, Lu Deng, Songbai Chen, Weijian Yi Abstract: Moving load identification from the dynamic responses of bridges is a typical inverse problem that is solved to estimate vehicle axle loads in motion from observed data. To reduce the ill-posedness of the problem and improve solution accuracy, this paper proposes a method for reconstructing the dynamic displacement response via combining the measured acceleration and strain signals for moving load identification. The identification accuracy achieved by employing the reconstructed displacement under different vehicle speeds and different identification algorithms was investigated via finite element (FE) analysis, and a laboratory experiment of a simply supported beam model was constructed to validate the effectiveness of the proposed method. Both the computation simulations and experimental results indicate that the reconstructed displacements fit the true values well and the proposed method can effectively overcome the ill-posedness of the problem in terms of equation resolution and achieve a high level of accuracy. Keywords: Moving load identification; Acceleration; Dynamic strain; Signal reconstruction; Modal decomposition.