Identification of vehicle axle loads from bridge responses using preconditioned least square QR-factorization algorithm
Article
Article Title | Identification of vehicle axle loads from bridge responses using preconditioned least square QR-factorization algorithm |
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ERA Journal ID | 3705 |
Article Category | Article |
Authors | Chen, Zhen (Author), Chan, Tommy H.T. (Author), Nguyen, Andy (Author) and Yu, Ling (Author) |
Journal Title | Mechanical Systems and Signal Processing |
Journal Citation | 128, pp. 479-496 |
Number of Pages | 18 |
Year | 2019 |
Place of Publication | United Kingdom |
ISSN | 0888-3270 |
1096-1216 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ymssp.2019.03.043 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0888327019302183 |
Abstract | This paper develops a novel method for moving force identification (MFI) called preconditioned least square QR-factorization (PLSQR) method. The algorithm seeks to reduce the impact of identification errors caused by unknown noise. The biaxial moving forces travel on a simply supported bridge at three different speeds is used to generate numerical simulations to assess the effectiveness and applicability of the algorithm. Results indicate that the method is more robust towards ill-posed problem and has higher identification precision than the conventional time domain method (TDM). In addition, the robustness and ill-posed immunity of PLSQR are directly affected by two kinds of regularization parameters, namely, number of iterations j and regularization matrix L. Compared with the standard form of least square QR-factorization (LSQR), i.e., the regularization matrix L being the identity matrix I_n, the PLSQR with the optimal number of iterations j and regularization matrix L has many advantages on MFI and it is more suitable for field trials due to better adaptability with type of sensors and number of sensors. |
Keywords | moving force identification; preconditioned least square QR-factorization; time domain method; regularization parameter; preconditioner |
ANZSRC Field of Research 2020 | 400510. Structural engineering |
401702. Dynamics, vibration and vibration control | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Queensland University of Technology |
Jinan University, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q535x/identification-of-vehicle-axle-loads-from-bridge-responses-using-preconditioned-least-square-qr-factorization-algorithm
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